diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml
index f43437d19c0..fed1792d2f9 100644
--- a/.github/copy-pr-bot.yaml
+++ b/.github/copy-pr-bot.yaml
@@ -1,4 +1,4 @@
 enabled: true
 auto_sync_draft: false
 auto_sync_ready: true
-trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "ChenhanYu", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Phlip79", "QiZhangNV", "ShriyaRishab", "Victarry", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cspades", "cuichenx", "deepakn94", "dimapihtar", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "frsun-nvda", "gautham-kollu", "gdengk", "guyueh1", "hxbai", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kanz-nv", "kevalmorabia97", "ko3n1g", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mehraakash", "mkhona-nvidia", "parthmannan", "prajwal1210", "pthombre", "rogerwaleffe", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "trintamaki", "tylerpoon", "wdykas", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yuzhongw-nvidia", "zhongbozhu"]
+trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "frsun-nvda", "gautham-kollu", "gdengk", "guyueh1", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kanz-nv", "kevalmorabia97", "ko3n1g", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "mkhona-nvidia", "parthmannan", "prajwal1210", "pthombre", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "trintamaki", "tylerpoon", "wdykas", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"]
diff --git a/.github/oncall_schedule.json b/.github/oncall_schedule.json
index 5fa49e966bc..58fcf0ddbbc 100644
--- a/.github/oncall_schedule.json
+++ b/.github/oncall_schedule.json
@@ -1,18 +1,6 @@
 [
-    {
-        "user": "dimapihtar",
-        "date": "2026-01-28"
-    },
-    {
-        "user": "gautham-kollu",
-        "date": "2026-02-04"
-    },
     {
         "user": "janEbert",
-        "date": "2026-02-11"
-    },
-    {
-        "user": "Phlip79",
         "date": "2026-02-18"
     },
     {
@@ -46,5 +34,17 @@
     {
         "user": "BoxiangW",
         "date": "2026-04-15"
+    },
+    {
+        "user": "Phlip79",
+        "date": "2026-04-22"
+    },
+    {
+        "user": "asolergi-nv",
+        "date": "2026-04-29"
+    },
+    {
+        "user": "dimapihtar",
+        "date": "2026-05-06"
     }
 ]
diff --git a/.github/scripts/readme.sh b/.github/scripts/readme.sh
new file mode 100644
index 00000000000..216d5224a28
--- /dev/null
+++ b/.github/scripts/readme.sh
@@ -0,0 +1,65 @@
+#!/bin/bash
+
+cat << 'EOF'
+╔══════════════════════════════════════════════════════════════════════╗
+║                                                                      ║
+║    ███╗   ███╗██████╗ ██████╗ ██╗██████╗  ██████╗ ███████╗         ║
+║    ████╗ ████║██╔══██╗██╔══██╗██║██╔══██╗██╔════╝ ██╔════╝         ║
+║    ██╔████╔██║██████╔╝██████╔╝██║██║  ██║██║  ███╗█████╗           ║
+║    ██║╚██╔╝██║██╔══██╗██╔══██╗██║██║  ██║██║   ██║██╔══╝           ║
+║    ██║ ╚═╝ ██║██████╔╝██║  ██║██║██████╔╝╚██████╔╝███████╗         ║
+║    ╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝╚═╝╚═════╝  ╚═════╝ ╚══════╝         ║
+║                                                                      ║
+║              H O W   T O :   M B R I D G E   T E S T I N G         ║
+╚══════════════════════════════════════════════════════════════════════╝
+
+  MBridge unit tests run automatically on every PR. To also trigger
+  functional tests, attach the label and re-run the workflow step.
+
+  ┌─────────────────────────────────────────────────────────────────┐
+  │  DEFAULT  │  Unit tests run on every PR (no action needed)      │
+  ├─────────────────────────────────────────────────────────────────┤
+  │                                                                  │
+  │    Every PR  ──►  cicd-mbridge-testing  ──►  unit tests only   │
+  │                                                                  │
+  └─────────────────────────────────────────────────────────────────┘
+
+  ┌─────────────────────────────────────────────────────────────────┐
+  │  STEP 1  │  Attach the label to your PR (for functional tests)  │
+  ├─────────────────────────────────────────────────────────────────┤
+  │                                                                  │
+  │    PR Labels  ──►  [ + Add label ]  ──►  "Run MBridge tests"   │
+  │                                                                  │
+  └─────────────────────────────────────────────────────────────────┘
+
+  ┌─────────────────────────────────────────────────────────────────┐
+  │  STEP 2  │  Re-run this workflow step                           │
+  ├─────────────────────────────────────────────────────────────────┤
+  │                                                                  │
+  │    Actions  ──►  [ Re-run jobs ]  ──►  Re-run failed jobs      │
+  │                                                                  │
+  └─────────────────────────────────────────────────────────────────┘
+
+  ┌─────────────────────────────────────────────────────────────────┐
+  │  RESULT  │  Unit + functional tests run!                        │
+  ├─────────────────────────────────────────────────────────────────┤
+  │                                                                  │
+  │         cicd-mbridge-testing  ◄── unit + functional tests      │
+  │                                                                  │
+  │         Tests run against MBridge using the merge commit       │
+  │         SHA of your pull request.                              │
+  │                                                                  │
+  └─────────────────────────────────────────────────────────────────┘
+
+                ┌────────────────────────────────────┐
+                │  Label present?     NO   → unit    │
+                │  Label present?     YES  → unit +  │
+                │                           functional│
+                └────────────────────────────────────┘
+
+  NOTE: The label must be present BEFORE the re-run is triggered.
+        The CI checks for "Run MBridge tests" at runtime.
+
+  NOTE: All MBridge test results are optional — failures do not
+        block merging your PR.
+EOF
diff --git a/.github/scripts/sync_team_usergroups.py b/.github/scripts/sync_team_usergroups.py
index 429387fc6de..c3fa5d474ff 100644
--- a/.github/scripts/sync_team_usergroups.py
+++ b/.github/scripts/sync_team_usergroups.py
@@ -29,7 +29,12 @@
 
 # Constants
 GITHUB_API_URL = "https://api.github.com"
-PARENT_TEAM_SLUG = "mcore-reviewers"
+
+# Teams whose *children* are each synced to their own Slack usergroup
+PARENT_TEAM_SLUGS = ["mcore-reviewers"]
+
+# Teams synced directly (the team itself, not its children)
+DIRECT_TEAM_SLUGS = ["mcore-engineers"]
 
 # Caches for email and Slack lookups
 _email_cache = {}
@@ -83,6 +88,8 @@ def github_team_to_slack_usergroup(team_slug):
         name = name[5:]  # Remove "core-"
     elif name.startswith("megatron-"):
         name = name[9:]  # Remove "megatron-"
+    elif name.startswith("mcore-"):
+        name = name[6:]  # Remove "mcore-"
 
     # Remove "-and-"
     name = name.replace("-and-", "-")
@@ -437,13 +444,13 @@ def sync_team_to_usergroup(team_slug, usergroup_handle, dry_run=False):
         return False
 
 
-def get_team_to_usergroup_mapping():
-    """Fetch child teams of mcore-reviewers and generate the mapping."""
+def get_team_to_usergroup_mapping(parent_team_slug):
+    """Fetch child teams of a parent team and generate the mapping."""
     org = get_org()
-    child_teams = get_child_teams(org, PARENT_TEAM_SLUG)
+    child_teams = get_child_teams(org, parent_team_slug)
 
     if not child_teams:
-        print(f"Error: No child teams found under '{PARENT_TEAM_SLUG}'")
+        print(f"Error: No child teams found under '{parent_team_slug}'")
         return {}
 
     mapping = {}
@@ -454,10 +461,30 @@ def get_team_to_usergroup_mapping():
     return mapping
 
 
-def sync_all_teams(dry_run=False):
-    """Sync all GitHub teams under mcore-reviewers to their Slack usergroups."""
-    print(f"Fetching child teams of '{PARENT_TEAM_SLUG}'...")
-    team_to_usergroup = get_team_to_usergroup_mapping()
+def sync_all_teams(dry_run=False, parent_teams=None, direct_teams=None):
+    """Sync GitHub teams to their Slack usergroups.
+
+    Args:
+        parent_teams: List of team slugs whose *children* are each synced.
+                      Defaults to PARENT_TEAM_SLUGS.
+        direct_teams: List of team slugs synced directly (not their children).
+                      Defaults to DIRECT_TEAM_SLUGS.
+    """
+    if parent_teams is None:
+        parent_teams = PARENT_TEAM_SLUGS
+    if direct_teams is None:
+        direct_teams = DIRECT_TEAM_SLUGS
+
+    team_to_usergroup = {}
+
+    for parent_slug in parent_teams:
+        print(f"Fetching child teams of '{parent_slug}'...")
+        mapping = get_team_to_usergroup_mapping(parent_slug)
+        team_to_usergroup.update(mapping)
+
+    for team_slug in direct_teams:
+        usergroup_handle = github_team_to_slack_usergroup(team_slug)
+        team_to_usergroup[team_slug] = usergroup_handle
 
     if not team_to_usergroup:
         return False
@@ -504,12 +531,40 @@ def main():
         action="store_true",
         help="List all configured team-to-usergroup mappings",
     )
+    parser.add_argument(
+        "--parent-team",
+        action="append",
+        dest="parent_teams",
+        metavar="SLUG",
+        help=(
+            "Sync all children of this GitHub team (can be repeated). "
+            f"Defaults to: {PARENT_TEAM_SLUGS}"
+        ),
+    )
+    parser.add_argument(
+        "--team",
+        action="append",
+        dest="direct_teams",
+        metavar="SLUG",
+        help=(
+            "Sync this GitHub team directly (can be repeated). "
+            f"Defaults to: {DIRECT_TEAM_SLUGS}"
+        ),
+    )
 
     args = parser.parse_args()
 
+    # Use CLI values when provided, otherwise fall back to module-level defaults
+    parent_teams = args.parent_teams if args.parent_teams is not None else PARENT_TEAM_SLUGS
+    direct_teams = args.direct_teams if args.direct_teams is not None else DIRECT_TEAM_SLUGS
+
     if args.list:
-        print(f"Fetching child teams of '{PARENT_TEAM_SLUG}'...")
-        team_to_usergroup = get_team_to_usergroup_mapping()
+        team_to_usergroup = {}
+        for parent_slug in parent_teams:
+            print(f"Fetching child teams of '{parent_slug}'...")
+            team_to_usergroup.update(get_team_to_usergroup_mapping(parent_slug))
+        for team_slug in direct_teams:
+            team_to_usergroup[team_slug] = github_team_to_slack_usergroup(team_slug)
         if not team_to_usergroup:
             sys.exit(1)
         print("\nTeam-to-usergroup mappings:")
@@ -519,7 +574,9 @@ def main():
             print(f"{team:<35} @{usergroup:<29}")
         return
 
-    success = sync_all_teams(dry_run=args.dry_run)
+    success = sync_all_teams(
+        dry_run=args.dry_run, parent_teams=parent_teams, direct_teams=direct_teams
+    )
     sys.exit(0 if success else 1)
 
 
diff --git a/.github/workflows/_build_test_publish_wheel.yml b/.github/workflows/_build_test_publish_wheel.yml
index 9e9062827de..0b71577b587 100644
--- a/.github/workflows/_build_test_publish_wheel.yml
+++ b/.github/workflows/_build_test_publish_wheel.yml
@@ -17,8 +17,6 @@ on:
         type: boolean
         default: true
     secrets:
-      TWINE_USERNAME:
-        required: true
       TWINE_PASSWORD:
         required: true
 
@@ -147,7 +145,6 @@ jobs:
     needs: [build-and-test-wheels]
     runs-on: ubuntu-latest
     if: inputs.no-publish == false
-    environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && 'main' || 'public' }}
     strategy:
       fail-fast: false
       matrix:
@@ -170,7 +167,7 @@ jobs:
 
       - name: Publish wheels
         env:
-          TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
+          TWINE_USERNAME: __token__
           TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
           TWINE_REPOSITORY: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && 'pypi' || 'testpypi' }}
           PLATFORM: ${{ matrix.PLATFORM }}
diff --git a/.github/workflows/_release_library.yml b/.github/workflows/_release_library.yml
index d39ee505c2a..684dacc27aa 100644
--- a/.github/workflows/_release_library.yml
+++ b/.github/workflows/_release_library.yml
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-name: 'Release'
+name: "Release"
 
 defaults:
   run:
@@ -38,13 +38,24 @@ on:
         description: Create a GitHub release
         type: boolean
         default: true
+      gh-release-use-changelog-builder:
+        required: false
+        description: Use release-changelog-builder-action to dynamically build changelog
+        type: boolean
+        default: true
+      gh-release-changelog-config:
+        required: false
+        description: Path to changelog builder configuration file
+        type: string
+        default: ".github/workflows/config/changelog-config.json"
+      gh-release-from-tag:
+        required: false
+        description: Starting tag for changelog builder (leave empty for auto-detect)
+        type: string
+        default: ""
     secrets:
-      TWINE_USERNAME:
-        required: true
       TWINE_PASSWORD:
         required: true
-      SLACK_WEBHOOK_ADMIN:
-        required: true
       SLACK_WEBHOOK:
         required: true
       PAT:
@@ -62,12 +73,10 @@ jobs:
       ref: ${{ inputs.release-ref }}
       no-publish: true
     secrets:
-      TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
       TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
 
   bump-next-version:
     runs-on: ubuntu-latest
-    environment: main # ${{ inputs.dry-run == true && 'public' || 'main' }}
     needs: build-test-publish-wheels-dry-run
     if: |
       (
@@ -90,8 +99,8 @@ jobs:
       - name: Bump version MCore
         id: bump-version-mcore
         env:
-          SRC_DIR: ''
-          PYPROJECT_NAME: 'megatron.core'
+          SRC_DIR: ""
+          PYPROJECT_NAME: "megatron.core"
         run: |
           set +u
           cd ${{ github.run_id }}
@@ -129,8 +138,8 @@ jobs:
       - name: Bump version MFSDP
         id: bump-version-mfsdp
         env:
-          SRC_DIR: 'megatron/core/distributed/fsdp/src/'
-          PYPROJECT_NAME: 'megatron_fsdp'
+          SRC_DIR: "megatron/core/distributed/fsdp/src/"
+          PYPROJECT_NAME: "megatron_fsdp"
         run: |
           set +u
 
@@ -323,7 +332,6 @@ jobs:
   create-gh-release:
     needs: [build-test-publish-wheels, bump-next-version]
     runs-on: ubuntu-latest
-    environment: ${{ inputs.dry-run == true && 'public' || 'main' }}
     if: |
       (
         success() || !failure()
@@ -345,12 +353,51 @@ jobs:
           ref: ${{ inputs.release-ref }}
           token: ${{ secrets.PAT || secrets.GITHUB_TOKEN }}
 
+      - name: Determine fromTag for changelog
+        id: determine-from-tag
+        if: inputs.gh-release-use-changelog-builder == true
+        run: |
+          cd ${{ github.run_id }}
+
+          # If gh-release-from-tag is provided, use it
+          if [[ -n "${{ inputs.gh-release-from-tag }}" ]]; then
+            FROM_TAG="${{ inputs.gh-release-from-tag }}"
+            echo "Using provided fromTag: $FROM_TAG"
+          else
+            # Get the most recent tag
+            FROM_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "")
+            if [[ -z "$FROM_TAG" ]]; then
+              echo "No previous tags found, leaving fromTag empty"
+            else
+              echo "Auto-detected most recent tag: $FROM_TAG"
+            fi
+          fi
+
+          echo "from-tag=$FROM_TAG" >> $GITHUB_OUTPUT
+
+      - name: Build Changelog
+        id: build-changelog
+        if: inputs.gh-release-use-changelog-builder == true
+        uses: mikepenz/release-changelog-builder-action@v6.1.0
+        env:
+          GITHUB_TOKEN: ${{ secrets.PAT || secrets.GITHUB_TOKEN }}
+        with:
+          configuration: ${{ github.run_id }}/${{ inputs.gh-release-changelog-config }}
+          owner: ${{ github.repository_owner }}
+          repo: ${{ github.event.repository.name }}
+          ignorePreReleases: "false"
+          failOnError: "false"
+          fromTag: ${{ steps.determine-from-tag.outputs.from-tag }}
+          toTag: ${{ inputs.release-ref }}
+          mode: ${{ inputs.gh-release-changelog-mode }}
+
       - name: Create release
         id: version-number
         env:
           SHA: ${{ inputs.release-ref }}
           GH_TOKEN: ${{ secrets.PAT }}
           IS_DRY_RUN: ${{ inputs.dry-run }}
+          BUILT_CHANGELOG: ${{ steps.build-changelog.outputs.changelog }}
         run: |
           cd ${{ github.run_id }}
 
@@ -359,7 +406,10 @@ jobs:
           IS_PRERELEASE=$([[ "$IS_RELEASE_CANDIDATE" == "true" || "$IS_ALPHA" == "true" ]] && echo "true" || echo "false")
           NAME="NVIDIA $PROJECT_NAME ${VERSION}"
 
-          if [[ "$IS_RELEASE_CANDIDATE" == "true" ]]; then
+          # Use built changelog if available, otherwise fall back to CHANGELOG.md
+          if [[ -n "$BUILT_CHANGELOG" ]]; then
+            CHANGELOG="$BUILT_CHANGELOG"
+          elif [[ "$IS_RELEASE_CANDIDATE" == "true" ]]; then
             DATE=$(date +"%Y-%m-%d")
             CHANGELOG="Prerelease: $NAME ($DATE)"
           else
@@ -402,10 +452,19 @@ jobs:
             eval "$CMD"
           fi
 
+  publish-docs:
+    needs: [bump-next-version, create-gh-release]
+    uses: ./.github/workflows/release-docs.yml
+    with:
+      dry-run: ${{ inputs.dry-run }}
+      publish-as-latest: true
+      docs-version-override: ${{ needs.bump-next-version.outputs.release-version }}
+      build-docs-ref: ${{ inputs.release-ref }}
+    secrets: inherit
+
   notify:
     needs: [build-test-publish-wheels, create-gh-release]
     runs-on: ubuntu-latest
-    environment: ${{ inputs.dry-run == true && 'public' || 'main' }}
     env:
       GH_URL: https://github.com/${{ github.repository }}/releases/tag/v${{ needs.build-test-publish-wheels.outputs.version }}
       PYPI_URL: https://${{ inputs.dry-run == true && 'test.' || '' }}pypi.org/project/${{ needs.build-test-publish-wheels.outputs.pypi-name }}/${{ needs.build-test-publish-wheels.outputs.version }}/
diff --git a/.github/workflows/_update_dependencies.yml b/.github/workflows/_update_dependencies.yml
index 063b966b5de..a60e69f701b 100644
--- a/.github/workflows/_update_dependencies.yml
+++ b/.github/workflows/_update_dependencies.yml
@@ -9,12 +9,6 @@ on:
     secrets:
       PAT:
         required: true
-      AZURE_CLIENT_ID:
-        required: true
-      AZURE_TENANT_ID:
-        required: true
-      AZURE_SUBSCRIPTION_ID:
-        required: true
       SSH_KEY:
         required: true
       SSH_PWD:
@@ -32,36 +26,31 @@ jobs:
         run: echo "date=$(date +%F)" | tee -a "$GITHUB_OUTPUT"
 
   update-lockfile:
-    environment: nemo-ci
     runs-on: linux-amd64-cpu16
     needs: [pre-flight]
     env:
       SOURCE_BRANCH: ${{ needs.pre-flight.outputs.bump-branch }}
       TARGET_BRANCH: ${{ inputs.target-branch }}
     steps:
-      - name: Install Azure CLI
-        run: curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
-
-      - name: Azure Login
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZURE_CLIENT_ID }}
-          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
-          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-
-      - name: Azure ACR Login
-        run: az acr login --name nemoci
-
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
           ref: ${{ env.TARGET_BRANCH }}
 
+      - name: Mock test data
+        run: mkdir -p assets/
+
+      - name: Fetch NGC Version
+        id: ngc-version
+        run: |
+          NGC_VERSION=$(cat docker/.ngc_version.dev)
+          echo "NGC_VERSION=${NGC_VERSION}" | tee -a "$GITHUB_OUTPUT"
+
       - name: Build container
         env:
           GH_TOKEN: ${{ secrets.PAT }}
         run: |
-          docker build -f docker/Dockerfile.ci.dev --build-arg FROM_IMAGE_NAME="nvcr.io/nvidia/pytorch:25.06-py3" --target=main -t megatron-core .
+          docker build -f docker/Dockerfile.ci.dev --build-arg FROM_IMAGE_NAME="${{ steps.ngc-version.outputs.NGC_VERSION }}" --target=main -t megatron-core .
 
       - name: Create bump branch if not exists
         run: |
@@ -96,7 +85,6 @@ jobs:
   create-pr:
     needs: [update-lockfile, pre-flight]
     runs-on: ubuntu-latest
-    environment: main
     env:
       SOURCE_BRANCH: ${{ needs.pre-flight.outputs.bump-branch }}
       TARGET_BRANCH: ${{ inputs.target-branch }}
@@ -107,18 +95,6 @@ jobs:
           token: ${{ secrets.PAT }}
           ref: ${{ env.TARGET_BRANCH }}
 
-      - name: Install GPG
-        run: sudo apt-get install -y gnupg2
-
-      - name: Import GPG key (for signing)
-        uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
-        id: gpg-action
-        with:
-          gpg_private_key: ${{ secrets.SSH_KEY }}
-          passphrase: ${{ secrets.SSH_PWD }}
-          git_user_signingkey: true
-          git_commit_gpgsign: true
-
       - name: Rebase against ${{ env.SOURCE_BRANCH }}
         run: |
           if git ls-remote --exit-code origin ${{ env.SOURCE_BRANCH }}; then
@@ -150,4 +126,102 @@ jobs:
             🙏 Please merge this PR only if the CI workflow completed successfully.
           commit-message: ${{ env.title }}
           signoff: true
-          committer: "${{ steps.gpg-action.outputs.name }} <${{ steps.gpg-action.outputs.email }}>"
+          committer: "github-actions[bot] <github-actions[bot]@users.noreply.github.com>"
+
+      - name: Post /ok to test comment
+        env:
+          GH_TOKEN: ${{ secrets.PAT }}
+        run: |
+          PR_NUMBER="${{ steps.create-pull-request.outputs.pull-request-number }}"
+          if [ -z "$PR_NUMBER" ]; then
+            echo "No PR was created, skipping comment"
+            exit 0
+          fi
+          SHA="${{ steps.create-pull-request.outputs.pull-request-head-sha }}"
+          gh pr comment "$PR_NUMBER" --body "/ok to test $SHA"
+
+      - name: Wait for CI checks
+        env:
+          GH_TOKEN: ${{ secrets.PAT }}
+        run: |
+          PR_NUMBER="${{ steps.create-pull-request.outputs.pull-request-number }}"
+          if [ -z "$PR_NUMBER" ]; then
+            echo "No PR was created, skipping wait"
+            exit 0
+          fi
+
+          # Fetch required status checks from branch protection rules
+          REQUIRED_CHECKS=$(gh api \
+            "repos/${{ github.repository }}/branches/${{ env.TARGET_BRANCH }}/protection/required_status_checks" \
+            --jq '.checks[].context' 2>/dev/null \
+            || gh api \
+            "repos/${{ github.repository }}/branches/${{ env.TARGET_BRANCH }}/protection/required_status_checks" \
+            --jq '.contexts[]' 2>/dev/null \
+            || true)
+
+          if [ -z "$REQUIRED_CHECKS" ]; then
+            echo "No branch protection rules found for ${{ env.TARGET_BRANCH }}, skipping wait"
+            exit 0
+          fi
+
+          echo "Required checks from branch protection:"
+          echo "$REQUIRED_CHECKS"
+
+          echo "Waiting for required checks to complete on PR #$PR_NUMBER..."
+          i=0
+          INITIALIZED=false
+          while true; do
+            i=$((i + 1))
+            CHECKS_JSON=$(gh pr checks "$PR_NUMBER" --json name,state 2>/dev/null || echo "[]")
+            ALL_DONE=true
+            FAILED_CHECKS=""
+            while IFS= read -r check; do
+              CHECK_STATE=$(echo "$CHECKS_JSON" | jq -r --arg name "$check" '.[] | select(.name == $name) | .state // ""' | tr '[:upper:]' '[:lower:]')
+              case "$CHECK_STATE" in
+                *success*|*pass*|*skip*|*neutral*) ;;
+                *pending*|*queued*|*progress*|*waiting*|*request*|"")
+                  ALL_DONE=false
+                  INITIALIZED=true
+                  break
+                  ;;
+                *)
+                  if [ "$INITIALIZED" = "true" ]; then
+                    FAILED_CHECKS="${FAILED_CHECKS}  - ${check} (${CHECK_STATE})"$'\n'
+                  else
+                    ALL_DONE=false
+                  fi
+                  ;;
+              esac
+            done <<< "$REQUIRED_CHECKS"
+            if [ "$ALL_DONE" = "true" ]; then
+              if [ -n "$FAILED_CHECKS" ]; then
+                echo "Required check(s) did not pass:"
+                echo "$FAILED_CHECKS"
+                exit 1
+              fi
+              echo "All required checks passed!"
+              break
+            fi
+            echo "Checks not yet complete (attempt $i), retrying in 30s..."
+            sleep 30
+          done
+
+      - name: Merge PR
+        env:
+          title: "chore(beep boop 🤖): Bump `uv.lock` (${{ env.TARGET_BRANCH}}) (${{ needs.pre-flight.outputs.date }})"
+        run: |
+          PR_NUMBER="${{ steps.create-pull-request.outputs.pull-request-number }}"
+          if [ -z "$PR_NUMBER" ]; then
+            echo "No PR was created, skipping merge"
+            exit 0
+          fi
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          git fetch origin ${{ env.SOURCE_BRANCH }}
+          git fetch origin ${{ env.TARGET_BRANCH }}
+          git checkout ${{ env.TARGET_BRANCH }}
+          git merge --squash origin/${{ env.SOURCE_BRANCH }}
+          git commit -m "${{ env.title }}"
+          git pull --rebase origin ${{ env.TARGET_BRANCH }}
+          git push origin ${{ env.TARGET_BRANCH }}
+          git push origin --delete ${{ env.SOURCE_BRANCH }}
diff --git a/.github/workflows/auto-assign-milestone.yml b/.github/workflows/auto-assign-milestone.yml
index 8153728f9fd..b972329bac1 100644
--- a/.github/workflows/auto-assign-milestone.yml
+++ b/.github/workflows/auto-assign-milestone.yml
@@ -13,7 +13,6 @@ permissions:
 jobs:
   assign-milestone:
     runs-on: ubuntu-latest
-    environment: nemo-ci
     if: github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Get PR info
diff --git a/.github/workflows/auto-reminder-bot.yml b/.github/workflows/auto-reminder-bot.yml
index c3aa8169b50..37e6e5498e3 100644
--- a/.github/workflows/auto-reminder-bot.yml
+++ b/.github/workflows/auto-reminder-bot.yml
@@ -9,7 +9,6 @@ on:
 
 jobs:
   run-script:
-    environment: main
     name: Run Auto Reminder Bot
     runs-on: ubuntu-latest
     if: github.repository == 'NVIDIA/Megatron-LM'
@@ -28,7 +27,7 @@ jobs:
 
       - name: Run Auto Reminder Bot
         run: |
-          export SLACK_TOKEN=${{ secrets.SLACK_TOKEN }}
-          export SLACK_WEBHOOK_URL=${{ secrets.SLACK_WEBHOOK_URL }}
+          export SLACK_TOKEN=${{ secrets.SLACK_BOT_TOKEN }}
+          export SLACK_WEBHOOK_URL=${{ secrets.SLACK_REVIEW_REMINDER_CHANNEL_WEBHOOK }}
           export GH_TOKEN=${{ secrets.PAT }}
           python tests/test_utils/python_scripts/auto_reminder_github.py
diff --git a/.github/workflows/auto-update-copy-pr-bot.yml b/.github/workflows/auto-update-copy-pr-bot.yml
index 5f6f1ade9e8..3358a747f34 100644
--- a/.github/workflows/auto-update-copy-pr-bot.yml
+++ b/.github/workflows/auto-update-copy-pr-bot.yml
@@ -3,12 +3,11 @@ name: Auto Update Copy PR Bot
 on:
   workflow_dispatch:
   schedule:
-    - cron: '0 0 * * *'
+    - cron: "0 0 * * *"
 
 jobs:
   auto-update-copy-pr-bot:
     runs-on: ubuntu-latest
-    environment: nemo-ci
     if: github.repository == 'NVIDIA/Megatron-LM'
     steps:
       - name: Checkout code
diff --git a/.github/workflows/build-test-publish-wheel.yml b/.github/workflows/build-test-publish-wheel.yml
index bca859d0e61..00711b50806 100644
--- a/.github/workflows/build-test-publish-wheel.yml
+++ b/.github/workflows/build-test-publish-wheel.yml
@@ -17,10 +17,9 @@ name: Build, test, and publish a PyPi wheel (to testpypi).
 on:
   push:
     branches:
-      - dev
       - main
-      - 'pull-request/[0-9]+'
-      - 'deploy-release/*'
+      - "pull-request/[0-9]+"
+      - "deploy-release/*"
   merge_group:
     types: [checks_requested]
 
@@ -34,7 +33,7 @@ permissions:
 
 jobs:
   pre-flight:
-    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.65.5
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.73.2
     if: github.repository == 'NVIDIA/Megatron-LM'
 
   build-test-publish-wheels:
@@ -43,8 +42,7 @@ jobs:
     with:
       no-publish: true
     secrets:
-      TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
-      TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
+      TWINE_PASSWORD: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SVC_PYPI_TOKEN || secrets.SVC_PYPI_TEST_TOKEN }}
 
   build-test-publish-wheel-summary:
     needs: [pre-flight, build-test-publish-wheels]
@@ -66,7 +64,7 @@ jobs:
         env:
           GH_TOKEN: ${{ github.token }}
           GITHUB_RUN_ID: ${{ github.run_id }}
-          SKIPPING_IS_ALLOWED: ${{ needs.pre-flight.outputs.docs_only == 'true' || needs.pre-flight.outputs.is_deployment_workflow == 'true' || needs.pre-flight.outputs.is_merge_group == 'true' || needs.pre-flight.outputs.is_ci_workload == 'true' || github.ref != 'refs/heads/main' }}
+          SKIPPING_IS_ALLOWED: true
         run: |
           FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion != "success")] | length') || echo 0
 
diff --git a/.github/workflows/check_api_backwards_compatibility_workflow.yml b/.github/workflows/check_api_backwards_compatibility_workflow.yml
deleted file mode 100644
index 4ba0ed2780c..00000000000
--- a/.github/workflows/check_api_backwards_compatibility_workflow.yml
+++ /dev/null
@@ -1,273 +0,0 @@
-name: API Compatibility Check
-
-on:
-  push:
-    branches:
-      - dev
-      - main
-      - 'pull-request/[0-9]+'
-      - 'deploy-release/*'
-  merge_group:
-    types: [checks_requested]
-
-  # Allow manual trigger
-  workflow_dispatch:
-    inputs:
-      baseline:
-        description: 'Baseline git reference (tag/branch/commit)'
-        required: true
-
-jobs:
-  pre-flight:
-    name: Pre-flight check
-    runs-on: ubuntu-latest
-    outputs:
-      should_skip: ${{ steps.check_files.outputs.should_skip }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Check if relevant files changed
-        id: check_files
-        run: |
-          # For manual triggers, never skip
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
-            echo "should_skip=false" >> $GITHUB_OUTPUT
-            echo "Manual trigger - will run compatibility check"
-            exit 0
-          fi
-
-          # Determine base SHA based on event type
-          if [ "${{ github.event_name }}" == "merge_group" ]; then
-            BASE_SHA="${{ github.event.merge_group.base_sha }}"
-            echo "Merge group event - comparing against base: $BASE_SHA"
-          else
-            # For push events, use merge-base to find common ancestor
-            # This ensures we only detect changes actually made in this PR branch,
-            # not changes that happened in dev after the branch was created
-            BASE_SHA=$(git merge-base origin/dev HEAD 2>/dev/null || echo "")
-            if [ -z "$BASE_SHA" ]; then
-              # Fallback for branches targeting main
-              BASE_SHA=$(git merge-base origin/main HEAD 2>/dev/null || echo "")
-            fi
-            echo "Push event - comparing against merge-base: $BASE_SHA"
-          fi
-
-          if [ -z "$BASE_SHA" ]; then
-            echo "Could not determine base SHA - will run compatibility check"
-            echo "should_skip=false" >> $GITHUB_OUTPUT
-            exit 0
-          fi
-
-          # Check for changes in megatron/core Python files (excluding tests and legacy)
-          # Note: Using both *.py and **/*.py to match files at root and in subdirectories
-          CHANGED_FILES=$(git diff --name-only "$BASE_SHA" HEAD -- \
-            'megatron/core/*.py' \
-            'megatron/core/**/*.py' \
-            ':!megatron/core/tests/**' \
-            ':!megatron/legacy/**' 2>/dev/null || echo "")
-
-          if [ -z "$CHANGED_FILES" ]; then
-            echo "should_skip=true" >> $GITHUB_OUTPUT
-            echo "No relevant megatron/core files changed - will skip compatibility check"
-          else
-            echo "should_skip=false" >> $GITHUB_OUTPUT
-            echo "Relevant files changed:"
-            echo "$CHANGED_FILES"
-          fi
-
-  check-compatibility:
-    needs: [pre-flight]
-    if: needs.pre-flight.outputs.should_skip != 'true'
-    name: "OPTIONAL: Check API Backward Compatibility"
-    runs-on: ubuntu-latest
-
-    # ============================================================================
-    # Configuration Parameters (modify here)
-    # ============================================================================
-    env:
-      # Default baseline for automatic PR checks
-      # Can be: branch name (e.g., 'main'), commit hash, or tag
-      # Will be resolved to commit hash during execution
-      DEFAULT_BASELINE: 'ed804b49860201e7103ce0f9c1129a330a384a65'
-      # Tag pattern for auto-detection (e.g., 'core_r*', 'core_v*')
-      TAG_PATTERN: 'core_v*'
-      # Tag regex filter (e.g., '^core_v[0-9]+\.[0-9]+\.[0-9]+$' for stable versions only)
-      TAG_REGEX_FILTER: '^core_v[0-9]+\.[0-9]+\.[0-9]+$'
-    # ============================================================================
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0 # Need full history to access baseline ref
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-
-      - name: Install griffe
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install griffe
-          python -c "import griffe; print('Griffe installed successfully')"
-          python -c "from griffe import Object; print('Object import successful')" || echo "Object import from griffe failed"
-          python -c "from griffe.dataclasses import Object; print('Object import from dataclasses successful')" || echo "Object import from dataclasses failed"
-
-      - name: Determine baseline reference
-        id: baseline
-        run: |
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
-            # Use manually specified baseline (branch, tag, or commit hash)
-            BASELINE_REF="${{ github.event.inputs.baseline }}"
-          else
-            # Use the configured default baseline
-            BASELINE_REF="${{ env.DEFAULT_BASELINE }}"
-            
-            # Uncomment below to auto-detect from tags instead:
-            # BASELINE_REF=$(git tag -l '${{ env.TAG_PATTERN }}' | grep -E '${{ env.TAG_REGEX_FILTER }}' | sort -V | tail -1)
-            # if [ -z "$BASELINE_REF" ]; then
-            #   echo "Warning: No tags matching pattern found. Using default: ${{ env.DEFAULT_BASELINE }}" >&2
-            #   BASELINE_REF="${{ env.DEFAULT_BASELINE }}"
-            # fi
-          fi
-
-          # Resolve baseline to commit hash (works for branches, tags, or commit hashes)
-          BASELINE_HASH=$(git rev-parse "$BASELINE_REF")
-
-          echo "baseline=$BASELINE_HASH" >> $GITHUB_OUTPUT
-          echo "Using baseline: $BASELINE_REF (resolved to commit: $BASELINE_HASH)"
-
-      - name: Run compatibility check
-        id: compat_check
-        run: |
-          # Save output to file for later display
-          python scripts/check_api_backwards_compatibility.py \
-            --baseline ${{ steps.baseline.outputs.baseline }} \
-            --verbose 2>&1 | tee compat_check_output.txt
-
-          # Capture exit code
-          EXIT_CODE=${PIPESTATUS[0]}
-          echo "exit_code=$EXIT_CODE" >> $GITHUB_OUTPUT
-          exit $EXIT_CODE
-        continue-on-error: true
-
-      - name: Fail job if breaking changes detected
-        if: steps.compat_check.outcome == 'failure'
-        run: |
-          echo ""
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo "🔍 WHAT IS THIS CHECK?"
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo ""
-          echo "This check ensures that changes to Megatron Core's public API do not"
-          echo "break backward compatibility for users. It compares your PR against"
-          echo "the latest stable release to detect breaking changes in:"
-          echo ""
-          echo "  • Function signatures (parameters, order, types)"
-          echo "  • Class structures and methods"
-          echo "  • Return types and public interfaces"
-          echo ""
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo "🛠️  HOW TO FIX THIS"
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo ""
-          echo "Choose ONE of these resolution strategies:"
-          echo ""
-          echo "1️⃣  REVERT THE BREAKING CHANGE (Recommended)"
-          echo "   → Modify your code to preserve backward compatibility"
-          echo "   → Add new parameters as optional (with defaults)"
-          echo "   → Keep existing parameters in the same order"
-          echo ""
-          echo "2️⃣  MARK AS INTERNAL API (If this is internal code)"
-          echo "   → Add @internal_api decorator from megatron.core.utils"
-          echo ""
-          echo "   Example (for classes):"
-          echo "      from megatron.core.utils import internal_api"
-          echo ""
-          echo "      @internal_api"
-          echo "      class ExperimentalFeature:"
-          echo "          pass"
-          echo ""
-          echo "   Example (for functions):"
-          echo "      from megatron.core.utils import internal_api"
-          echo ""
-          echo "      @internal_api"
-          echo "      def internal_helper_function():"
-          echo "          pass"
-          echo ""
-          echo "3️⃣  MARK AS EXPERIMENTAL API (If this is experimental code)"
-          echo "   → Add @experimental_api decorator from megatron.core.utils"
-          echo ""
-          echo "   Example:"
-          echo "      from megatron.core.utils import experimental_api"
-          echo ""
-          echo "      @experimental_api"
-          echo "      class ExperimentalFeature:"
-          echo "          pass"
-          echo ""
-          echo "4️⃣  USE DEPRECATION (For gradual API changes)"
-          echo "   → Add @deprecated decorator for transition period"
-          echo "   → Example:"
-          echo "      from megatron.core.utils import deprecated"
-          echo ""
-          echo "      @deprecated(version='1.0', removal_version='2.0',"
-          echo "                  alternative='new_function')"
-          echo "      def old_function():"
-          echo "          pass"
-          echo ""
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo "📋 BREAKING CHANGES DETECTED"
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo ""
-          cat compat_check_output.txt
-          echo ""
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo "📚 MORE INFORMATION"
-          echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-          echo ""
-          echo "📖 Full documentation: docs/api-backwards-compatibility-check.md"
-          echo "🔧 Checker script: scripts/check_api_backwards_compatibility.py"
-          echo "❓ Questions? Check the docs or ask in #megatron-core"
-          echo ""
-
-          echo "::error::Breaking API changes detected. Please review the output above and choose a resolution strategy."
-          exit 1
-
-      - name: Success message
-        if: steps.compat_check.outcome == 'success'
-        run: |
-          echo "::notice::✅ No breaking API changes detected!"
-
-  api-backward-compatibility-summary:
-    needs: [pre-flight, check-compatibility]
-    runs-on: ubuntu-latest
-    name: "OPTIONAL: API Backward Compatibility Check Summary"
-    if: always() && !cancelled()
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Validate workflow result
-        shell: bash -x -e -u -o pipefail {0}
-        env:
-          GH_TOKEN: ${{ github.token }}
-          SKIPPING_IS_ALLOWED: ${{ needs.pre-flight.outputs.should_skip == 'true' }}
-        run: |
-          FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion != "success" and .name != "OPTIONAL: API Backward Compatibility Check Summary")] | length') || echo 0
-
-          if [ "${FAILED_JOBS:-0}" -eq 0 ] || [ "$SKIPPING_IS_ALLOWED" == "true" ]; then
-              if [ "$SKIPPING_IS_ALLOWED" == "true" ]; then
-                  echo "✅ Compatibility check was skipped (no relevant files changed)"
-              else
-                  echo "✅ All checks passed successfully"
-              fi
-              exit 0
-          else
-              echo "❌ Found $FAILED_JOBS failed job(s)"
-              gh run view $GITHUB_RUN_ID --json jobs --jq '.jobs[] | select(.status == "completed" and .conclusion != "success" and .name != "OPTIONAL: API Backward Compatibility Check Summary") | .name'
-              exit 1
-          fi
diff --git a/.github/workflows/cherry-pick-release-commit.yml b/.github/workflows/cherry-pick-release-commit.yml
index 58b447939a7..9da305f07e6 100644
--- a/.github/workflows/cherry-pick-release-commit.yml
+++ b/.github/workflows/cherry-pick-release-commit.yml
@@ -17,7 +17,6 @@ on:
   push:
     branches:
       - main
-      - dev
 
 jobs:
   cherry-pick:
@@ -27,5 +26,5 @@ jobs:
       target-branches-pattern: 'core_(*dev_)?r[0-9]+\.[0-9]+\.[0-9]+'
     secrets:
       PAT: ${{ secrets.PAT }}
-      SLACK_WEBHOOK_ADMIN: ${{ secrets.SLACK_WEBHOOK_ADMIN }}
-      SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
+      SLACK_WEBHOOK_ADMIN: ${{ secrets.SLACK_TEAM_GROUP_ID }}
+      SLACK_WEBHOOK: ${{ secrets.SLACK_CI_CHANNEL_WEBHOOK }}
diff --git a/.github/workflows/cicd-approve-test-queue.yml b/.github/workflows/cicd-approve-test-queue.yml
index 1c35031cb35..2cba41eafb8 100644
--- a/.github/workflows/cicd-approve-test-queue.yml
+++ b/.github/workflows/cicd-approve-test-queue.yml
@@ -155,8 +155,6 @@ jobs:
 
               workflow_id = workflow["id"]
               workflow_name = workflow["display_title"]
-              pr_info = workflow.get("pull_requests", [{}])[0]
-              pr_number = pr_info.get("number", "unknown")
               print(f"Approving workflow {workflow_name} with Run Id: {workflow_id}")
 
               deployment_url = f"actions/runs/{workflow_id}/pending_deployments"
@@ -183,8 +181,8 @@ jobs:
     steps:
       - name: Notify
         env:
-          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
-          SLACK_WEBHOOK_ADMIN: <!subteam^${{ secrets.SLACK_WEBHOOK_ADMIN }}>
+          SLACK_WEBHOOK: ${{ secrets.SLACK_CI_CHANNEL_WEBHOOK }}
+          SLACK_WEBHOOK_ADMIN: <!subteam^${{ secrets.SLACK_TEAM_GROUP_ID }}>
           GITHUB_RUN_ID: ${{ github.run_id }}
           GITHUB_REPOSITORY: ${{ github.repository }}
         run: |
diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml
index 3aff7995099..2cc025baf99 100644
--- a/.github/workflows/cicd-main.yml
+++ b/.github/workflows/cicd-main.yml
@@ -77,6 +77,9 @@ jobs:
         run: |
           # Skip SSO check for scheduled jobs, main branch, dev branch, or merge groups
           if [ "${{ env.SCHEDULED_JOB }}" == "true" ] || [ "${IS_MAIN_BRANCH}" == "true" ] || [ "${IS_DEV_BRANCH}" == "true" ] || [ "${IS_MERGE_GROUP}" == "true" ]; then
+            echo "is_maintainer=true" | tee -a $GITHUB_OUTPUT
+            exit 0
+          fi
 
           # Use SSO membership check result
           IS_MEMBER="${{ steps.check-sso.outputs.is_member }}"
@@ -126,7 +129,7 @@ jobs:
   pre-flight:
     needs: [is-not-external-contributor]
     if: github.repository == 'NVIDIA/Megatron-LM'
-    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.65.10
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.73.2
 
   linting:
     runs-on: ubuntu-latest
@@ -186,6 +189,127 @@ jobs:
           echo "Running CI tests"
           echo "is_merge_group: ${{ needs.pre-flight.outputs.is_merge_group }}"
 
+  cicd-parse-downstream-testing:
+    runs-on: ubuntu-latest
+    needs:
+      - pre-flight
+      - cicd-wait-in-queue
+    if: |
+      needs.pre-flight.result != 'cancelled'
+      && needs.cicd-wait-in-queue.result != 'cancelled'
+      && (
+        success()
+        || needs.pre-flight.outputs.is_ci_workload == 'true'
+        || needs.pre-flight.outputs.force_run_all == 'true'
+        || needs.pre-flight.outputs.is_merge_group == 'true'
+      )
+      && !cancelled()
+    outputs:
+      mbridge-test-suite: ${{ steps.select-mbridge-test-suite.outputs.main }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Get PR info
+        id: get-pr-info
+        if: startsWith(github.ref, 'refs/heads/pull-request/')
+        uses: nv-gha-runners/get-pr-info@main
+
+      - name: Select MBridge test suite
+        id: select-mbridge-test-suite
+        env:
+          GH_TOKEN: ${{ secrets.PAT }}
+        run: |
+          PR_NUMBER=${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number }}
+          TEST_SUITE=$(gh pr view $PR_NUMBER --json labels | jq -r 'if [.labels[].name] | any(. == "Run MBridge tests") then "all" else "unit-only" end')
+          echo "main=$TEST_SUITE" | tee -a $GITHUB_OUTPUT
+
+      - name: How-To
+        run: bash .github/scripts/readme.sh
+
+  cicd-mbridge-testing:
+    runs-on: ubuntu-latest
+    needs:
+      - pre-flight
+      - cicd-wait-in-queue
+      - cicd-parse-downstream-testing
+    if: |
+      needs.pre-flight.result != 'cancelled'
+      && needs.cicd-wait-in-queue.result != 'cancelled'
+      && needs.cicd-parse-downstream-testing.result != 'cancelled'
+      && (
+        success()
+        || needs.pre-flight.outputs.is_ci_workload == 'true'
+        || needs.pre-flight.outputs.force_run_all == 'true'
+        || needs.pre-flight.outputs.is_merge_group == 'true'
+      )
+      && !cancelled()
+    steps:
+      - name: Get PR info
+        id: get-pr-info
+        if: startsWith(github.ref, 'refs/heads/pull-request/')
+        uses: nv-gha-runners/get-pr-info@main
+
+      - name: Checkout MBridge and create testing branch
+        uses: actions/checkout@v4
+        with:
+          ref: main
+          repository: NVIDIA-NeMo/Megatron-Bridge
+          path: megatron-bridge
+          token: ${{ secrets.PAT }}
+
+      - name: Create testing branch
+        env:
+          MBRIDGE_BRANCH_NAME: mcore-testing-${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number || github.run_id }}
+        run: |
+          cd megatron-bridge
+          git fetch origin main
+          git checkout -b ${{ env.MBRIDGE_BRANCH_NAME }} origin/main
+          git push origin ${{ env.MBRIDGE_BRANCH_NAME }} --force
+
+      - name: Get merge commit sha
+        shell: bash -x -e -u -o pipefail {0}
+        id: sha
+        env:
+          IS_PR: ${{ startsWith(github.ref, 'refs/heads/pull-request/') }}
+          IS_MERGE_GROUP: ${{ github.event_name == 'merge_group' }}
+        run: |
+          if [[ "$IS_PR" == "true" ]]; then
+            SHA=${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').merge_commit_sha }}
+          elif [[ "$IS_MERGE_GROUP" == "true" ]]; then
+            SHA=${{ github.event.merge_group.head_sha }}
+          else
+            SHA=${GITHUB_SHA}
+          fi
+          echo "main=${SHA}" | tee -a "$GITHUB_OUTPUT"
+
+      - name: Trigger MBridge tests
+        uses: convictional/trigger-workflow-and-wait@v1.6.5
+        env:
+          MBRIDGE_BRANCH_NAME: mcore-testing-${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number || github.run_id }}
+        with:
+          owner: NVIDIA-NeMo
+          repo: Megatron-Bridge
+          workflow_file_name: cicd-main.yml
+          github_token: ${{ secrets.PAT }}
+          ref: ${{ env.MBRIDGE_BRANCH_NAME }}
+          wait_interval: 60
+          propagate_failure: true
+          client_payload: |
+            {
+              "mcore_ref": "${{ steps.sha.outputs.main }}",
+              "test_suite": "${{ needs.cicd-parse-downstream-testing.outputs.mbridge-test-suite }}",
+              "triggered_by": "https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}"
+            }
+
+      - name: Delete testing branch
+        if: always()
+        env:
+          MBRIDGE_BRANCH_NAME: mcore-testing-${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number || github.run_id }}
+        run: |
+          cd megatron-bridge
+          git push origin --delete ${{ env.MBRIDGE_BRANCH_NAME }}
+
   cicd-container-build:
     needs: [is-not-external-contributor, pre-flight, cicd-wait-in-queue]
     runs-on: ${{ needs.is-not-external-contributor.outputs.selected_runner }}
@@ -350,7 +474,7 @@ jobs:
       - name: Parse unit tests
         id: parse-unit-tests
         run: |
-          cat tests/test_utils/recipes/unit-tests.yaml | yq -o json '[.products[].test_case[] | { "bucket": .}] | sort_by(.model, .test_case)' | jq -c > unit-tests.json
+          cat tests/test_utils/recipes/h100/unit-tests.yaml | yq -o json '[.products[].test_case[] | { "bucket": .}] | sort_by(.model, .test_case)' | jq -c > unit-tests.json
           echo "unit-tests=$(cat unit-tests.json)" | tee -a $GITHUB_OUTPUT
 
   cicd-unit-tests-latest:
@@ -568,8 +692,8 @@ jobs:
           GITHUB_RUN_ID: ${{ github.run_id }}
           SKIPPING_IS_ALLOWED: ${{ needs.pre-flight.outputs.docs_only == 'true' || needs.pre-flight.outputs.is_deployment_workflow == 'true' || needs.pre-flight.outputs.is_merge_group == 'true' || needs.pre-flight.outputs.is_ci_workload == 'true' }}
         run: |
-          FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion == "failure" and .name != "merge-queue-notification")] | length') || echo 0
-          SKIPPED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion == "skipped" and .name != "merge-queue-notification")] | length') || echo 0
+          FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion == "failure" and .name != "merge-queue-notification" and .name != "cicd-mbridge-testing")] | length') || echo 0
+          SKIPPED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion == "skipped" and .name != "merge-queue-notification" and .name != "cicd-mbridge-testing")] | length') || echo 0
 
           if [ "${FAILED_JOBS:-0}" -eq 0 ] && ([ "${SKIPPED_JOBS:-0}" -eq 0 ] || [ "$SKIPPING_IS_ALLOWED" == "true" ]); then
               echo "✅ All previous jobs completed successfully"
diff --git a/.github/workflows/config/changelog-config.json b/.github/workflows/config/changelog-config.json
new file mode 100644
index 00000000000..e640b90a0f3
--- /dev/null
+++ b/.github/workflows/config/changelog-config.json
@@ -0,0 +1,24 @@
+{
+    "categories": [],
+    "ignore_labels": [
+      "ignore"
+    ],
+    "sort": "ASC",
+    "template": "\n${{CHANGELOG}}\n\n<details><summary>Changelog Details</summary>\n\n${{UNCATEGORIZED}}\n</details>\n",
+    "pr_template": "- ${{TITLE}} by @${{AUTHOR}} :: PR: #${{NUMBER}}",
+    "commit_template": "- ${{TITLE}} by @${{AUTHOR}}",
+    "empty_template": "${{OWNER}}\n${{REPO}}\n${{FROM_TAG}}\n${{TO_TAG}}",
+    "duplicate_filter": {
+      "pattern": ".+",
+      "on_property": "title",
+      "method": "match"
+    },
+    "transformers": [],
+    "max_tags_to_fetch": 100,
+    "max_pull_requests": 500,
+    "max_back_track_time_days": 365,
+    "exclude_merge_branches": [],
+    "tag_resolver": {
+      "method": "semver"
+    }
+}
diff --git a/.github/workflows/copyright-check.yml b/.github/workflows/copyright-check.yml
index ac0d49daf9a..a7f51cd8a0e 100644
--- a/.github/workflows/copyright-check.yml
+++ b/.github/workflows/copyright-check.yml
@@ -17,14 +17,14 @@ name: Copyright check
 on:
   push:
     branches:
-      - 'pull-request/[0-9]+'
-      - 'deploy-release/*'
+      - "pull-request/[0-9]+"
+      - "deploy-release/*"
   merge_group:
     types: [checks_requested]
 
 jobs:
   pre-flight:
-    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.65.10
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.73.2
     if: github.repository == 'NVIDIA/Megatron-LM'
 
   copyright-check:
@@ -48,8 +48,13 @@ jobs:
       && github.repository == 'NVIDIA/Megatron-LM'
     runs-on: ubuntu-latest
     steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
       - name: Result
         env:
+          GH_TOKEN: ${{ github.token }}
+          GITHUB_RUN_ID: ${{ github.run_id }}
           SKIPPING_IS_ALLOWED: ${{ needs.pre-flight.outputs.docs_only == 'true' || needs.pre-flight.outputs.is_deployment_workflow == 'true' || needs.pre-flight.outputs.is_merge_group == 'true' || needs.pre-flight.outputs.is_ci_workload == 'true' }}
         run: |
           FAILED_JOBS=$(gh run view $GITHUB_RUN_ID --json jobs --jq '[.jobs[] | select(.status == "completed" and .conclusion != "success")] | length') || echo 0
diff --git a/.github/workflows/dependabot.yml b/.github/workflows/dependabot.yml
index 9dc1e6ac5a9..81a5cd57d12 100644
--- a/.github/workflows/dependabot.yml
+++ b/.github/workflows/dependabot.yml
@@ -11,7 +11,6 @@ permissions:
 jobs:
   get-release-branch-names:
     runs-on: ubuntu-latest
-    environment: nemo-ci
     outputs:
       mcore: ${{ steps.get-branch.outputs.mcore_release_branch }}
     if: github.repository == 'NVIDIA/Megatron-LM'
@@ -21,11 +20,11 @@ jobs:
         env:
           PAT: ${{ secrets.PAT }}
         run: |
-          latest_branch=$(git ls-remote --heads https://token:${PAT}@github.com/NVIDIA-NeMo/Eval.git 'refs/heads/r*' | 
+          latest_branch=$(git ls-remote --heads https://token:${PAT}@github.com/NVIDIA/Megatron-LM.git 'refs/heads/core_r*' | 
             grep -o 'core_r[0-9]\+\.[0-9]\+\.[0-9]\+' | 
             sort -V | 
             tail -n1)
-          echo "mcore_release_branch=$latest_branch" >> $GITHUB_OUTPUT
+          echo "mcore_release_branch=$latest_branch" | tee -a $GITHUB_OUTPUT
 
   bump-tags:
     needs: [get-release-branch-names]
@@ -41,9 +40,6 @@ jobs:
       target-branch: ${{ matrix.target-branch }}
     secrets:
       PAT: ${{ secrets.PAT }}
-      AZURE_CLIENT_ID: ${{ secrets.AZURE_CLIENT_ID }}
-      AZURE_TENANT_ID: ${{ secrets.AZURE_TENANT_ID }}
-      AZURE_SUBSCRIPTION_ID: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
       SSH_KEY: ${{ secrets.SSH_KEY }}
       SSH_PWD: ${{ secrets.SSH_PWD }}
 
@@ -54,8 +50,8 @@ jobs:
     steps:
       - name: Notify
         env:
-          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
-          SLACK_WEBHOOK_ADMIN: <!subteam^${{ secrets.SLACK_WEBHOOK_ADMIN }}>
+          SLACK_WEBHOOK: ${{ secrets.SLACK_CI_CHANNEL_WEBHOOK }}
+          SLACK_WEBHOOK_ADMIN: <!subteam^${{ secrets.SLACK_TEAM_GROUP_ID }}>
           GITHUB_RUN_ID: ${{ github.run_id }}
           GITHUB_REPOSITORY: ${{ github.repository }}
         run: |
diff --git a/.github/workflows/install-test.yml b/.github/workflows/install-test.yml
index ece9184ee94..5a0abb8596d 100644
--- a/.github/workflows/install-test.yml
+++ b/.github/workflows/install-test.yml
@@ -22,14 +22,14 @@ on:
     branches:
       - dev
       - main
-      - 'pull-request/[0-9]+'
-      - 'deploy-release/*'
+      - "pull-request/[0-9]+"
+      - "deploy-release/*"
   merge_group:
     types: [checks_requested]
 
 jobs:
   pre-flight:
-    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.65.5
+    uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_cicd_preflight.yml@v0.73.2
     if: github.repository == 'NVIDIA/Megatron-LM'
 
   pip-test-pytorch:
@@ -43,11 +43,10 @@ jobs:
     name: Pip - Python${{ matrix.python-version }} - AMD64/Linux - NGC PyTorch
     container:
       image: nvcr.io/nvidia/pytorch:25.05-py3
-    environment: nemo-ci
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.12']
+        python-version: ["3.12"]
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -89,11 +88,10 @@ jobs:
     name: UV - Python${{ matrix.python-version }} - AMD64/Linux - NGC PyTorch
     container:
       image: nvcr.io/nvidia/pytorch:25.05-py3
-    environment: nemo-ci
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.12']
+        python-version: ["3.12"]
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
diff --git a/.github/workflows/oncall-rotation.yml b/.github/workflows/oncall-rotation.yml
index 71ae094e6c8..a621be7f652 100644
--- a/.github/workflows/oncall-rotation.yml
+++ b/.github/workflows/oncall-rotation.yml
@@ -17,7 +17,7 @@ name: Oncall Rotation
 on:
   schedule:
     # Runs at 09:00 UTC every Wednesday
-    - cron: '0 9 * * 3'
+    - cron: "0 9 * * 3"
   workflow_dispatch:
 
 permissions:
@@ -25,7 +25,6 @@ permissions:
 
 jobs:
   rotate-schedule:
-    environment: main
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
@@ -36,7 +35,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
 
       - name: Rotate Schedule
         env:
@@ -59,4 +58,3 @@ jobs:
           git commit -m "chore: rotate oncall schedule" || echo "No changes to commit"
           git pull --rebase
           git push origin HEAD:main
-
diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml
index d15ea74f052..6d619a8a1bc 100644
--- a/.github/workflows/release-docs.yml
+++ b/.github/workflows/release-docs.yml
@@ -20,23 +20,62 @@ on:
         required: true
         type: boolean
         default: true
-      version-number:
-        description: Version number to release this as (use `latest` for main branch)
-        required: true
+      publish-as-latest:
+        description: Publish as Latest stable version.
+        required: false
+        type: boolean
+        default: true
+      docs-version-override:
+        description: Docs version if commit is not tagged
+        required: false
         type: string
+        default: ""
+      update-version-picker:
+        description: Update version picker.
+        required: false
+        type: boolean
+        default: true
       notify-emails:
         description: Email addresses to send the notification to. Format as "me@me.com,you@you.com".
+        required: false
+        type: string
+  workflow_call:
+    inputs:
+      dry-run:
+        description: Whether to run the workflow in dry-run mode
         required: true
+        type: boolean
+        default: true
+      publish-as-latest:
+        description: Publish as Latest stable version.
+        required: false
+        type: boolean
+        default: true
+      docs-version-override:
+        description: Docs version if commit is not tagged
+        required: false
+        type: string
+        default: ""
+      update-version-picker:
+        description: Update version picker.
+        required: false
+        type: boolean
+        default: true
+      notify-emails:
+        description: Email addresses to send the notification to. Format as "me@me.com,you@you.com".
+        required: false
         type: string
-      aws-region:
-        description: AWS region
+      build-docs-ref:
+        description: Reference to build the docs from
         required: false
         type: string
-        default: us-east-1
+        default: ${{ github.sha }}
 
 jobs:
   build-docs:
     uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_build_docs.yml@v0.67.0
+    with:
+      ref: ${{ inputs.build-docs-ref }}
 
   publish-docs:
     runs-on: ubuntu-latest
@@ -45,7 +84,7 @@ jobs:
       - uses: actions/checkout@v6
         with:
           repository: NVIDIA-NeMo/FW-CI-templates
-          ref: v0.67.2
+          ref: v0.74.0
           path: FW-CI-templates
 
       - uses: ./FW-CI-templates/.github/actions/publish-docs
@@ -59,10 +98,12 @@ jobs:
           artifacts-name: docs-html
           artifacts-path: _build/html
           emails-csv: ${{ inputs.notify-emails && format('{0},{1}', vars.docs_release_emails, inputs.notify-emails) || vars.docs_release_emails }}
-          overwrite-latest-on-tag: false
+          overwrite-latest-on-tag: ${{ inputs.publish-as-latest }}
+          docs-version-override: ${{ inputs.docs-version-override }}
+          update-version-picker: ${{ inputs.update-version-picker }}
           run-on-version-tag-only: ${{ github.ref_name != 'main' }}
           request-name: megatron-core-publish-docs-${{ github.run_id }}
-          aws-region: ${{ inputs.aws-region }}
+          aws-region: ${{ vars.DOCS_AWS_REGION }}
           aws-role-to-assume: ${{ secrets.AWS_ASSUME_ROLE_ARN }}
           aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
diff --git a/.github/workflows/release-freeze.yml b/.github/workflows/release-freeze.yml
index 82f26168bd6..dc4bad0a9a7 100644
--- a/.github/workflows/release-freeze.yml
+++ b/.github/workflows/release-freeze.yml
@@ -42,5 +42,5 @@ jobs:
       freeze-commit: ${{ inputs.freeze-commit }}
       dry-run: ${{ inputs.dry-run }}
     secrets:
-      SLACK_WEBHOOK: ${{ secrets.SLACK_RELEASE_ENDPOINT }}
-      SLACK_WEBHOOK_ADMIN: ${{ secrets.SLACK_WEBHOOK_ADMIN }}
+      SLACK_WEBHOOK: ${{ secrets.SLACK_MAIN_CHANNEL_WEBHOOK }}
+      SLACK_WEBHOOK_ADMIN: ${{ secrets.SLACK_TEAM_GROUP_ID }}
diff --git a/.github/workflows/release-nightly-docs.yml b/.github/workflows/release-nightly-docs.yml
new file mode 100644
index 00000000000..89ceb1fbcd8
--- /dev/null
+++ b/.github/workflows/release-nightly-docs.yml
@@ -0,0 +1,29 @@
+# Copyright (c) 2026, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: Release Nightly Docs
+
+on:
+  schedule:
+    - cron: "0 10 * * *"
+
+jobs:
+  call-release-docs:
+    uses: ./.github/workflows/release-docs.yml
+    with:
+      dry-run: false
+      publish-as-latest: false
+      docs-version-override: "nightly"
+      update-version-picker: false
+    secrets: inherit
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
index aa04408689b..647e6af2379 100644
--- a/.github/workflows/release.yaml
+++ b/.github/workflows/release.yaml
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-name: 'Release Megatron-Core'
+name: "Release Megatron-Core"
 
 on:
   workflow_dispatch:
@@ -30,6 +30,16 @@ on:
         required: true
         default: true
         type: boolean
+      generate-changelog:
+        description: Generate changelog
+        required: false
+        default: true
+        type: boolean
+      publish-docs:
+        description: Publish docs
+        required: false
+        default: true
+        type: boolean
       version-bump-branch:
         description: Branch for version bump
         required: true
@@ -47,9 +57,9 @@ jobs:
       dry-run: ${{ inputs.dry-run || false }}
       version-bump-branch: ${{ inputs.version-bump-branch || github.ref_name }}
       create-gh-release: ${{ inputs.create-gh-release || true }}
+      gh-release-use-changelog-builder: ${{ inputs.generate-changelog }}
+      publish-docs: ${{ inputs.publish-docs }}
     secrets:
-      TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }}
-      TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
-      SLACK_WEBHOOK_ADMIN: ${{ secrets.SLACK_WEBHOOK_ADMIN }}
-      SLACK_WEBHOOK: ${{ secrets.SLACK_RELEASE_ENDPOINT }}
+      TWINE_PASSWORD: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SVC_PYPI_TOKEN || secrets.SVC_PYPI_TEST_TOKEN }}
+      SLACK_WEBHOOK: ${{ (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/r')) && secrets.SLACK_MAIN_CHANNEL_WEBHOOK || secrets.SLACK_CI_CHANNEL_WEBHOOK }}
       PAT: ${{ secrets.PAT }}
diff --git a/.github/workflows/sync-team-usergroups.yml b/.github/workflows/sync-team-usergroups.yml
index 1c6cecaeb7a..fb48a6ca5d4 100644
--- a/.github/workflows/sync-team-usergroups.yml
+++ b/.github/workflows/sync-team-usergroups.yml
@@ -16,10 +16,11 @@ name: Sync GitHub Teams to Slack User Groups
 
 on:
   workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *"
 
 jobs:
   sync-usergroups:
-    environment: main
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
@@ -28,7 +29,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
 
       - name: Sync Teams to User Groups
         env:
diff --git a/.github/workflows/trigger-mbridge-tests.yml b/.github/workflows/trigger-mbridge-tests.yml
index b1a3aa0089d..023851e966a 100644
--- a/.github/workflows/trigger-mbridge-tests.yml
+++ b/.github/workflows/trigger-mbridge-tests.yml
@@ -2,182 +2,41 @@
 # SPDX-License-Identifier: Apache-2.0
 
 name: Trigger MBridge Tests
-# Remote testing of MBridge from MCore
-# Triggers MBridge CI tests with current MCore commit to verify backward compatibility
-
 on:
-  # Manual trigger only
   workflow_dispatch:
     inputs:
       mbridge_ref:
-        description: 'MBridge branch/ref to trigger'
+        description: "MBridge branch/ref to trigger"
         required: false
         type: string
-        default: 'main'
-      run_cicd_main:
-        description: 'Run cicd-main.yml (full CI/CD)'
-        required: false
-        type: boolean
-        default: true
-      run_install_test:
-        description: 'Run install-test.yml (quick install check)'
-        required: false
-        type: boolean
-        default: true
+        default: "main"
       test_suite:
-        description: 'Test suite to run (for cicd-main)'
+        description: "Test suite to run"
         required: false
         type: choice
         options:
-          - 'all'
-          - 'unit-only'
-          - 'functional-only'
-        default: 'all'
+          - "all"
+          - "unit-only"
+          - "functional-only"
+        default: "all"
 
 jobs:
-  # First job: Get MCore commit info (shared by all matrix jobs)
-  get-mcore-info:
+  trigger-mbridge-tests:
     runs-on: ubuntu-latest
-    outputs:
-      sha: ${{ steps.mcore_info.outputs.sha }}
-      short_sha: ${{ steps.mcore_info.outputs.short_sha }}
-      branch: ${{ steps.mcore_info.outputs.branch }}
-      repo_url: ${{ steps.mcore_info.outputs.repo_url }}
     steps:
-      - name: Checkout MCore
-        uses: actions/checkout@v4
+      - name: Trigger MBridge tests
+        uses: convictional/trigger-workflow-and-wait@v1.6.5
         with:
-          fetch-depth: 0
-      
-      - name: Get MCore commit info
-        id: mcore_info
-        run: |
-          echo "sha=$(git rev-parse HEAD)" >> $GITHUB_OUTPUT
-          echo "short_sha=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
-          echo "branch=${GITHUB_REF#refs/heads/}" >> $GITHUB_OUTPUT
-          
-          # Get repo URL from origin remote, fallback to constructing from github context
-          REPO_URL=$(git remote get-url origin 2>/dev/null || echo "${{ github.server_url }}/${{ github.repository }}.git")
-          echo "repo_url=${REPO_URL}" >> $GITHUB_OUTPUT
-
-          echo "📦 MCore commit: $(git rev-parse --short HEAD)"
-          echo "🌿 Branch: ${GITHUB_REF#refs/heads/}"
-          echo "📍 Repo: ${REPO_URL}"
-
-  # Matrix job: Trigger and monitor MBridge workflows in parallel
-  trigger-and-monitor:
-    needs: [get-mcore-info]
-    runs-on: ubuntu-latest
-    continue-on-error: true  # Don't fail workflow if monitoring times out
-    strategy:
-      fail-fast: false  # Continue other matrix jobs even if one fails
-      matrix:
-        include:
-          - workflow: install-test.yml
-            name: Install Test
-          - workflow: cicd-main.yml
-            name: CI/CD Main
-    
-    name: ${{ matrix.name }}
-    
-    steps:
-      - name: Check if workflow should run
-        id: should_run
-        run: |
-          if [[ "${{ matrix.workflow }}" == "install-test.yml" && "${{ inputs.run_install_test }}" == "true" ]]; then
-            echo "run=true" >> $GITHUB_OUTPUT
-          elif [[ "${{ matrix.workflow }}" == "cicd-main.yml" && "${{ inputs.run_cicd_main }}" == "true" ]]; then
-            echo "run=true" >> $GITHUB_OUTPUT
-          else
-            echo "run=false" >> $GITHUB_OUTPUT
-            echo "⏭️ Skipping ${{ matrix.workflow }} (not enabled)"
-          fi
-      
-      - name: Trigger ${{ matrix.workflow }}
-        if: steps.should_run.outputs.run == 'true'
-        id: trigger
-        env:
-          GH_TOKEN: ${{ secrets.PAT }}
-        run: |
-          echo "🚀 Triggering ${{ matrix.workflow }} | MCore: ${{ needs.get-mcore-info.outputs.short_sha }} | MBridge: ${{ inputs.mbridge_ref }}"
-          
-          gh workflow run ${{ matrix.workflow }} \
-            --repo NVIDIA-NeMo/Megatron-Bridge --ref ${{ inputs.mbridge_ref }} \
-            --field mcore_commit=${{ needs.get-mcore-info.outputs.sha }} \
-            --field mcore_branch=${{ needs.get-mcore-info.outputs.branch }} \
-            --field mcore_repo=${{ needs.get-mcore-info.outputs.repo_url }} \
-            --field test_suite=${{ inputs.test_suite }} \
-            --field triggered_by=mcore-ci
-      
-      - name: Get run ID
-        if: steps.should_run.outputs.run == 'true'
-        id: get_run_id
-        env:
-          GH_TOKEN: ${{ secrets.PAT }}
-        run: |
-          sleep 10  # Wait for run to appear
-          RUN_ID=$(gh run list \
-            --repo NVIDIA-NeMo/Megatron-Bridge \
-            --workflow=${{ matrix.workflow }} \
-            --limit 5 \
-            --json databaseId,createdAt \
-            --jq "sort_by(.createdAt) | reverse | .[0] | .databaseId")
-          
-          echo "run_id=${RUN_ID}" >> $GITHUB_OUTPUT
-          echo "📋 Run ID: ${RUN_ID}"
-          
-          cat >> $GITHUB_STEP_SUMMARY << EOF
-          ## 🔄 ${{ matrix.name }} Triggered
-          
-          **MCore:** \`${{ needs.get-mcore-info.outputs.short_sha }}\` | **MBridge:** \`${{ inputs.mbridge_ref }}\` | **Suite:** \`${{ inputs.test_suite }}\`
-          
-          - 🔄 [${{ matrix.workflow }}](https://github.com/NVIDIA-NeMo/Megatron-Bridge/actions/runs/${RUN_ID}) - Running...
-          - ⏳ Monitoring every 5 minutes until completion
-          
-          > **Note:** Tests run without approval when triggered from MCore
-          EOF
-      
-      - name: Monitor workflow
-        if: steps.should_run.outputs.run == 'true'
-        id: monitor
-        continue-on-error: true
-        env:
-          GH_TOKEN: ${{ secrets.PAT }}
-        run: |
-          RUN_ID="${{ steps.get_run_id.outputs.run_id }}"
-          echo "📊 Monitoring ${{ matrix.workflow }} (Run ID: ${RUN_ID})"
-          
-          gh run watch ${RUN_ID} --repo NVIDIA-NeMo/Megatron-Bridge --exit-status
-          
-          CONCLUSION=$(gh run view ${RUN_ID} --repo NVIDIA-NeMo/Megatron-Bridge --json conclusion --jq -r .conclusion)
-          echo "workflow_status=${CONCLUSION}" >> $GITHUB_ENV
-          echo "✅ Completed: ${CONCLUSION}"
-      
-      - name: Report results
-        if: always() && steps.should_run.outputs.run == 'true'
-        run: |
-          CONCLUSION="${{ env.workflow_status || 'unknown' }}"
-          RUN_ID="${{ steps.get_run_id.outputs.run_id }}"
-          
-          case "$CONCLUSION" in
-            "success") ICON="✅"; MSG="passed" ;;
-            "failure") ICON="❌"; MSG="failed"; EXIT_CODE=1 ;;
-            "cancelled") ICON="🚫"; MSG="cancelled"; EXIT_CODE=0 ;;
-            *) ICON="⏳"; MSG="still running or timed out"; EXIT_CODE=0 ;;
-          esac
-          
-          cat >> $GITHUB_STEP_SUMMARY << EOF
-          ## 📊 ${{ matrix.name }} Results
-          
-          ### ${ICON} ${{ matrix.workflow }}
-          **Status:** \`${CONCLUSION}\`
-          
-          [View full results →](https://github.com/NVIDIA-NeMo/Megatron-Bridge/actions/runs/${RUN_ID})
-          
-          ---
-          *Triggered from MCore \`${{ needs.get-mcore-info.outputs.short_sha }}\`*
-          EOF
-          
-          echo "${ICON} ${{ matrix.name }} ${MSG}"
-          exit ${EXIT_CODE:-0}
-
+          owner: NVIDIA-NeMo
+          repo: Megatron-Bridge
+          workflow_file_name: cicd-main.yml
+          github_token: ${{ secrets.PAT }}
+          ref: ${{ inputs.mbridge_ref }}
+          wait_interval: 60
+          propagate_failure: true
+          client_payload: |
+            {
+              "mcore_ref": "${{ github.sha }}",
+              "test_suite": "${{ inputs.test_suite }}",
+              "triggered_by": "https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}"
+            }
diff --git a/.gitlab/stages/00.pre.yml b/.gitlab/stages/00.pre.yml
index ff9e4e5178b..e00ce8afc36 100644
--- a/.gitlab/stages/00.pre.yml
+++ b/.gitlab/stages/00.pre.yml
@@ -39,6 +39,9 @@ pre:create_ci_branches:
       - branch: ci-approve-main
       - branch: ci-approve-dev
       - branch: ci-sync-branches
+      - branch: ci-testing-1
+      - branch: ci-testing-2
+      - branch: ci-testing-3
   tags:
     - arch/amd64
     - env/prod
@@ -49,7 +52,7 @@ pre:create_ci_branches:
   stage: .pre
   image: python:3.10
   variables:
-    GIT_STRATEGY: 'clone'
+    GIT_STRATEGY: "clone"
   script:
     - git remote set-url origin "https://gitlab-ci-token:${PROJECT_ACCESS_TOKEN_MCORE}@${GITLAB_ENDPOINT}/adlr/megatron-lm.git"
     - git switch --force-create $branch
@@ -80,7 +83,7 @@ pre:create_ci_branches_dev:
   stage: .pre
   image: python:3.10
   variables:
-    GIT_STRATEGY: 'clone'
+    GIT_STRATEGY: "clone"
   script:
     - git remote set-url origin "https://gitlab-ci-token:${PROJECT_ACCESS_TOKEN_MCORE}@${GITLAB_ENDPOINT}/adlr/megatron-lm.git"
     - git switch --force-create $branch
@@ -137,7 +140,7 @@ pre:maybe_cherry_pick_to_main:
   stage: .pre
   image: nentangso/alpine-git-curl-jq
   variables:
-    GIT_STRATEGY: 'clone'
+    GIT_STRATEGY: "clone"
   script:
     - |
       set -x
@@ -202,7 +205,7 @@ pre:maybe_cherry_pick_commit:
   stage: .pre
   image: nentangso/alpine-git-curl-jq
   variables:
-    GIT_STRATEGY: 'clone'
+    GIT_STRATEGY: "clone"
   script:
     - set -x
     - set +e
diff --git a/.gitlab/stages/01.build.yml b/.gitlab/stages/01.build.yml
index 20252e7d045..61521295a93 100644
--- a/.gitlab/stages/01.build.yml
+++ b/.gitlab/stages/01.build.yml
@@ -121,6 +121,7 @@ test:build_image:
     KUBERNETES_SERVICE_MEMORY_LIMIT: 90Gi
     SHARED_PATH: /builds/$CI_PROJECT_PATH/shared
   script:
+    - apk add skopeo
     - |
       set -x
 
@@ -132,6 +133,11 @@ test:build_image:
         ${IMAGE}:${CI_PIPELINE_ID}-arm64
 
       docker manifest push ${IMAGE}:${CI_PIPELINE_ID}
+
+      if [[ "$CI_COMMIT_BRANCH" == "ci-rebuild-mcore-nemo-image" || "$CI_COMMIT_BRANCH" == "main" || "$CI_COMMIT_BRANCH" == "dev" ]]; then
+        skopeo copy --all docker://${IMAGE}:${CI_PIPELINE_ID} docker://${IMAGE}:${CI_COMMIT_BRANCH}
+      fi
+
     - echo "MCORE_MR_COMMIT=$CI_COMMIT_SHA" | tee -a build.env
     - echo "MCORE_BACKWARDS_COMMIT=$MCORE_BACKWARDS_COMMIT" | tee -a build.env
     - cat build.env
diff --git a/.gitlab/stages/02.test.yml b/.gitlab/stages/02.test.yml
index 33dd8d7a5fb..a324ce037fb 100644
--- a/.gitlab/stages/02.test.yml
+++ b/.gitlab/stages/02.test.yml
@@ -211,31 +211,6 @@ test:unit_tests_notify:
       when: always
     - when: never
 
-test:linting_docs_build:
-  extends: [.test_rules]
-  image: ${UTILITY_IMAGE}:${CI_PIPELINE_ID}
-  tags:
-    - arch/amd64
-    - env/prod
-    - origin/jet-fleet
-    - owner/jet-core
-    - purpose/utility
-    - team/megatron
-  needs: [test:build_image]
-  script:
-    - cd ..
-    - rm -rf documentation && git clone https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/nemo-megatron-core-tme/documentation.git
-    - mv megatron-lm/ documentation/
-    - cd documentation/
-    - ./repo docs
-  rules:
-    - if: $PUBLISH == "yes"
-      when: never
-    - if: $BUILD == "no"
-      when: never
-    - when: on_success
-      allow_failure: true
-
 # Override from template
 secret_detection:
   rules:
diff --git a/.gitlab/stages/03.integration-tests.yml b/.gitlab/stages/03.integration-tests.yml
index d28ecd8e137..70fa345e513 100644
--- a/.gitlab/stages/03.integration-tests.yml
+++ b/.gitlab/stages/03.integration-tests.yml
@@ -141,18 +141,21 @@ integration:configure:
 
 integration:run_lts_dgx_a100:
   extends: [.integration_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: A100
 
 integration:run_lts_dgx_h100:
   extends: [.integration_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: H100
 
 integration:run_lts_dgx_gb200:
   extends: [.integration_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: GB200
diff --git a/.gitlab/stages/04.functional-tests.yml b/.gitlab/stages/04.functional-tests.yml
index d32ff86a344..002c96e7c0f 100644
--- a/.gitlab/stages/04.functional-tests.yml
+++ b/.gitlab/stages/04.functional-tests.yml
@@ -168,18 +168,21 @@ functional:configure:
 
 functional:run_lts_dgx_a100:
   extends: [.functional_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: A100
 
 functional:run_lts_dgx_h100:
   extends: [.functional_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: H100
 
 functional:run_lts_dgx_gb200:
   extends: [.functional_run]
+  allow_failure: true
   variables:
     ENVIRONMENT: lts
     CLUSTER: GB200
@@ -205,15 +208,15 @@ functional:run_dev_dgx_gb200:
 functional:run_nemo:
   extends: [.functional_tests_rules]
   trigger:
-    project: 'dl/joc/nemo-ci'
+    project: "dl/joc/nemo-ci"
     branch: main-mirror
     strategy: depend
   inherit:
     variables: true
   variables:
-    MCORE_MR_COMMIT: $CI_COMMIT_SHA
-    TEST_NEMO2_MODULE: 'True'
-    ALLOW_FAILURE_DEPENDENCY: 'True'
+    MCORE_COMMIT: $CI_COMMIT_SHA
+    TEST_NEMO2_MODULE: "True"
+    ALLOW_FAILURE_DEPENDENCY: "True"
     TESTS_TO_RUN_ON_THIS_COMMIT: nightly
   rules:
     - if: $FUNCTIONAL_TEST == "yes"
@@ -229,6 +232,8 @@ functional:x_notify:
     - functional:run_dev_dgx_a100
     - functional:run_lts_dgx_h100
     - functional:run_dev_dgx_h100
+    - functional:run_lts_dgx_gb200
+    - functional:run_dev_dgx_gb200
   tags:
     - arch/amd64
     - env/prod
diff --git a/CHANGELOG.md b/CHANGELOG.md
deleted file mode 100644
index babdc18b8a4..00000000000
--- a/CHANGELOG.md
+++ /dev/null
@@ -1,368 +0,0 @@
-# Changelog
-
-## NVIDIA Megatron Core 0.15.0
-
-* Features  
-  * Performance  
-    * Fused QKV preprocessing with precomputed RoPE caches (3x preprocessing speedup, 10-14% E2E) ([MR \!3912](https://github.com/NVIDIA/Megatron-LM/commit/f0d9fa97fead9825ae3eada36ee2df568bfa415b))  
-    * Use new TE interface for user buffers ([MR \!3886](https://github.com/NVIDIA/Megatron-LM/commit/d47b83807142b6490c7a000e63d25a479b106fd9))  
-    * Add CPU activation offloading via TE ([MR \!4286](https://github.com/NVIDIA/Megatron-LM/commit/310671436c36e6bd198e92c4f30bc84469cc31d8))  
-    * Add configurable double buffering ([MR \!4026](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4026))  
-    * Add Muon optimizer and distributed optimizer support ([MR \!4106](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4106))  
-    * Add setting to support Adam or AdamW optimizer ([MR \!3866](https://github.com/NVIDIA/Megatron-LM/commit/03fd0b41b3840c6f19558161d98373a9242402e5))  
-  * MoE  
-    * Add DTensor support for EP and DSv3 modules ([MR \!3955](https://github.com/NVIDIA/Megatron-LM/commit/268fda08592528b7bc1a21aadaed259980ca8efb))  
-    * Add HybridEP backend to Flex Dispatcher ([MR \!4237](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4237))  
-    * Support FP8 recomputation for MoE components ([MR \!4030](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4030))  
-    * Implement NVFP4 Zero Padding for MoE ([MR \!4225](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4225))  
-    * Compute shared experts before router ([MR \!4068](https://github.com/NVIDIA/Megatron-LM/commit/e8024d716f3036ebcef8c5254c7830ad09aaf41b))  
-    * Enable bias in expert MLP ([MR \!3858](https://github.com/NVIDIA/Megatron-LM/commit/a329dd6da586261a45a8f7d04c1e659ffedd80ae))  
-  * Model support  
-    * Add YaRN support for GPT-OSS ([MR \!4044](https://github.com/NVIDIA/Megatron-LM/commit/2c1b77a9984bfa978e7cf1f58522e5f8e045d017))  
-    * Add support for Qwen3-Next arguments ([MR \!4070](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4070))  
-    * Add FP8 init for MTP ([MR \!3958](https://github.com/NVIDIA/Megatron-LM/commit/d6c6e54ec5eb43d4e196c7ae84e0e88f28613e6b))  
-    * Add fp8\_dpa option for FP8 scaling ([MR \!4053](https://github.com/NVIDIA/Megatron-LM/commit/61047e60e617e71ebe120ec293b62df6b0efc84f))  
-    * Add RADIO-g support to converter and tester ([MR \!4371](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4371))  
-    * Add audio semantic reasoning data for voice chat and speech instructions ([MR \!4397](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4397))  
-  * FSDP  
-    * Enable joint training of parallel modules ([MR \!3850](https://github.com/NVIDIA/Megatron-LM/commit/53008b844f98886a2144c216ecd25952cb2dda58))  
-    * Add support for multimodule communication ([MR \!4235](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4235))  
-  * Inference  
-    * Add CUDA Graph runner lookup table cache (up to 2x E2E speedup) ([MR \!4082](https://github.com/NVIDIA/Megatron-LM/commit/ab43252fdbedcc3662014ae0e110bd3278d844f4))  
-    * Add MoE dropping and padding router for CUDA Graph \+ decode ([MR \!3816](https://github.com/NVIDIA/Megatron-LM/commit/56818f9e5090ff9eb0f13f10bfe408aae4031c5c))  
-    * Dynamic audio shapes with variable sequence lengths (2.5x throughput improvement) ([MR \!4274](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4274))  
-    * Integrate unified memory for dynamic inference context ([MR \!3985](https://github.com/NVIDIA/Megatron-LM/commit/ef4ae4528a0924159069b9f3a2719616156bafa2))  
-  * Post-training  
-    * Add GPT-OSS ModelOpt support with quantization, import/export ([MR \!4169](https://github.com/NVIDIA/Megatron-LM/commit/a2d8c806b35bc708b13e6c069e19e5dfb49b8481))  
-    * Enable KD support with hybrid training loop ([MR \!4021](https://github.com/NVIDIA/Megatron-LM/commit/48d7275062a8307f82bd0fa6c1504032c7f3af96))  
-    * Add ModelOpt pruning example ([MR \!4022](https://github.com/NVIDIA/Megatron-LM/commit/5a58976ebe007064c2ff5e76e815aa5fcf1a8787))  
-  * RL  
-    * Add importance sampling and partial rollouts to Megatron RL ([MR \!4000](https://github.com/NVIDIA/Megatron-LM/commit/8399280ed3b72a183f44820896a67392c0a47e3e))  
-    * Add sequence packing for RL ([MR \!4191](https://github.com/NVIDIA/Megatron-LM/commit/ee8e9307f3ad655e6a46f98a483d8192995b02c2))  
-  * Ease of use  
-    * Handle CUDA absence during import ([MR \!4120](https://github.com/NVIDIA/Megatron-LM/commit/ae44e49271dc45b51a7400ecf6debc598ba90b54))  
-    * Add granary dataloader functionality ([MR \!4291](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4291))  
-    * Enable SWA mixing with attention ([MR \!3855](https://github.com/NVIDIA/Megatron-LM/commit/e5bc9249d7ad34355f5db4c8ff7d7a9080f94dc2))  
-* Bug fixes  
-  * Fix convergence bug in MXFP8 parameter gradient buffer reuse ([MR \!3999](https://github.com/NVIDIA/Megatron-LM/commit/c2c36f77cf7a0476daee5bb2dec604c2764de320))  
-  * Fix loss mask cloning to prevent incorrect updates ([MR \!4164](https://github.com/NVIDIA/Megatron-LM/commit/c94d58f3260aa568588265e07b3c06bb58cbde41))  
-  * Fix metadata loss in checkpoints ([MR \!4182](https://github.com/NVIDIA/Megatron-LM/commit/d8c6aa4c0b5d4c15ec1196802bce292d4580ed4a))  
-  * Fix FSDP grad accum fusion support ([MR \!4018](https://github.com/NVIDIA/Megatron-LM/commit/9f72f4775509668173c75eaab5d58a49f4473748))  
-  * Fix non-TE optimizer checkpoint issue ([MR \!3931](https://github.com/NVIDIA/Megatron-LM/commit/2ebb6ee95af8b547e3c0ac394d494cb189b890bc))  
-  * Fix BERT virtual pipeline parallelism ([MR \!3993](https://github.com/NVIDIA/Megatron-LM/commit/18420b63408101fe5a49d125fb29625f1ad6ab26))  
-  * Fix gc.freeze() slowdown by adding gc.collect() on last layer ([MR \!4003](https://github.com/NVIDIA/Megatron-LM/commit/a3f9e566c9595753553a73d403b2a481ad283fc0))  
-  * Fix full iteration CUDA graph non-tensor handling ([MR \!4019](https://github.com/NVIDIA/Megatron-LM/commit/8479eb35fbca9631acb846c3ad5d868e02214227))  
-  * Fix model\_auto\_sync mis-set and add gradient assertion ([MR \!4062](https://github.com/NVIDIA/Megatron-LM/commit/03045f2d880813695f75707e3262a2bfb4206dfe))  
-  * Fix HF import dtype and checkpoint loading issues ([MR \!4095](https://github.com/NVIDIA/Megatron-LM/commit/435e7e0620ff870d99debd73b3c9113226622dde))  
-  * Fix missing initialization in ProcessGroupCollection ([MR \!4159](https://github.com/NVIDIA/Megatron-LM/commit/5f2becf232a85df8687dc539e604e00a6a875da1))  
-  * Fix sink attention TP ([MR \!4173](https://github.com/NVIDIA/Megatron-LM/commit/3b1b9b267193d72d4f8dc710561c2368de8c114c))  
-  * Fix num\_microbatches calculation ([MR \!4199](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4199))  
-  * Fix 1f1b overlap unit tests for MTP standalone ([MR \!4210](https://github.com/NVIDIA/Megatron-LM/commit/44bc753d69cf509c158bb261434498b141fe5130))  
-  * Fix stale state dict handling ([MR \!4226](https://github.com/NVIDIA/Megatron-LM/commit/0ba847081113a92ce01084f33cd4a0c1f31b327b))  
-  * Fix dataset divergence with tokenizer PAD handling ([MR \!4231](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4231))  
-  * Fix parameter initialization ([MR \!4296](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4296))  
-  * Ensure tensor-parallel attributes set regardless of initialization flag ([MR \!4312](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/4312))  
-* Known issues
-
-## NVIDIA Megatron Core 0.14.0
-
-* Features  
-  * Inference  
-    * Add async support for DynamicInferenceEngine ([MR \!3187](https://github.com/NVIDIA/Megatron-LM/commit/05079d55a5bfcc7a43f4619e36a40a9e8db3f882))  
-    * Pad input tensors and enable FP8 weights for FP8 inference ([MR \!3341](https://github.com/NVIDIA/Megatron-LM/commit/6a6cd478839d90cf09a837adf8c79cbc844bc920))  
-    * Force inference to always gather logits with tensor parallelism ([MR \!3442](https://github.com/NVIDIA/Megatron-LM/commit/7c9cdcb794089968278c7272e0261a68edf5d369))  
-    * Multi batch size CUDA Graphs for Dynamic Inference ([MR \!3402](https://github.com/NVIDIA/Megatron-LM/commit/30aabe5e3133c6d70aa55aaabad4ea8cb04ce63c))  
-  * Post-training  
-    * ModelOpt updates ([MR \!3268](https://github.com/NVIDIA/Megatron-LM/commit/550ed5243c3a18e39430c15e8918ee63e41d7eaf))  
-      * Add speculative decoding AR validation feature  
-      * Add DeepSeek and Qwen model configs  
-  * Performance  
-    * ModelCommProcessGroup integration ([MR \!3391](https://github.com/NVIDIA/Megatron-LM/commit/26adc2dfde53fbc2b063e2fdd1d9ed26578811a6))  
-    * Add HyperCommGrid: N-Dimensional Communication Grid for Model Parallelism ([MR \!3398](https://github.com/NVIDIA/Megatron-LM/commit/45400df7da7fa23e3aff86804e5ac254d9a8d3c0))  
-      * Flexible creation and management of communication groups  
-    * Add support for Spike No More embedding initializations and weight decay skipping ([MR \!3500](https://github.com/NVIDIA/Megatron-LM/commit/ee74aa66a06b24e511270f285db475941ef63bfd))  
-  * MoE  
-    * We're actively optimizing large-scale fine-grained MoE performance on Blackwell Platform.  
-    * Features:  
-      * Support Expert Parallel A2A Overlapping ([MR \!3470](https://github.com/NVIDIA/Megatron-LM/commit/0c6c1176fb3e3e00534b3591f1ad023d4ecad6fb); [MR \!3074](https://github.com/NVIDIA/Megatron-LM/commit/4b30ec54aba97e16a083eca33d2df1dd48e1b48f))  
-      * Support CP and recompute for MTP ([MR \!3330](https://github.com/NVIDIA/Megatron-LM/commit/650ab87d04105869f197f2ddc441e3b18ca93724))  
-      * Add support for global aux loss ([MR \!3318](https://github.com/NVIDIA/Megatron-LM/commit/e58d9080ea212e005ccba0b6607bfcc86451285d))  
-    * Memory Optimization  
-      * Support recomputation for FP8 layernorm/moe\_act/shared\_experts ([MR \!3465](https://github.com/NVIDIA/Megatron-LM/commit/6850cc6a739d168f8c84db6cdacf4fe2931c0c49))  
-      * Support optimizer offloading for DSV3 FP8 training  ([MR \!3659](https://github.com/NVIDIA/Megatron-LM/commit/abbde02f54b62a5194ebe951218e98feceba6d42))  
-    * Performance Optimization  
-      * Add MoE router fusion ([MR \!3809](https://github.com/NVIDIA/Megatron-LM/commit/d93743a9f11d5d17824b8b49868cc90f2904896f))  
-      * Updates for MoE cudagraph ([MR \!3631](https://github.com/NVIDIA/Megatron-LM/commit/95452706d7aa16dc174813e12639a8c8356fbe87))  
-    * Bug fixes:  
-      * Fix router input jitter dtype ([MR \!3774](https://github.com/NVIDIA/Megatron-LM/commit/20b395424d2e2bbfaab57b2f954294eb57c90c82))
-  * Model support  
-    * Add MiMo video VLM train example ([MR \!3543](https://github.com/NVIDIA/Megatron-LM/commit/786f5629d3462aff2f8855f51db70e882c475116))  
-    * Add AVLM for MIMO ([MR \!3624](https://github.com/NVIDIA/Megatron-LM/commit/db41707430bff743f986b5779712c74242b99caa))  
-  * Ease of use  
-    * Add uv support for source installs ([MR \!3615](https://github.com/NVIDIA/Megatron-LM/commit/164204cd7216e642bdef7299c569d95f02f9a79e))  
-    * Automated weekly prereleases ([MR \!3574](https://github.com/NVIDIA/Megatron-LM/commit/7e59266c70ef34a246438640af690b55c7ecac28))  
-* Bug fixes  
-  * Use mscale\_all\_dim for softmax\_factor ([MR \!2800](https://github.com/NVIDIA/Megatron-LM/commit/e96a358f60c82b8ac8d965d91c3cc4ad0230a4e0))  
-  * Fix FP8 param blockwise scaling unit test ([MR \!3480](https://github.com/NVIDIA/Megatron-LM/commit/57082f946a04c3390fcfc43634dc546ec3ded033))  
-  * Fix unit test blockwise scaling ([MR \!3491](https://github.com/NVIDIA/Megatron-LM/commit/6d95fe63658f967e56a3fda88a9c30a424fcb520))  
-  * Optimize prefill for token-less requests ([MR \!3499](https://github.com/NVIDIA/Megatron-LM/commit/daaa650a9ac4291d4027ca2fdeb4298ce024efd2))  
-  * Add default values for Fp8Padding and Fp8Unpadding ([MR \!3501](https://github.com/NVIDIA/Megatron-LM/commit/42b2b1d10a9cb699b7e5aa40f6bfba9c2a1348aa))  
-  * Fix CUDA graph logic for flexible pp layout ([MR \!3505](https://github.com/NVIDIA/Megatron-LM/commit/020d85e50ddf0f0282802002acb3662129a519c5))  
-  * Load FP8 models with strict=False ([MR \!3508](https://github.com/NVIDIA/Megatron-LM/commit/1ab876ddc4c1893c76f26d775226a8d1dcdfb3d2))  
-  * Skip rope check for torch \< 1.4.0 ([MR \!3528](https://github.com/NVIDIA/Megatron-LM/commit/d8180ef8ed0bb6f305dcdedf1b27d91304f361a3))  
-  * Disable Apex tests for stability ([MR \!3539](https://github.com/NVIDIA/Megatron-LM/commit/d1256277fe378add0a2cfd7251f5a350b6d126ec))  
-  * Fix typo in parallel\_state expert parallelism ([MR \!3548](https://github.com/NVIDIA/Megatron-LM/commit/5783ff32af759b8102cf0cb0bb82b30c48b9da26))  
-  * Guard modelopt on macOS ([MR \!3549](https://github.com/NVIDIA/Megatron-LM/commit/76144fe1106e4fb0e69aa75b7a6ab66e71e8f37f))  
-  * Retry on CUDA function failure ([MR \!3554](https://github.com/NVIDIA/Megatron-LM/commit/809aab68307a64c1386d68cc78ef70f8f4e12a80))  
-  * Fix NCCL mem pool creation error ([MR \!3557](https://github.com/NVIDIA/Megatron-LM/commit/b61e21153146a563309b5d44cb5d7f7425806072))  
-  * Fix get\_rotary\_seq\_len return type ([MR \!3559](https://github.com/NVIDIA/Megatron-LM/commit/1fa6bc83c7aeae95abc8e86ff0aac596985a01c3))  
-  * Retry on CUDA function failure ([MR \!3560](https://github.com/NVIDIA/Megatron-LM/commit/7da88d74865c3f1a59894173246f26e7b3bf91b9))  
-  * Fix NCCL allocator attribute error ([MR \!3565](https://github.com/NVIDIA/Megatron-LM/commit/6b656114795d74c3353cb007c59af49b1752f447))  
-  * Ensure multi-prompt inference works ([MR \!3568](https://github.com/NVIDIA/Megatron-LM/commit/0fae48931000c9c7af06f7dcf037b5b7d96e0cd6))  
-  * Fix MD5 on FIPS systems ([MR \!3577](https://github.com/NVIDIA/Megatron-LM/commit/83ee8c2848a3b1d42b40086a64da11e19f4b191f))  
-  * Fixes dynamic context and inference bugs ([MR \!3582](https://github.com/NVIDIA/Megatron-LM/commit/e9c1da60a1ccc85376666d58568ed1d3e5a4f9db))  
-  * Fix TE version for interleaved fused RoPE ([MR \!3586](https://github.com/NVIDIA/Megatron-LM/commit/b72b6cc161f5273b545bca09677382917cf20492))  
-  * Fix MTP with MoE and TP logging ([MR \!3594](https://github.com/NVIDIA/Megatron-LM/commit/9af96623b66693e058f6bfce8d0094dc976792d8))  
-  * Guard TE import fix ([MR \!3596](https://github.com/NVIDIA/Megatron-LM/commit/1bf946b1ec3f11e71459c7c0d06a97edbed96a1a))  
-  * Add assertion for NCCL UB case ([MR \!3599](https://github.com/NVIDIA/Megatron-LM/commit/e11d28592f19c122859be764b7afe7c208d9acc1))  
-  * Remove Encoder PP related Functions ([MR \!3604](https://github.com/NVIDIA/Megatron-LM/commit/9e49aa4446a58cc21c4dc0c5d0806551ad075ca7))  
-  * Fix segfaults in tests ([MR \!3605](https://github.com/NVIDIA/Megatron-LM/commit/f6492fe8164fd5b9ad55007d435ccfc66cb98cc7))  
-  * Fix TE error in distributed optimizer ([MR \!3625](https://github.com/NVIDIA/Megatron-LM/commit/e6c510ff3c1159f8955589b26f7c395bdf0607d9))  
-  * Remove redundant barrier in checkpoint flow ([MR \!3626](https://github.com/NVIDIA/Megatron-LM/commit/26869feb6a3ac7f5616cb7253c37a4244d107d70))  
-  * Support VPP MTP, fix logging ([MR \!3630](https://github.com/NVIDIA/Megatron-LM/commit/c351a473c7eedac2c43eab0815afb9759f4f8187))  
-  * Retry mechanism for free(): invalid pointer errors ([MR \!3632](https://github.com/NVIDIA/Megatron-LM/commit/ec35b41b2df145a7ccb84afc48d94e0786e094da))  
-  * Fix test\_replication.py issues ([MR \!3633](https://github.com/NVIDIA/Megatron-LM/commit/f7b50b271b2e0e396069e02551b21aa6fb374b43))  
-  * Fix typo in parallel\_state ([MR \!3634](https://github.com/NVIDIA/Megatron-LM/commit/3c79a2c330290df58804c33e28e7c197fcc1f0b9))  
-  * Fix CUDA graph logic determination ([MR \!3635](https://github.com/NVIDIA/Megatron-LM/commit/90efa3ef8a3c4f9e0f1db9f67ab9348bfa501387))  
-  * Fix TE installation error ([MR \!3636](https://github.com/NVIDIA/Megatron-LM/commit/7e7322c01c9cb8ec254ecd9042700b22b70fe5c8))  
-  * Ensure correct sharding type in local tests ([MR \!3643](https://github.com/NVIDIA/Megatron-LM/commit/946357f8dd7fdc12424b3a66bc999e6c0a02696c))  
-  * Fix cudagraphed backward buffer reuse for last layer ([MR \!3645](https://github.com/NVIDIA/Megatron-LM/commit/ee61cf450d24760952e8995aab045ab6d55b986e))  
-  * Set default for packed\_seq\_params in get\_rotary\_seq\_len ([MR \!3651](https://github.com/NVIDIA/Megatron-LM/commit/510d58c46664f44c556005ac928c5c531e12f761))  
-  * Fix dynamic example script errors ([MR \!3653](https://github.com/NVIDIA/Megatron-LM/commit/72e290bf1f4bbf0c8047bb10a51da6ea6372e163))  
-  * Guard TE import fix ([MR \!3666](https://github.com/NVIDIA/Megatron-LM/commit/ac198fc0d60a8c748597e01ca4c6887d3a7bcf3d))  
-* Breaking changes:  
-  * `megatron.core.distributed.custom_fsdp` refactored as breaking change to `megatron.core.distributed.fsdp.src.megatron_fsdp`  
-* Known issues
-
-## NVIDIA Megatron Core 0.13.0
-
-* Support bf16 dtype for optimizer states to use precision-aware optimizer in TransformerEngine  
-* MoE
-  * Features:  
-    * Flexible Asymmetric Virtual Pipeline Parallelism with Custom Pipeline Layout (--pipeline-model-parallel-layout)  
-    * Add support to pass custom parallelism groups to MoE modules.  
-    * Add Hybrid Shard Data-Parallel support for MoE models (--num-distributed-optimizer-instances)  
-    * Support EP \+ custom FSDP training for DeepSeek-V3  
-    * FP8 support for Multi-Token-Prediction  
-  * Memory Optimization  
-    * Fine-grained recomputation to reduce activation memory. (--recompute-modules with \--recompute-granularity selective)  
-    * Memory efficient token permutation by moving the probs multiplication from unpermutation to activation function of GroupedMLP.  
-  * Performance Optimization  
-    * MLA RoPE fusion kernel and YARN embedding cache.  
-    * FP8 padding optimization of MoE models by padding the routing map.  
-  * Bug fixes:  
-    * Fix the aux loss calculation when expert\_bias or group limited routing is used. This leads to load\_balancing\_loss values change compared to the previous version.  
-    * Fix packed sequence support for MLA  
-  * Known Issues:  
-    * MTP is not compatible with flexible pipeline layout, will be fixed at \!3594.  
-    * MTP convergence issue with TP2, will be fixed at \!3594.
-
-## NVIDIA Megatron Core 0.12.0
-
-* Add FP8 recipe selection to arguments (--fp8-recipe, --first-last-layers-bf16, --num-layers-at-start-in-bf16, --num-layers-at-end-in-bf16)
-* Context parallel: fix loss scaling when calculate_per_token_loss=True
-* Make the number of data parallel communication buckets configurable (--ddp-num-buckets, --ddp-pad-buckets-for-high-nccl-busbw)
-* Inference
-  * Support in-flight batching and chunked KV cache
-  * Reduce memory usage,
-    * by not materializing full attention mask
-    * by only materializing logits for the last token during decode
-    * by removing an obsolete tensor reference
-* Hybrid Model
-  * Inference
-    * Add CUDA graph support
-    * Change tools/run_mamba_text_generation_server.py to use megatron.core.inference
-    * Fix a shape issue when materializing logits for Mamba model
-  * Improve initialization of Mamba layers
-  * Add configuration switches (--mamba-state-dim, --mamba-head-dim, --mamba-num-groups, --is-hybrid-model)
-  * Make num_floating_point_operations work with hybrid model
-  * Make hybrid_conversion.py work with mixer that uses TE linear
-  * Add FP8 support
-  * Fix Mamba dt_bias tensor parallelism
-  * Support multimodal tokenizer
-  * Improve data parallelism scaling
-* MoE
-  * Features:
-    * DeepEP support, compatible with all the parallelisms and token drop / dropless
-    * Important precision improvement: Enable FP32/FP64 routing and unpermutation using –moe-router-dtype. FP32 is recommended for all fine-grained MoE training
-    * CUDA Graph support for MoE
-    * Multi-Token Prediction (MTP) Support
-    * Fused indices_to_multihot kernel for DeepEP dispatcher
-  * Bug fixes:
-    * Fix Hang Issue with MoE+Dense Hybrid models
-    * Update theoretical memory and tflops estimation for MoE and MLA
-    * Fix MoE Aux loss scaling for per token loss
-    * Fixes for group limited routing and expert bias. We verified these fixes through dsv3 e2e verifications
-  * Known issues:
-    * The ckpt trained with Custom FSDP for MoE may not be compatible with 3D parallel training.
-
-## NVIDIA Megatron Core 0.11.0
-
-* Add multi datacenter training support though N/S connection
-* MoE
-  * Features
-    * Support DeepSeek-V3 fine-tuning
-      * Aux-loss-free load balancing strategy
-      * Node-limited routing and Device-limited routing support.
-      * Tensor Parallelism support for MLA and Sequence Auxiliary Loss
-      * MTP (with TP and PP support) is coming soon.
-    * Permutation / Unpermutation fusion kernel from TransformerEngine.
-    * Uneven virtual pipeline parallel split support in first and last PP stage.
-  * Bug fixes:
-    * Fix the grad scale when TP != expert-TP and average_in_collective is enabled in DDP.
-    * Fix TEGroupedMLP distckpt compatibility issue with FP8 padding/unpadding.
-  * Known Issues:
-    * When training the Dense+MoE hybrid model, the process will hang if any PP rank does not have expert params.
-* Add MX-FP16 support for optimizer and master weights
-* CUDA Graph memory optimizations
-* Enable UCC backend for PP communication
-* Optimizer CPU offload support for memory savings
-* Models
-  * Initial RADIO/CRADIO implementation
-  * llama3.2 support
-* Hybrid Model
-  * Support quantization via TensorRT Model Optimizer
-
-## NVIDIA Megatron Core 0.10.0
-
-* Adding MLA to MCore
-* Enable FP8 for GroupedMLP
-* MoE Parallel Folding
-* Enhance MoE Architecture: Support MoE Layer Frequency Patterns and Configurable MoE FFN Hidden Size
-* Multimodal: NVLM training and evaluation support in MCore
-* Mamba Hybrid
-  * Increase performance and reduce memory footprint of Triton language/compiler distributed caching
-  * Add more unit testing and fix bugs
-
-## NVIDIA Megatron Core 0.9.0
-
-* Uneven pipeline parallelism
-  * Enable pipeline parallelism where first and last ranks have fewer transformer layers than the intermediate ranks
-* Per layer CUDAGraph support for GPT training with Transformer Engine modules
-* Enable different TP sizes for the vision encoder
-* Enable pipeline parallelism for T5 & Llava models
-* Support multi-tile multi-image input in Llava models
-* MoE
-  * FP8 support
-  * Runtime upcycling support
-  * Dispatcher implementation optimizations
-  * Shared expert support with overlapping optimizations
-    * Qwen Model support
-* Known Issues
-  * When using sequence parallel, during the transformer block forward pass, dropout is not using the appropriate rng context.
-* NVRx / Fault tolerance
-  * fault and hang detection in addition to existing straggler detection
-  * graceful exit and auto restart
-
-## NVIDIA Megatron Core 0.8.0
-
-* Multimodal
-  * Added initial support for training vision language models using the LLaVA architecture
-  * Added initial support for inference with multimodal inputs
-  * End-to-end multimodal example from data collection to training to evaluation is provided in examples/multimodal
-* MoE
-  * Context Parallel support.
-  * Distributed checkpoint support for grouped GEMM.
-* Mamba
-
-## NVIDIA Megatron Core 0.7.0
-
-* MoE
-  * Token drop support
-  * Several efficiency optimizations
-  * Improved model parallelism
-  * Memory optimizations
-* Distributed checkpointing
-  * Enabled for Retro
-  * Asynchronous checkpoint saving
-* Several minor bug fixes, speed improvements, and memory optimizations
-
-## NVIDIA Megatron Core 0.6.0
-
-* MoE (Mixture of Experts)
-  * Performance optimization
-    * Communication optimization for multi GPU and Single GPU
-    * 23% improvement (323 TFLOPS/GPU) over MCore 0.5.0 on Mixtral with Hopper BF16
-    * GroupedMLP enhancement for Hopper
-    * DP Overlapping. Support overlapping computation with gradient reduction and parameter gathering.
-  * All-to-All based Token Dispatcher
-  * Layer-wise logging for load balancing loss.
-  * Improved expert parallel support including distributed optimizer.
-* Distributed optimizer
-* RETRO
-  * Data processing
-* BERT
-  * Distributed checkpointing
-* Dist checkpointing
-  * PyTorch native distributed backend
-  * Improved saving/loading speed
-* TensorRT-LLM Export
-  * Integration with TensorRT Model Optimizer Post-training quantization (PTQ)
-  * Text generation driver to perform PTQ in Megatron-LM
-  * Llama2 and Nemotron3-8b examples to use TensorRT-LLM unified build API to build engine after training.
-* Several minor enhancements, bug fixes, and documentation updates
-
-## NVIDIA Megatron Core 0.5.0
-
-### Key Features and Enhancements
-
-Megatron core documentation is now [live!](https://docs.nvidia.com/megatron-core/developer-guide/latest/user-guide/index.html#quick-start)
-
-### Model Features
-
-* MoE (Mixture of Experts)
-  * Support for Z-loss, Load balancing and Sinkhorn
-  * Layer and communications refactor
-  * Richer parallelism mappings and EP can be combined with other model parallel techniques for larger MoE variants, e.g. EP + TP + DP + SP + PP
-  * Token dropless architecture with Top-K routing
-  * Performance optimization with with GroupedGEMM when number of local experts is > 1
-  * Distributed checkpointing
-* Interleaved rotary embedding
-
-### Datasets
-
-* Masked WordPiece datasets for BERT and T5
-* Raw and mock datasets
-
-### Parallelism
-
-### Performance
-
-* Activation offloading to CPU
-* Rope and Swiglu fusion
-* Sliding window attention (via Transformer Engine)
-
-### General Improvements
-
-* Timers
-
-## NVIDIA Megatron Core 0.4.0
-
-### Key Features and Enhancements
-
-#### Models
-
-* BERT
-* RETRO
-* T5
-
-#### Parallelism
-
-* Mixture of Experts support for GPT
-* Model parallel efficient Distributed Data Parallel (DDP)
-* Context Parallel (2D Tensor Parallel) support
-
-#### Datasets
-
-* GPT Dataset
-* Blended Dataset
diff --git a/docs/add_copyright_header.py b/docs/add_copyright_header.py
new file mode 100644
index 00000000000..9bc4481c506
--- /dev/null
+++ b/docs/add_copyright_header.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+#!/usr/bin/env python3
+"""One-off script to add NVIDIA copyright header to all .md files under docs/."""
+
+from pathlib import Path
+
+HEADER = """   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+"""
+
+def main():
+    docs_dir = Path(__file__).resolve().parent
+    already_has = "Copyright (c) 2022-2026, NVIDIA CORPORATION"
+    count = 0
+    for path in sorted(docs_dir.rglob("*.md")):
+        content = path.read_text(encoding="utf-8")
+        if content.strip().startswith(already_has):
+            continue
+        new_content = HEADER + content
+        path.write_text(new_content, encoding="utf-8")
+        count += 1
+        print(path.relative_to(docs_dir))
+    print(f"\nUpdated {count} files.")
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/advanced/index.md b/docs/advanced/index.md
index 573cb0ee81a..98ff0806cff 100644
--- a/docs/advanced/index.md
+++ b/docs/advanced/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Discussions
 
 In-depth technical discussions and optimization guides:
diff --git a/docs/api-backwards-compatibility-check.md b/docs/api-backwards-compatibility-check.md
index a417abfc2df..e1b6939b06f 100644
--- a/docs/api-backwards-compatibility-check.md
+++ b/docs/api-backwards-compatibility-check.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # API Backward Compatibility Checking
 
 ## Overview
diff --git a/docs/api-guide/core/datasets.md b/docs/api-guide/core/datasets.md
index e97e99ae1db..d80c0183375 100644
--- a/docs/api-guide/core/datasets.md
+++ b/docs/api-guide/core/datasets.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # datasets package
 
 ```{include} ../../../megatron/core/datasets/readme.md
diff --git a/docs/api-guide/core/dist_checkpointing.md b/docs/api-guide/core/dist_checkpointing.md
index 959aa4b07e0..ee0e5562ef3 100644
--- a/docs/api-guide/core/dist_checkpointing.md
+++ b/docs/api-guide/core/dist_checkpointing.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # dist_checkpointing package
 
 A library for saving and loading the distributed checkpoints.
@@ -32,19 +41,62 @@ import torch, argparse
 torch.serialization.add_safe_globals([argparse.Namespace])
 ```
 
-Checkpointing Distributed Optimizer
------------------------------------
+## Checkpointing Distributed Optimizer
 
-Checkpoint Compatibility and Optimizer State Formats
-####################################################
+### Checkpoint Compatibility and Optimizer State Formats
 
 Beginning with **mcore v0.14**, the ``flattened_range`` attribute was removed from ``dist_checkpointing``. As a result:
 
-- Optimizer states saved with mcore versions < 0.14 are no longer loadable. Loading these legacy optimizer states is not supported because the required sharded metadata is no longer available.
-- Model weights from older checkpoints remain fully compatible. No additional work is required—model weights from checkpoints produced by earlier versions are loaded automatically.
+- Optimizer states saved with mcore versions <= 0.14 can no longer be loaded directly. Loading these legacy optimizer states is not supported because the required sharded metadata is no longer available. If you need to continue training from older checkpoints, refer to the workaround described below.
+- Model weights from older checkpoints remain fully compatible. No extra steps are needed—model weights from checkpoints created by earlier versions load automatically; simply add the ``--no-load-optim`` flag.
+
+### Workaround: Loading legacy optimizer states with ToT MCore
+
+**Step 1: Convert the legacy checkpoint using mcore v0.15.0**
+
+Run a dummy training job with mcore v0.15.0 to re-save the checkpoint with new optimizer states format.
+
+```bash
+MODEL_TRAIN_PARAMS=(
+    # Define model architecture and training parameters here
+)
+OLD_CKPT=/workspace/mcore_ckpt_old
+CONVERTED_CKPT=/workspace/mcore_ckpt_0.15.0
+
+torchrun --nproc_per_node=8 /opt/megatron-lm/pretrain_gpt.py \
+   --save-interval 1 \
+   --eval-interval 1 \
+   --exit-interval 1 \
+   --eval-iters 1 \
+   --use-distributed-optimizer \
+   --save ${CONVERTED_CKPT} \
+   --load ${OLD_CKPT} \
+   --ckpt-format torch_dist \
+   "${MODEL_TRAIN_PARAMS[@]}"
+```
+
+**Step 2: Load the converted checkpoint with ToT MCore**
+
+Use the converted checkpoint as the input for continued training with ToT MCore.
+
+```bash
+MODEL_TRAIN_PARAMS=(
+    # Define model architecture and training parameters here
+)
+NEW_CKPT=/workspace/mcore_ckpt_new
+CONVERTED_CKPT=/workspace/mcore_ckpt_0.15.0
+
+torchrun --nproc_per_node=8 /opt/megatron-lm/pretrain_gpt.py \
+   --use-distributed-optimizer \
+   --save ${NEW_CKPT} \
+   --load ${CONVERTED_CKPT} \
+   --ckpt-format torch_dist \
+   "${MODEL_TRAIN_PARAMS[@]}"
+```
+
+After this step, training can proceed normally using ToT MCore with fully supported optimizer state loading.
 
-Distributed Optimizer Checkpoint Formats
-########################################
+## Distributed Optimizer Checkpoint Formats
 
 The refactor of the Distributed Optimizer introduces **two checkpoint formats**:
 
@@ -57,8 +109,7 @@ The refactor of the Distributed Optimizer introduces **two checkpoint formats**:
    - Slower than dp_reshardable.
    - Enabled via the ``--dist-ckpt-optim-fully-reshardable`` flag.
 
-Workflow for Changing Model Parallelism
-#######################################
+### Workflow for Changing Model Parallelism
 
 You can combine formats to optimize both flexibility and performance:
 
diff --git a/docs/api-guide/core/dist_checkpointing.strategies.md b/docs/api-guide/core/dist_checkpointing.strategies.md
index 7aab8609504..22fe3517a54 100644
--- a/docs/api-guide/core/dist_checkpointing.strategies.md
+++ b/docs/api-guide/core/dist_checkpointing.strategies.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # dist_checkpointing.strategies package
 
 Package defining different checkpoint formats (backends) and saving/loading algorithms (strategies).
diff --git a/docs/api-guide/core/distributed.md b/docs/api-guide/core/distributed.md
index 1921c0bdd57..13da4285ec5 100644
--- a/docs/api-guide/core/distributed.md
+++ b/docs/api-guide/core/distributed.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # distributed package
 
 This package contains various utilities to finalize model weight gradients
diff --git a/docs/api-guide/core/fusions.md b/docs/api-guide/core/fusions.md
index 396280ad7da..fdd358e813c 100644
--- a/docs/api-guide/core/fusions.md
+++ b/docs/api-guide/core/fusions.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # fusions package
 
 This package provides modules that provide commonly fused
diff --git a/docs/api-guide/core/index.md b/docs/api-guide/core/index.md
index 150fd72cb1e..0d39e46e744 100644
--- a/docs/api-guide/core/index.md
+++ b/docs/api-guide/core/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Core APIs
 
 Low-level API reference for core Megatron components.
diff --git a/docs/api-guide/core/pipeline_parallel.md b/docs/api-guide/core/pipeline_parallel.md
index 42fac8cc449..35f3c5b5cc2 100644
--- a/docs/api-guide/core/pipeline_parallel.md
+++ b/docs/api-guide/core/pipeline_parallel.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # pipeline_parallel package
 
 This package contains implementations for two different pipeline parallelism
diff --git a/docs/api-guide/core/tensor_parallel.md b/docs/api-guide/core/tensor_parallel.md
index 33a9160c82b..2d41c5f4467 100644
--- a/docs/api-guide/core/tensor_parallel.md
+++ b/docs/api-guide/core/tensor_parallel.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # tensor_parallel package
 
 This package contains an implementation for tensor parallelism in transformer
diff --git a/docs/api-guide/core/transformer.md b/docs/api-guide/core/transformer.md
index d004381844b..622fb006f99 100644
--- a/docs/api-guide/core/transformer.md
+++ b/docs/api-guide/core/transformer.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # transformer package
 
 The `transformer` package provides a customizable and configurable
diff --git a/docs/api-guide/index.md b/docs/api-guide/index.md
index 851114d98e8..7afa2450dd0 100644
--- a/docs/api-guide/index.md
+++ b/docs/api-guide/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # API Guide
 
 API reference documentation for Megatron Core components.
diff --git a/docs/api-guide/internal/index.md b/docs/api-guide/internal/index.md
index c216a976c77..312081ce70b 100644
--- a/docs/api-guide/internal/index.md
+++ b/docs/api-guide/internal/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Internal Utilities
 
 Internal utility APIs.
diff --git a/docs/api-guide/internal/num_microbatches_calculator.md b/docs/api-guide/internal/num_microbatches_calculator.md
index 470c9e49128..0c223588ce5 100644
--- a/docs/api-guide/internal/num_microbatches_calculator.md
+++ b/docs/api-guide/internal/num_microbatches_calculator.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Microbatches Calculator
 
 This api is used to calculate the number of microbatches required to fit a given model on a given batch size.
diff --git a/docs/api-guide/internal/optimizer_param_scheduler.md b/docs/api-guide/internal/optimizer_param_scheduler.md
index 13e1f77ccc0..45e5e4b7da1 100644
--- a/docs/api-guide/internal/optimizer_param_scheduler.md
+++ b/docs/api-guide/internal/optimizer_param_scheduler.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Optimizer Parameters Scheduler
 
 This api is used to calculate the learning rate and weight decay for the optimizer.
diff --git a/docs/api-guide/models/index.md b/docs/api-guide/models/index.md
index c6279d2409a..e5bb531454b 100644
--- a/docs/api-guide/models/index.md
+++ b/docs/api-guide/models/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Model APIs
 
 API reference for Megatron Core model implementations.
diff --git a/docs/api-guide/models/models.bert.md b/docs/api-guide/models/models.bert.md
index 3c53027c7c9..1543f4df865 100644
--- a/docs/api-guide/models/models.bert.md
+++ b/docs/api-guide/models/models.bert.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # models.bert package
 
 Useful package for training bert and bert like encoder only models. It optionally comes with a binary head that can be used for classification tasks .
diff --git a/docs/api-guide/models/models.gpt.md b/docs/api-guide/models/models.gpt.md
index a7c254d348b..1c3cbb5484c 100644
--- a/docs/api-guide/models/models.gpt.md
+++ b/docs/api-guide/models/models.gpt.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # models.gpt package
 
 This is the implementation of the popular GPT model. It supports several features like model parallelization (Tensor Parallel, Pipeline Parallel, Data Parallel) , mixture of experts, FP8 , Distributed optimizer etc. We are constantly adding new features. So be on the lookout or raise an issue if you want to have something added.
diff --git a/docs/api-guide/models/models.md b/docs/api-guide/models/models.md
index 69dfc80211d..a633546f0c9 100644
--- a/docs/api-guide/models/models.md
+++ b/docs/api-guide/models/models.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # models package
 
 This package contains most of the popular LLMs . Currently we have support for GPT, Bert, and T5 . This is an ever growing list so keep an eye out.
diff --git a/docs/api-guide/models/models.t5.md b/docs/api-guide/models/models.t5.md
index 90952096b63..4694b80113a 100644
--- a/docs/api-guide/models/models.t5.md
+++ b/docs/api-guide/models/models.t5.md
@@ -1,2 +1,11 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # models.t5 package
 
diff --git a/docs/api-guide/router_replay.md b/docs/api-guide/router_replay.md
index 300a50db127..4c1dc98b2b6 100644
--- a/docs/api-guide/router_replay.md
+++ b/docs/api-guide/router_replay.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Design Document: MoE Router Replay Feature
 
 ### 1. Overview
diff --git a/docs/conf.py b/docs/conf.py
index a64da441084..84ed89753f5 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2025-2026, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -25,9 +25,9 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 project = "Megatron Core"
-copyright = "2025, NVIDIA Corporation"
+copyright = "2026, NVIDIA Corporation"
 author = "NVIDIA Corporation"
-release = "latest"
+release = "nightly"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/docs/developer/contribute.md b/docs/developer/contribute.md
index 859b5562f4b..aeb785f915d 100644
--- a/docs/developer/contribute.md
+++ b/docs/developer/contribute.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Contributing to Megatron-LM
 
 This document outlines the processes and policies for issues and pull requests by non-NVIDIA contributors to the Megatron-LM GitHub repository.
diff --git a/docs/developer/generate_docs.md b/docs/developer/generate_docs.md
index 52fa288122d..d985f542caa 100644
--- a/docs/developer/generate_docs.md
+++ b/docs/developer/generate_docs.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Generating Docs Locally
 
 To generate docs locally, use the following commands:
diff --git a/docs/developer/oncall.md b/docs/developer/oncall.md
index b88da7bb6df..ee5582ca24d 100644
--- a/docs/developer/oncall.md
+++ b/docs/developer/oncall.md
@@ -1,3 +1,13 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+-->
+
 # Oncall Overview
 
 During your oncall week, you will be assigned to all PRs marked “Ready for 
diff --git a/docs/developer/submit.md b/docs/developer/submit.md
index a096312d21e..a46df22f85c 100644
--- a/docs/developer/submit.md
+++ b/docs/developer/submit.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # How to Submit a PR
 
 ## Step 1: Add PR label `Expert Review`
diff --git a/docs/discussions/README.md b/docs/discussions/README.md
index 4ac3c4e3254..b95300649d1 100644
--- a/docs/discussions/README.md
+++ b/docs/discussions/README.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron Discussions
 
 This directory contains in-depth guides, tutorials, and discussions about optimizing and using Megatron for various use cases.
diff --git a/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md b/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
index c2354ad07f0..5ef71043f86 100644
--- a/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
+++ b/docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron-FSDP User Guide
 
 ## Table of Contents
diff --git a/docs/documentation.md b/docs/documentation.md
index 16fbd7b9a7e..652a142aec9 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 ---
 orphan: true
 ---
diff --git a/docs/get-started/install.md b/docs/get-started/install.md
index dd000500f58..e1d7202b3fc 100644
--- a/docs/get-started/install.md
+++ b/docs/get-started/install.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron Core Installation
 
 Installation is supported using Docker and pip.
diff --git a/docs/get-started/overview.md b/docs/get-started/overview.md
index 883f40e0c61..42b964d5cec 100644
--- a/docs/get-started/overview.md
+++ b/docs/get-started/overview.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Overview
 
 Megatron-Core and Megatron-LM are open-source tools that are typically used together to train LLMs at scale across GPUs. Megatron-Core expands the capability of Megatron-LM. Megatron Bridge connects Megatron-Core and Megatron-LM to other popular training models, such as Hugging Face.
diff --git a/docs/get-started/quickstart.md b/docs/get-started/quickstart.md
index 61868e7877c..2addcb519a2 100644
--- a/docs/get-started/quickstart.md
+++ b/docs/get-started/quickstart.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Quick Start
 
 ## Quick Installation
diff --git a/docs/get-started/releasenotes.md b/docs/get-started/releasenotes.md
index e2d77cf0070..e624de19f15 100644
--- a/docs/get-started/releasenotes.md
+++ b/docs/get-started/releasenotes.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Release Notes
 
 
diff --git a/docs/index.md b/docs/index.md
index 448a75e4c93..c68f608a73b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron Core User Guide
 
 **Megatron Core** is a GPU-optimized library for training large language models at scale. It provides modular, composable building blocks for creating custom training frameworks with state-of-the-art parallelism strategies and performance optimizations.
@@ -6,13 +15,13 @@ Megatron Core offers a flexible, reusable foundation for building large-scale tr
 
 ## Key Features
 
-* Composable transformer building blocks (attention, MLP, etc.)
+* Composable transformer building blocks (attention, MLP)
 * Advanced parallelism strategies (TP, PP, DP, EP, CP)
 * Pipeline schedules and distributed optimizers
 * Mixed precision support (FP16, BF16, FP8)
 * GPU-optimized kernels and memory management
 * High-performance dataloaders and dataset utilities
-* Model architectures (LLaMA, Qwen, DeepSeek, GPT, Mamba, etc.)
+* Model architectures (LLaMA, Qwen, DeepSeek, GPT, Mamba)
 
 
 ```{toctree}
@@ -81,16 +90,16 @@ developer/generate_docs
 ```{toctree}
 :maxdepth: 2
 :hidden:
-:caption: Discussions
+:caption: API Reference
 
-advanced/index
+api-guide/index
+apidocs/index.rst
 ```
 
 ```{toctree}
 :maxdepth: 2
 :hidden:
-:caption: API Reference
+:caption: Resources
 
-api-guide/index
-apidocs/index.rst
+advanced/index
 ```
\ No newline at end of file
diff --git a/docs/llama_mistral.md b/docs/llama_mistral.md
index a79bb2c4bf9..076409cd4f5 100644
--- a/docs/llama_mistral.md
+++ b/docs/llama_mistral.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Llama, Mistral and other Llama-like model support in Megatron-LM
 
 NOTE: In order to simplify code we now only support converting llama-3.x and mistral checkpoints downloaded from Hugging Face. For converting other models, see [Megatron Bridge](models/index.md).
diff --git a/docs/models/index.md b/docs/models/index.md
index 6fabd1f582c..0ee379b01bd 100644
--- a/docs/models/index.md
+++ b/docs/models/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Supported Models
 
 Megatron Core supports a wide range of language and multimodal models with optimized implementations for large-scale training.
diff --git a/docs/models/llms.md b/docs/models/llms.md
index 6789a4c551c..f649673a2cc 100644
--- a/docs/models/llms.md
+++ b/docs/models/llms.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Language Models
 
 Megatron Core supports the following language model architectures for large-scale training.
diff --git a/docs/models/multimodal.md b/docs/models/multimodal.md
index 66ed8ccd9cb..dce977e261d 100644
--- a/docs/models/multimodal.md
+++ b/docs/models/multimodal.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Multimodal Models
 
 Megatron Core supports multimodal models that combine language with vision, audio, and other modalities for comprehensive multimodal understanding.
diff --git a/docs/project.json b/docs/project.json
index aa547ddd298..d5b9535338b 100644
--- a/docs/project.json
+++ b/docs/project.json
@@ -1,2 +1,2 @@
-{"name": "megatron-lm", "version": "latest"}
+{"name": "megatron-lm", "version": "nightly"}
 
diff --git a/docs/user-guide/data-preparation.md b/docs/user-guide/data-preparation.md
index 3ff5eedba89..18da2d80fe1 100644
--- a/docs/user-guide/data-preparation.md
+++ b/docs/user-guide/data-preparation.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Data Preparation
 
 Preparing your data correctly is essential for successful training with Megatron Core.
diff --git a/docs/user-guide/features/context_parallel.md b/docs/user-guide/features/context_parallel.md
index 841c16326b3..c44366187be 100644
--- a/docs/user-guide/features/context_parallel.md
+++ b/docs/user-guide/features/context_parallel.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # context_parallel package
 
 ## Context parallelism overview
diff --git a/docs/user-guide/features/custom_fsdp.md b/docs/user-guide/features/custom_fsdp.md
index 2f81eb0c5ef..ab1a1efc402 100644
--- a/docs/user-guide/features/custom_fsdp.md
+++ b/docs/user-guide/features/custom_fsdp.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron FSDP
 
 **NOTE: In M-Core 0.14, the custom FSDP refactored its checkpoint implementation to use DTensor-based torch distributed checkpointing. The custom FSDP was also renamed Megatron FSDP. The relevant sections of this document are no longer applicable.**
diff --git a/docs/user-guide/features/dist_optimizer.md b/docs/user-guide/features/dist_optimizer.md
index ddb6079885c..4e47791c12f 100644
--- a/docs/user-guide/features/dist_optimizer.md
+++ b/docs/user-guide/features/dist_optimizer.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Distributed Optimizer
 
 The motivation for the distributed optimizer is to save memory by distributing the optimizer state evenly across data parallel ranks (https://arxiv.org/abs/1910.02054), versus the naive method of replicating the optimizer state across data parallel ranks.
diff --git a/docs/user-guide/features/fine_grained_activation_offloading.md b/docs/user-guide/features/fine_grained_activation_offloading.md
index 53211d1d06c..494674bd4f0 100644
--- a/docs/user-guide/features/fine_grained_activation_offloading.md
+++ b/docs/user-guide/features/fine_grained_activation_offloading.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Fine-grained Activation Offloading (collaborated with rednote)
 
 Memory capacity is more and more important with the rising of extreme sparse MoE models like DeepSeek-V3 and Qwen3-235B. Fine-grained recomputing reduces the memory footprint at the cost of extra recomputation, while offloading could utilize the host-device bandwidth to achieve nearly zero-overhead. Fine-grained Activation Offloading targets at offloading the activation at the granularity of specific modules, so that we can calibrate the amount of offloading activation to maximize the training throughput.
diff --git a/docs/user-guide/features/index.md b/docs/user-guide/features/index.md
index 7730443e91b..fc5a1aa1abe 100644
--- a/docs/user-guide/features/index.md
+++ b/docs/user-guide/features/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Advanced Features
 
 Advanced feature guides for key Megatron Core capabilities.
diff --git a/docs/user-guide/features/megatron_energon.md b/docs/user-guide/features/megatron_energon.md
index d08bde21e38..9ebba72083a 100644
--- a/docs/user-guide/features/megatron_energon.md
+++ b/docs/user-guide/features/megatron_energon.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron Energon
 
 Advanced multimodal dataloader for efficient loading of text, images, video, and audio at scale.
diff --git a/docs/user-guide/features/megatron_rl.md b/docs/user-guide/features/megatron_rl.md
index 128b41bdaf5..653ecb92459 100644
--- a/docs/user-guide/features/megatron_rl.md
+++ b/docs/user-guide/features/megatron_rl.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Megatron RL
 
 Reinforcement learning library for post-training large language models at scale.
diff --git a/docs/user-guide/features/moe.md b/docs/user-guide/features/moe.md
index 56aca8c6999..2ba4ce4452e 100644
--- a/docs/user-guide/features/moe.md
+++ b/docs/user-guide/features/moe.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Mixture of Experts
 
 ```{toctree}
diff --git a/docs/user-guide/features/multi_latent_attention.md b/docs/user-guide/features/multi_latent_attention.md
index 5628b8cfee3..4310843557a 100644
--- a/docs/user-guide/features/multi_latent_attention.md
+++ b/docs/user-guide/features/multi_latent_attention.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Multi-Latent Attention
 
 ## Multi-Latent Attention overview
diff --git a/docs/user-guide/features/multi_token_prediction.md b/docs/user-guide/features/multi_token_prediction.md
index 891bf4c93c5..e16108bbcfa 100644
--- a/docs/user-guide/features/multi_token_prediction.md
+++ b/docs/user-guide/features/multi_token_prediction.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Multi-Token Prediction (MTP)
 
 Multi-Token Prediction (MTP) extends the prediction scope to multiple future tokens at each position. On the one hand, an MTP objective densifies the training signals and may improve
@@ -7,7 +16,7 @@ data efficiency. On the other hand, MTP may enable the model to pre-plan its rep
 
 The k-th MTP module consists of a shared embedding layer, a projection matrix, a Transformer block, and a shared output head. For the i-th input token at the (k - 1)-th prediction depth, we first combine the representation of the i-th token and the embedding of the (i + K)-th token with the linear projection. The combined serves as the input of the Transformer block at the k-th depth to produce the output representation.
 
-For more information, please refer to [DeepSeek-V3 Technical Report](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf)
+For more information, refer to [DeepSeek-V3 Technical Report](https://arxiv.org/pdf/2412.19437.pdf)
 
 ## Related Arguments
 
@@ -45,4 +54,4 @@ Use `m` to represent MTP layers in the pipeline layout string. For example:
 
 ## Precautions
 
-Please do not use Context Parallel (CP), or arbitrary AttnMaskType, or learned absolute position embedding type with MTP. These use cases are not yet supported.
+Do not use Context Parallel (CP), or arbitrary AttnMaskType, or learned absolute position embedding type with MTP. These use cases are not yet supported.
diff --git a/docs/user-guide/features/optimizer_cpu_offload.md b/docs/user-guide/features/optimizer_cpu_offload.md
index 408d7f6a788..1496bd0a91e 100644
--- a/docs/user-guide/features/optimizer_cpu_offload.md
+++ b/docs/user-guide/features/optimizer_cpu_offload.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Optimizer CPU Offload
 
 ```{include} ../../../megatron/core/optimizer/cpu_offloading/README.md
diff --git a/docs/user-guide/features/pipeline_parallel_layout.md b/docs/user-guide/features/pipeline_parallel_layout.md
index 30c8ce1a500..96b00eca004 100644
--- a/docs/user-guide/features/pipeline_parallel_layout.md
+++ b/docs/user-guide/features/pipeline_parallel_layout.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Custom Pipeline Model Parallel Layout
 
 *This is an experimental feature and may be changed.*
diff --git a/docs/user-guide/features/tokenizers.md b/docs/user-guide/features/tokenizers.md
index 0aecf8df8a7..bc1a47cec76 100644
--- a/docs/user-guide/features/tokenizers.md
+++ b/docs/user-guide/features/tokenizers.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Tokenizers
 
 Megatron Core provides a unified tokenizer system with a HuggingFace-style API for easy tokenizer management and configuration.
@@ -141,7 +150,7 @@ Use a null tokenizer for testing or non-text models:
 
 ```python
 tokenizer = MegatronTokenizer.from_pretrained(
-    metadata_path={"library": "null"},
+    metadata_path={"library": "null-text"},
     vocab_size=131072,
 )
 ```
@@ -173,16 +182,6 @@ torchrun --nproc_per_node=8 pretrain_gpt.py \
 
 If `--tokenizer-metadata` is not specified, a default metadata file is generated automatically based on the tokenizer type.
 
-### Legacy Tokenizer Support
-
-The old tokenizer system is still supported for backward compatibility:
-
-```bash
-torchrun --nproc_per_node=8 pretrain_gpt.py \
-    --legacy-tokenizer \
-    ...
-```
-
 ## Supported Tokenizer Libraries
 
 | Library | Description | Use Case |
diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md
index bbe85451582..45e70c3a520 100644
--- a/docs/user-guide/index.md
+++ b/docs/user-guide/index.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # User Guide
 
 Comprehensive guides for using Megatron Core and Megatron-LM.
diff --git a/docs/user-guide/msc_integration.md b/docs/user-guide/msc_integration.md
index fd73ac7e8f4..a197f25afc1 100644
--- a/docs/user-guide/msc_integration.md
+++ b/docs/user-guide/msc_integration.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 ```{include} ../../megatron/core/MSC_Integration.md
 ```
 
diff --git a/docs/user-guide/parallelism-guide.md b/docs/user-guide/parallelism-guide.md
index 2baf518ae85..8d5cb8ff7c3 100644
--- a/docs/user-guide/parallelism-guide.md
+++ b/docs/user-guide/parallelism-guide.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Parallelism Strategies Guide
 
 Megatron Core supports multiple parallelism strategies that can be combined to efficiently train models from billions to trillions of parameters across thousands of GPUs.
diff --git a/docs/user-guide/quickstart.md b/docs/user-guide/quickstart.md
deleted file mode 100644
index 7baed06d6be..00000000000
--- a/docs/user-guide/quickstart.md
+++ /dev/null
@@ -1,3 +0,0 @@
-```{include} ../../megatron/core/QuickStart.md
-```
-
diff --git a/docs/user-guide/training-examples.md b/docs/user-guide/training-examples.md
index 2824c608c36..425728c9e74 100644
--- a/docs/user-guide/training-examples.md
+++ b/docs/user-guide/training-examples.md
@@ -1,3 +1,12 @@
+<!---
+   Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
+   NVIDIA CORPORATION and its licensors retain all intellectual property
+   and proprietary rights in and to this software, related documentation
+   and any modifications thereto. Any use, reproduction, disclosure or
+   distribution of this software and related documentation without an express
+   license agreement from NVIDIA CORPORATION is strictly prohibited.
+-->
+
 # Training Examples
 
 Get started with Megatron Core training using these practical examples.
diff --git a/docs/versions1.json b/docs/versions1.json
index a524c5921a8..ae1809d538e 100644
--- a/docs/versions1.json
+++ b/docs/versions1.json
@@ -1,7 +1,12 @@
 [
     {
-        "name": "latest",
-        "version": "latest",
+        "name": "nightly",
+        "version": "nightly",
+        "url": "https://docs.nvidia.com/megatron-core/nightly/"
+    },
+    {
+        "name": "0.16.0 (latest)",
+        "version": "0.16.0",
         "url": "https://docs.nvidia.com/megatron-core/latest/"
     },
     {
diff --git a/examples/gpt3/gpt_config.yaml b/examples/gpt3/gpt_config.yaml
index 18d305d9cb1..600f50221ce 100644
--- a/examples/gpt3/gpt_config.yaml
+++ b/examples/gpt3/gpt_config.yaml
@@ -92,7 +92,6 @@ model_parallel:
 
   # Optimizations
   gradient_accumulation_fusion: True
-  async_tensor_model_parallel_allreduce: True
   tp_comm_overlap: False
 
   # Debug Options
diff --git a/examples/inference/gpt/gpt_dynamic_inference.py b/examples/inference/gpt/gpt_dynamic_inference.py
index 88b744b3ac0..2582f57e13e 100644
--- a/examples/inference/gpt/gpt_dynamic_inference.py
+++ b/examples/inference/gpt/gpt_dynamic_inference.py
@@ -1,40 +1,31 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
+# pylint: disable=bad-builtin
+
 import hashlib
 import io
 import json
-import math
 import os
-import pickle
 import sys
 import warnings
-import torch
-from argparse import ArgumentParser
 from collections import defaultdict
-from functools import partial
+from typing import Dict, List, Optional
+
+import torch
 from tqdm import tqdm
-from typing import Dict, List, Tuple, Optional
 
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
 )
 
-import megatron
 from examples.inference.gpt.utils import (
     Request,
-    add_common_inference_args,
     build_dynamic_engine_setup_prefix,
     build_requests,
     get_curr_time,
     get_global_peak_memory_stats_bytes,
 )
-from megatron.core.inference.contexts.dynamic_context import (
-    ContextOverflowError,
-    DynamicInferenceContext,
-)
-from megatron.core.inference.contexts.attention_context.mamba_metadata import (
-    MambaInferenceStateConfig,
-)
+from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
 from megatron.core.inference.engines import DynamicInferenceEngine, EngineSuspendedError
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
@@ -43,195 +34,27 @@
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
 )
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
-from megatron.core.transformer.module import MegatronModule
-from megatron.core.utils import get_mamba_inference_state_config_from_model
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
+from megatron.inference.utils import (
+    add_inference_args,
+    get_inference_config_from_model_and_args,
+    get_model_for_inference,
+)
 
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
 )
-from megatron.training import get_args, get_model as _get_model, get_tokenizer, initialize_megatron
-from megatron.training.checkpointing import load_checkpoint
-from model_provider import model_provider
-from gpt_builders import gpt_builder
-from mamba_builders import mamba_builder
+import logging
 
+import megatron
 from megatron.core.utils import configure_nvtx_profiling
-import logging
+from megatron.training import get_args, get_tokenizer, initialize_megatron
 
 torch.serialization.add_safe_globals([io.BytesIO])
 torch.serialization.add_safe_globals([megatron.core.rerun_state_machine.RerunState])
 torch.serialization.add_safe_globals([megatron.core.rerun_state_machine.RerunDiagnostic])
 
 
-def add_dynamic_inference_args(parser: ArgumentParser) -> ArgumentParser:
-    """Dynamic inference arguments."""
-
-    add_common_inference_args(parser)
-
-    group = parser.add_argument_group(title='Dynamic inference')
-    group.add_argument(
-        "--inference-ckpt-non-strict",
-        action="store_true",
-        help="Load checkpoint with `strict=False`.",
-    )
-    group.add_argument(
-        "--termination-id", type=int, default=None,
-        help="Termination ID that overrides `tokenizer.eod`.",
-    )
-    group.add_argument(
-        "--suspend-resume-interval", type=int, default=None,
-        help="Suspend and resume the dynamic engine every "
-        "`suspend_resume_interval` steps. This is used to tet the suspend/resume "
-        "system.",
-    )
-    group.add_argument(
-        "--inference-repeat-n", type=int, default=1,
-        help="Repeat inference iterations N times for benchmarking."
-    )
-    group.add_argument(
-        "--throughput-check-only",
-        action='store_true',
-        default=False,
-        help="If true, only run throughput check without verifying outputs."
-    )
-
-    return parser
-
-
-def get_model() -> MegatronModule:
-    """Initialize model and load checkpoint."""
-
-    args = get_args()
-
-    if args.model_provider == "gpt":
-        model_builder = gpt_builder
-    elif args.model_provider == "mamba":
-        model_builder = mamba_builder
-    else:
-        raise ValueError(f"Invalid model provider {args.model_provider}")
-
-    # Build model.
-    model = _get_model(
-        partial(model_provider, model_builder),
-        wrap_with_ddp=False
-    )
-
-    # Load checkpoint.
-    assert args.load is not None
-    args.exit_on_missing_checkpoint = True
-    load_checkpoint(
-        ddp_model=model,
-        optimizer=None,
-        opt_param_scheduler=None,
-        strict=not args.inference_ckpt_non_strict,
-    )
-
-    # No virtual PP.
-    assert len(model) == 1, "Above condition should have caught this"
-    model = model[0]
-
-    # Eval mode.
-    model.eval()
-
-    return model
-
-
-def get_inference_context(
-    requests: List[Request],
-    sampling_params: Optional[SamplingParams] = None,
-    calculate_max_sequence_length_from_requests: bool = True,
-    mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None,
-):
-    """The inference context manages the KV cache and other inference state."""
-
-    args = get_args()
-
-    # Max sequence length.
-    if calculate_max_sequence_length_from_requests:
-        max_gen_length = sampling_params.num_tokens_to_generate    
-        max_context_length = max(len(r.prompt_tokens) for r in requests)
-        max_sequence_length = max_context_length + max_gen_length
-    else:
-        max_sequence_length = args.inference_max_seq_length
-
-    metrics_writer = None
-    if args.inference_logging_step_interval > 0 and args.inference_wandb_logging:
-        metrics_writer = get_wandb_writer()
-
-    # Inference context.
-    context = DynamicInferenceContext(
-        params_dtype=args.params_dtype,
-        num_layers=args.num_layers // args.pipeline_model_parallel_size,
-        kv_channels=args.kv_channels,
-        num_attention_heads=(
-            args.num_query_groups if args.group_query_attention else args.num_attention_heads
-        ),
-        max_sequence_length=max_sequence_length,
-        num_cuda_graphs=(
-            args.inference_dynamic_batching_num_cuda_graphs
-            if args.cuda_graph_impl == "local"
-            else None
-        ),
-        block_size_tokens=args.inference_dynamic_batching_block_size,
-        buffer_size_gb=args.inference_dynamic_batching_buffer_size_gb,
-        paused_buffer_size_gb=args.inference_dynamic_batching_paused_buffer_size_gb,
-        max_requests=args.inference_dynamic_batching_max_requests,
-        max_tokens=args.inference_dynamic_batching_max_tokens,
-        tensor_model_parallel_size=args.tensor_model_parallel_size,
-        pipeline_model_parallel_size=args.pipeline_model_parallel_size,
-        materialize_only_last_token_logits=not args.return_log_probs,
-        mamba_inference_state_config=mamba_inference_state_config,
-        cache_mla_latent=args.multi_latent_attention and args.cache_mla_latents,
-        kv_lora_rank=args.kv_lora_rank if args.multi_latent_attention else None,
-        qk_pos_emb_head_dim=args.qk_pos_emb_head_dim,
-        use_cuda_graphs_for_non_decode_steps=not args.decode_only_cuda_graphs,
-        use_flashinfer_fused_rope=args.use_flashinfer_fused_rope,
-        unified_memory_level=args.inference_dynamic_batching_unified_memory_level,
-        cuda_graph_max_tokens=args.inference_dynamic_batching_cuda_graph_max_tokens,
-        cuda_graph_mixed_prefill_count=args.inference_dynamic_batching_cuda_graph_mixed_prefill_count,
-        metrics_writer=metrics_writer,
-        offload_kv_cache=args.rl_offload_kv_cache_during_training
-    )
-
-    return context
-
-
-def get_inference_controller(
-    model: MegatronModule, context: DynamicInferenceContext
-) -> TextGenerationController:
-    """Buid text generation controller, which manages the model inference context.
-
-    Args:
-        model (MegatronModule): Megatron GPT model.
-        context (DynamicInferenceContext): Context for managing KV cache blocks.
-
-    Return:
-        (TextGenerationController) Inference text generation controller.
-    """
-
-    args = get_args()
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
-
-    # Wrap model in inference wrapper.
-    model = GPTInferenceWrapper(model, args, context)
-
-    # Note: the following is taken from AbstractModelInferenceWrapper.prep_model_for_inference().
-    from megatron.core import parallel_state
-
-    model.model_is_pipeline_parallel = not (
-        parallel_state.is_pipeline_first_stage() and parallel_state.is_pipeline_last_stage()
-    )
-
-    # Text generation controller.
-    controller = TextGenerationController(model, tokenizer)
-
-    return controller
-
-
 def run_inference(
     requests: List[Request],
     engine: DynamicInferenceEngine,
@@ -284,11 +107,7 @@ def _add_request():
         """
         nonlocal num_requests_added
         _request = requests[num_requests_added]
-        engine.add_request(
-            num_requests_added,
-            _request.prompt_text,
-            _request.sampling_params,
-        )
+        engine.add_request(num_requests_added, _request.prompt_text, _request.sampling_params)
         _request.time_start = get_curr_time()
         _request.state = "started"
         num_requests_added += 1
@@ -305,10 +124,9 @@ def _add_request():
                 _add_request()
         else:
             # Add deterministic number of requests (generally used for debugging).
-            for i in range(min(
-                args.incoming_requests_per_step,
-                num_requests_total - num_requests_added,
-            )):
+            for i in range(
+                min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
+            ):
                 _add_request()
         add_times.append(get_curr_time() - add_start)
 
@@ -318,11 +136,12 @@ def _add_request():
             result = engine.step_modern()
         except EngineSuspendedError as e:
             result = e
-            pass # ignore error in order to call 'engine.resume()' below.
+            pass  # ignore error in order to call 'engine.resume()' below.
         attempted_step_count += 1
 
-        # After step, we lost track of last iteration's is_decode_only, so we need to get it from the engine
-        is_decode_only = engine.is_decode_only 
+        # After step, we lost track of last iteration's is_decode_only,
+        # so we need to get it from the engine
+        is_decode_only = engine.is_decode_only
 
         # Test suspending and resuming engine.
         if args.suspend_resume_interval is not None:
@@ -335,9 +154,9 @@ def _add_request():
             # Resume, 0+ attempted steps later.
             if (
                 attempted_step_count > 0
-                and
-                (attempted_step_count - args.suspend_resume_interval // 2)
-                    % args.suspend_resume_interval == 0
+                and (attempted_step_count - args.suspend_resume_interval // 2)
+                % args.suspend_resume_interval
+                == 0
             ):
                 print("**** step %d/%d ... resume." % (engine.step_count, attempted_step_count))
                 engine.resume()
@@ -349,7 +168,9 @@ def _add_request():
         # Record cuda_graph_request_count.
         cuda_graph_request_count = result["cuda_graph_request_count"]
         if args.cuda_graph_impl == "local" and cuda_graph_request_count is not None:
-            cuda_graph_request_count_map[cuda_graph_request_count] = cuda_graph_request_count_map.get(cuda_graph_request_count, 0) + 1
+            cuda_graph_request_count_map[cuda_graph_request_count] = (
+                cuda_graph_request_count_map.get(cuda_graph_request_count, 0) + 1
+            )
 
         # Update requests.
         active_request_ids = result["active_request_ids"]
@@ -374,6 +195,8 @@ def _add_request():
                 request.request_id = finished_request.request_id
                 request.events = finished_request.events
 
+                request.ttft = finished_request.ttft
+
                 # Update prompt, in case engine has been suspended and resumed.
                 request.prompt_tokens = finished_request.prompt_tokens.tolist()
                 request.prompt_text = finished_request.prompt
@@ -408,38 +231,37 @@ def _add_request():
         engine.resume()
 
     return {
-        "step_times" : step_times,
-        "add_times" : add_times,
-        "output_times" : output_times,
-        "total_output_tokens" : total_output_tokens,
-        "cuda_graph_request_count_map" : cuda_graph_request_count_map,
+        "step_times": step_times,
+        "add_times": add_times,
+        "output_times": output_times,
+        "total_output_tokens": total_output_tokens,
+        "cuda_graph_request_count_map": cuda_graph_request_count_map,
     }
 
 
 @torch.inference_mode()
 def main():
-
+    """Run dynamic inference."""
     # Initialize Megatron.
     initialize_megatron(
-        extra_args_provider=add_dynamic_inference_args,
+        extra_args_provider=add_inference_args,
         args_defaults={'no_load_rng': True, 'no_load_optim': True},
     )
 
     # Start Nsight profiler.
     if os.environ.get("NSIGHT_PREFIX"):
         torch.cuda.cudart().cudaProfilerStart()
-    
-    level_str = os.getenv("LOG_LEVEL", "INFO").upper() 
-    level = getattr(logging, level_str, logging.INFO) 
+
+    level_str = os.getenv("LOG_LEVEL", "INFO").upper()
+    level = getattr(logging, level_str, logging.INFO)
     logging.basicConfig(level=level, force=True)
 
     configure_nvtx_profiling(True)
 
     args = get_args()
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+
+    # Build tokenizer
+    tokenizer = build_tokenizer(args)
 
     # Reset peak memory stats so functional tests measure this run and not
     # whatever happened earlier during initialization.
@@ -456,42 +278,36 @@ def main():
         termination_id=args.termination_id if args.termination_id is not None else tokenizer.eod,
         top_n_logprobs=args.top_n_logprobs,
         stop_words=args.stop_words,
-    ) 
-
-    model = get_model()
+    )
 
-    mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
+    model = get_model_for_inference()
 
     # Requests, context, controller.
     requests = build_requests(args, tokenizer, sampling_params)
-    context = get_inference_context(
-        requests,
-        sampling_params,
-        mamba_inference_state_config=mamba_inference_state_config,
-    )
-    controller = get_inference_controller(model, context)
+    inference_config = get_inference_config_from_model_and_args(model, args)
+
+    # Calculate max_sequence_length from requests
+    max_gen_length = sampling_params.num_tokens_to_generate
+    max_context_length = max(len(r.prompt_tokens) for r in requests)
+    inference_config.max_sequence_length = max_context_length + max_gen_length
+    context = DynamicInferenceContext(model.config, inference_config)
+    wrapped_model = GPTInferenceWrapper(model, context)
+    controller = TextGenerationController(wrapped_model, tokenizer)
 
     # Validate all context_length's <= max_tokens.
-    if args.disable_chunked_prefill:
+    if not args.enable_chunked_prefill:
         invalid_prompt_length_map = {}
         for request_idx, request in enumerate(requests):
             if len(request.prompt_tokens) > context.max_tokens:
                 invalid_prompt_length_map[request_idx] = len(request.prompt_tokens)
-        assert not invalid_prompt_length_map, (
-            "request idxs with prompts longer than context.max_tokens: "
-            ", ".join(f"{k}({v})" for k, v in invalid_prompt_length_map.items())
+        assert (
+            not invalid_prompt_length_map
+        ), "request idxs with prompts longer than context.max_tokens: " ", ".join(
+            f"{k}({v})" for k, v in invalid_prompt_length_map.items()
         )
 
     # Inference engine.
-    engine = DynamicInferenceEngine(
-        controller,
-        context,
-        enable_cuda_graph=args.cuda_graph_impl == "local",
-        random_seed=args.seed,
-        track_paused_request_events=args.inference_dynamic_batching_track_paused_request_events,
-        enable_chunked_prefill=not args.disable_chunked_prefill,
-        inference_logging_step_interval=args.inference_logging_step_interval,
-    )
+    engine = DynamicInferenceEngine(controller, context)
 
     setup_prefix = build_dynamic_engine_setup_prefix(args, model, context, requests)
     print("~~~")
@@ -522,14 +338,13 @@ def main():
 
     # Validate all requests finished.
     for request in requests:
-        assert request.state == "finished", (
-            f"request.state == '{request.state}' != 'finished'."
-        )
+        assert request.state == "finished", f"request.state == '{request.state}' != 'finished'."
 
     peak_mem_stats = get_global_peak_memory_stats_bytes()
 
     # Print unique prompts + outputs.
     if torch.distributed.get_rank() == 0:
+
         def escape_str(s):
             return s.replace("\n", "\\n")
 
@@ -547,7 +362,10 @@ def escape_str(s):
             # ---- Prompt summary line ----
             prompt_len = len(requests[request_idxs[0]].prompt_tokens)
             escaped_prompt_text = escape_str(prompt_text)
-            print(f"\n{unique_idx+1}/{len(unique_prompt_map)} [n {len(request_idxs)}, l {prompt_len}] {escaped_prompt_text}")
+            print(
+                f"\n{unique_idx+1}/{len(unique_prompt_map)}"
+                f"[n {len(request_idxs)}, l {prompt_len}] {escaped_prompt_text}"
+            )
 
             # ---- Group all outputs for this prompt ----
             output_map = defaultdict(list)
@@ -567,16 +385,17 @@ def escape_str(s):
                     # Use hash of prompt + generated text in case engine was
                     # suspended and resumed, which misaligns boundary between
                     # prompt and generated tokens.
-                    o_hash = hashlib.sha256(
-                        (prompt_text + output_text).encode()
-                    ).hexdigest()[:6]
+                    o_hash = hashlib.sha256((prompt_text + output_text).encode()).hexdigest()[:6]
                     o_len = len(requests[output_request_idxs[0]].output_tokens)
                     escaped_output_text = escape_str(output_text)
                 else:
                     o_hash = "--"
                     o_len = 0
                     escaped_output_text = "--"
-                print(f"  >>>> [n {len(output_request_idxs)}, {o_len} tokens, hash {o_hash}{', <evicted>' if evicted else ''}] {escaped_output_text}")
+                print(
+                    f"  >>>> [n {len(output_request_idxs)}, {o_len} tokens, hash {o_hash}"
+                    f"{', <evicted>' if evicted else ''}] {escaped_output_text}"
+                )
                 text_hashes.append(o_hash)
 
         # Write results to JSON. Primarily used for functional testing.
@@ -592,14 +411,17 @@ def escape_str(s):
                         "generated_text": req.output_text,
                         "generated_tokens": req.output_tokens,
                         "latency": req.time_end - req.time_start,
-                        "cuda_graph_request_count_map" : result["cuda_graph_request_count_map"],
-                        "step_count" : engine.step_count,
-                        "top_n_logprobs" : getattr(req, 'generated_top_n_logprobs', None),
-                        "prompt_top_n_logprobs" : getattr(req, 'prompt_top_n_logprobs', None),
+                        "ttft": req.ttft,  # Time-to-first-token in seconds
+                        "cuda_graph_request_count_map": result["cuda_graph_request_count_map"],
+                        "step_count": engine.step_count,
+                        "top_n_logprobs": getattr(req, 'generated_top_n_logprobs', None),
+                        "prompt_top_n_logprobs": getattr(req, 'prompt_top_n_logprobs', None),
                     }
                     if req.sampling_params.return_log_probs:
                         result_dict["prompt_logprobs"] = getattr(req, 'prompt_log_probs', None)
-                        result_dict["generated_logprobs"] = getattr(req, 'generated_log_probs', None)
+                        result_dict["generated_logprobs"] = getattr(
+                            req, 'generated_log_probs', None
+                        )
                         result_dict["logprobs"] = getattr(req, 'logprobs', None)
                     json_results[req.request_id] = result_dict
 
@@ -631,7 +453,7 @@ def escape_str(s):
         d_count = len(d_times)
 
         p_mean = p_total / p_count
-        d_mean = d_total / d_count if d_count != 0 else 0.
+        d_mean = d_total / d_count if d_count != 0 else 0.0
 
         # Commented out for now as the step/add/output times are not calculated correctly.
         # print(
@@ -643,18 +465,13 @@ def escape_str(s):
         #     f"mean [ p {p_mean:.3f}s, d {d_mean:.3f}s ], "
         #     f"count [ p {p_count}, d {d_count} ]."
         # )
-        capture_str = (
-            f"{engine.capture_stats['time']:.2f} sec"
-            if engine.capture_stats else
-            "--"
-        )
+        capture_str = f"{engine.capture_stats['time']:.2f} sec" if engine.capture_stats else "--"
         print(
-            f"{setup_prefix} … "
-            f"throughput: {throughput:.3f} tok/s … ",
+            f"{setup_prefix} … " f"throughput: {throughput:.3f} tok/s … ",
             f"total time: {total_time:.3f}s … "
             f"mem {peak_alloc_gb:.1f}/{peak_resvd_gb:.1f} GB … "
             f"steps: {engine.step_count:d} … "
-            f"capture {capture_str}"
+            f"capture {capture_str}",
         )
         print("~~~")
 
diff --git a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
index cbb7a1aa745..536f533eccd 100644
--- a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
+++ b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
@@ -2,43 +2,33 @@
 
 import asyncio
 import json
+import logging
 import os
 import time
-import torch
-import torch.distributed as dist
+import warnings
 from collections import defaultdict
-from tqdm import tqdm
 from typing import List
-import warnings
-import logging
 
-from examples.inference.gpt.gpt_dynamic_inference import (
-    add_dynamic_inference_args,
-    get_inference_context,
-    get_inference_controller,
-    get_model,
-)
-from examples.inference.gpt.utils import (
-    Request, 
-    build_dynamic_engine_setup_prefix, 
-    build_requests,
-    add_common_inference_args
-)
+import torch
+import torch.distributed as dist
 
-from megatron.core import parallel_state
+from examples.inference.gpt.utils import Request, build_dynamic_engine_setup_prefix, build_requests
 from megatron.core.inference.engines import DynamicInferenceEngine
 from megatron.core.inference.inference_client import InferenceClient
 from megatron.core.inference.inference_request import DynamicInferenceRequestRecord
 from megatron.core.inference.sampling_params import SamplingParams
-from megatron.core.utils import get_mamba_inference_state_config_from_model
-
+from megatron.inference.utils import (
+    add_inference_args,
+    get_dynamic_inference_engine,
+    get_model_for_inference,
+)
 from megatron.training import get_args, get_tokenizer, initialize_megatron
-from megatron.training.arguments import parse_args
 
 # pylint: disable=line-too-long
 
 logging.basicConfig(level=logging.INFO, force=True)
 
+
 async def main(
     engine: DynamicInferenceEngine,
     requests: List[Request],
@@ -51,12 +41,11 @@ async def main(
             "Sampling parameters are specified per request.",
             DeprecationWarning,
         )
-        
+
     # once you call engine.start_listening_to_data_parallel_coordinator,
     # the engine will start accepting requests from the data parallel coordinator.
     # and processing them in an asyncio coroutine.
     # leaving inference_coordinator_port as None will find a free port automatically.
-    
     dp_addr = await engine.start_listening_to_data_parallel_coordinator(
         inference_coordinator_port=port,
         launch_inference_coordinator=True,
@@ -69,14 +58,11 @@ async def main(
         # Since the client doesn't directly call engine.async_step here, we test
         # the suspend-resume system ~4 times.
         suspend_resume_interval = max(1, len(requests) // 4)
-        suspend_idxs = set(range(
-            suspend_resume_interval,
-            len(requests) + 1,
-            suspend_resume_interval,
-        ))
+        suspend_idxs = set(
+            range(suspend_resume_interval, len(requests) + 1, suspend_resume_interval)
+        )
         resume_idxs = set(
-            min(len(requests), i + suspend_resume_interval // 2)
-            for i in suspend_idxs
+            min(len(requests), i + suspend_resume_interval // 2) for i in suspend_idxs
         )
     else:
         suspend_idxs = set()
@@ -98,7 +84,10 @@ async def main(
             current_time = time.time_ns() / 10**9
             if args.incoming_requests_per_step is None:
                 # Only add requests that have arrived at the current time.
-                while num_requests_added < num_requests_total and requests[num_requests_added].time_arrival <= current_time:
+                while (
+                    num_requests_added < num_requests_total
+                    and requests[num_requests_added].time_arrival <= current_time
+                ):
                     request = requests[num_requests_added]
                     # These add-request calls will queue up the request on a zmq socket and return
                     # instantaneously. They will return an asyncio future which can be awaited for
@@ -114,10 +103,9 @@ async def main(
 
             else:
                 # Add deterministic number of requests (generally used for debugging).
-                for i in range(min(
-                    args.incoming_requests_per_step,
-                    num_requests_total - num_requests_added
-                )):
+                for i in range(
+                    min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
+                ):
                     # Change sampling parameters to force different generation lengths.
                     request = requests[num_requests_added]
                     n = request.sampling_params.num_tokens_to_generate
@@ -135,7 +123,7 @@ async def main(
                 break
             # Relinquish control since there are no more requests to add at the moment. This allows the engine to run.
             await asyncio.sleep(0)
-        
+
         # While we wait for the requests to complete, the engine runs in the background.
         results: List[DynamicInferenceRequestRecord] = await asyncio.gather(*futures)
 
@@ -157,6 +145,9 @@ async def main(
                     result_dict["logprobs"] = req.prompt_log_probs + req.generated_log_probs
                 throughput = len(req.generated_tokens) / req.latency
                 throughputs.append(throughput)
+                if req.routing_indices is not None:
+                    result_dict["routing_indices"] = req.routing_indices.tolist()
+                                
                 json_results[req.request_id] = result_dict
             throughput_dict = {"throughput": throughputs}
             if args.throughput_check_only:
@@ -170,16 +161,19 @@ async def main(
                 req = record.merge()
                 unique_prompt_map[req.prompt].append(req)
             for idx, (prompt_text, reqs) in enumerate(unique_prompt_map.items()):
-                print(f"%d/%d. prompt '%s' ... [%d] output '%s'." % (
-                    idx,
-                    len(unique_prompt_map),
-                    prompt_text.replace("\n", "\\n"),
-                    len(reqs),
-                    reqs[0].generated_text.replace("\n", "\\n"),
-                ))
+                print(
+                    f"%d/%d. prompt '%s' ... [%d] output '%s'."
+                    % (
+                        idx,
+                        len(unique_prompt_map),
+                        prompt_text.replace("\n", "\\n"),
+                        len(reqs),
+                        reqs[0].generated_text.replace("\n", "\\n"),
+                    )
+                )
 
         # kill the engines and suspend the client
-        # Right now, we can only call stop when all requests are done. 
+        # Right now, we can only call stop when all requests are done.
         # Todo: Make this explicit in the Client class....
         await client.stop_engines()
         client.stop()
@@ -190,11 +184,11 @@ async def main(
 
 
 if __name__ == "__main__":
-    # enable inference mode in the very beginning as some fp-8 optimizations
+    # enable inference mode in the very beginning as some fp8 optimizations
     # check for it.
     with torch.inference_mode():
         initialize_megatron(
-            extra_args_provider=add_dynamic_inference_args,
+            extra_args_provider=add_inference_args,
             args_defaults={'no_load_rng': True, 'no_load_optim': True},
         )
 
@@ -213,34 +207,16 @@ async def main(
             ),
         )
 
-        # Requests, context, conroller.
-        model = get_model()
-        mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
+        model = get_model_for_inference()
+
         requests = (
             build_requests(args, tokenizer, sampling_params) if dist.get_rank() == 0 else None
         )
 
-        context = get_inference_context(
-            None,
-            None,
-            calculate_max_sequence_length_from_requests=False,
-            mamba_inference_state_config=mamba_inference_state_config,
-        )
-
-        controller = get_inference_controller(model, context)
-
-        # Inference engine.
-        engine = DynamicInferenceEngine(
-            controller,
-            context,
-            enable_cuda_graph=args.cuda_graph_impl == "local",
-            random_seed=args.seed,
-            enable_chunked_prefill=not args.disable_chunked_prefill,
-            inference_logging_step_interval=args.inference_logging_step_interval,
-        )
+        engine = get_dynamic_inference_engine(model=model)
 
         if dist.get_rank() == 0:
-            setup_prefix = build_dynamic_engine_setup_prefix(args, model, context, requests)
+            setup_prefix = build_dynamic_engine_setup_prefix(args, model, engine.context, requests)
             print("~~~")
             print(setup_prefix)
             print("~~~")
@@ -249,13 +225,7 @@ async def main(
         if os.environ.get("NSIGHT_PREFIX"):
             torch.cuda.cudart().cudaProfilerStart()
 
-        asyncio.run(
-            main(
-                engine,
-                requests,
-                args.inference_coordinator_port,
-            )
-        )
+        asyncio.run(main(engine, requests, args.inference_coordinator_port))
 
         # Stop Nsight profiler.
         if os.environ.get("NSIGHT_PREFIX"):
diff --git a/examples/inference/gpt/gpt_static_inference.py b/examples/inference/gpt/gpt_static_inference.py
index 03a60927ab2..17cf7c53b05 100644
--- a/examples/inference/gpt/gpt_static_inference.py
+++ b/examples/inference/gpt/gpt_static_inference.py
@@ -1,21 +1,11 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
 import os
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
-from model_provider import model_provider
-from gpt_builders import gpt_builder
-from mamba_builders import mamba_builder
-import torch
 import sys
 import time
-import warnings
-from functools import partial
 from argparse import Namespace
 
 import torch
-import tqdm
 
 from megatron.core.inference.contexts import StaticInferenceContext
 from megatron.core.inference.engines import StaticInferenceEngine
@@ -23,17 +13,12 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
 )
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.transformer.module import MegatronModule
-from pretrain_gpt import model_provider as gpt_model_provider
-from pretrain_mamba import model_provider as mamba_model_provider
 
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
@@ -41,18 +26,18 @@
 
 import asyncio
 import json
-from typing import Any, AsyncIterator, List
+from typing import List
 
-from examples.inference.gpt.utils import add_common_inference_args, build_requests
-from megatron.core import mpu
-from megatron.training import get_args, get_model, get_tokenizer, print_rank_0
-from megatron.training.checkpointing import load_checkpoint
+from examples.inference.gpt.utils import build_requests
+from megatron.inference.utils import add_inference_args, get_model_for_inference
+from megatron.training import get_args, get_tokenizer, print_rank_0
 from megatron.training.initialize import initialize_megatron
 
+
 def add_static_inference_args(parser):
     """Static inference arguments."""
 
-    add_common_inference_args(parser)
+    add_inference_args(parser)
 
     group = parser.add_argument_group(title='Static inference')
     group.add_argument(
@@ -79,34 +64,17 @@ def get_inference_engine(args: Namespace, model: MegatronModule) -> StaticInfere
     Returns:
         AbstractBackend: The chosen backend
     """
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
-    inference_wrapper_config = InferenceWrapperConfig(
-        hidden_size=args.hidden_size,
-        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
-        fp32_residual_connection=args.fp32_residual_connection,
-        params_dtype=args.params_dtype,
-        padded_vocab_size=args.padded_vocab_size,
-        inference_max_requests=args.inference_max_batch_size,
-        inference_max_seq_length=args.inference_max_seq_length,
-        nccl_all_reduce_for_prefill=args.nccl_all_reduce_for_prefill,
-        fp8=args.fp8,
-        moe_pad_experts_for_cuda_graph_inference = args.moe_pad_experts_for_cuda_graph_inference
-    )
-
-    inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
-
-    inference_wrapped_model = GPTInferenceWrapper(
-        model, inference_wrapper_config, inference_context
+    tokenizer = build_tokenizer(args)
+    inference_context = StaticInferenceContext(
+        args.inference_max_requests, args.inference_max_seq_length
     )
+    inference_wrapped_model = GPTInferenceWrapper(model, inference_context)
     text_generation_controller = TextGenerationController(
         inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
     )
     engine_kwargs = {
-        "text_generation_controller" : text_generation_controller,
-        "legacy" : args.use_legacy_static_engine,
+        "text_generation_controller": text_generation_controller,
+        "legacy": args.use_legacy_static_engine,
     }
     if not args.use_legacy_static_engine:
         engine_kwargs["buffer_size_gb"] = args.inference_dynamic_batching_buffer_size_gb
@@ -165,22 +133,7 @@ def main():
 
     args = get_args()
 
-    if args.max_batch_size is not None:
-        warnings.warn(
-            f"`--max-batch-size` has been deprecated in favor of `--inference-max-requests`."
-        )
-        args.inference_max_batch_size = max(args.max_batch_size, args.inference_max_batch_size)
-
-    # Set up model and load checkpoint
-    if args.model_provider == "gpt":
-        model_builder = gpt_builder
-    elif args.model_provider == "mamba":
-        model_builder = mamba_builder
-    else:
-        raise ValueError(f"Invalid model provider {args.model_provider}")
-    model = get_model(partial(model_provider, model_builder), wrap_with_ddp=False)
-    load_checkpoint(model, None, None, strict=False)
-    model = model[0]
+    model = get_model_for_inference()
 
     inference_engine = get_inference_engine(args, model)
 
@@ -193,10 +146,9 @@ def main():
         top_n_logprobs=args.top_n_logprobs,
     )
 
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    # Build tokenizer
+    tokenizer = build_tokenizer(args)
+
     requests = build_requests(args, tokenizer)
     prompts = [r.prompt_text for r in requests]
 
@@ -276,7 +228,7 @@ def main():
                 )
             ),
             len(requests),
-            args.inference_max_batch_size,
+            args.inference_max_requests,
             stats["allocated_bytes.all.peak"] / (1024**3),
             stats["reserved_bytes.all.peak"] / (1024**3),
             latency,
@@ -293,6 +245,5 @@ def main():
         torch.distributed.destroy_process_group()
 
 
-
 if __name__ == "__main__":
     main()
diff --git a/examples/inference/gpt/utils.py b/examples/inference/gpt/utils.py
index a04b856c0a6..eceb5fa16fd 100644
--- a/examples/inference/gpt/utils.py
+++ b/examples/inference/gpt/utils.py
@@ -1,158 +1,23 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import copy
-import json
 import itertools
+import json
 import random
 import time
-import torch
 from argparse import ArgumentParser, Namespace
-from tqdm import tqdm
+from functools import partial
 from typing import Any, List, Optional
 
-from megatron.core.inference.inference_request import DynamicInferenceRequest
+import torch
+from tqdm import tqdm
+
 from megatron.core.inference.contexts import DynamicInferenceContext
 from megatron.core.inference.contexts.dynamic_context import get_mem_size_str
-from megatron.core.transformer.module import MegatronModule
-
+from megatron.core.inference.inference_request import DynamicInferenceRequest
 from megatron.core.inference.sampling_params import SamplingParams
-
-
-def add_common_inference_args(parser: ArgumentParser) -> ArgumentParser:
-    """Common inference arguments."""
-
-    group = parser.add_argument_group(title='Common inference')
-
-    group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.')
-    group.add_argument("--top_k", type=int, default=1, help='Top k sampling.')
-    group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.')
-    group.add_argument(
-        "--return-log-probs",
-        action='store_true',
-        default=False,
-        help='Return the log probabilities of the final output tokens',
-    )
-    group.add_argument(
-        "--prompts",
-        metavar='N',
-        type=str,
-        nargs='+',
-        help='Input prompts with each prompt within quotes and seperated by space',
-    )
-    group.add_argument(
-        "--num-tokens-to-prompt",
-        type=int,
-        nargs="+",
-        default=[64, 1024],
-        help='Number of tokens to use for simulated prompts. This should be a '
-        'space-separated pair of integers, and the generated prompt lengths will '
-        'be uniformly sampled within this range.',
-    )
-    group.add_argument(
-        "--num-tokens-to-generate",
-        type=int,
-        default=30,
-        help='Number of tokens to generate for each prompt',
-    )
-    group.add_argument(
-        "--num-tokens-from-file",
-        action='store_true',
-        default=False,
-        help='Use per-prompt num_tokens_to_generate from prompt file',
-    )
-    group.add_argument(
-        "--top-n-logprobs",
-        type=int,
-        default=0,
-        help='Return the top n logprobs for the generated tokens and their corresponding token as a dictionary',
-    )
-    group.add_argument(
-        "--incoming-requests-per-step",
-        type=int, default=None,
-        help="Add a deterministic number of requests per step. This arg is "
-        "prioritized over `--incoming-requests-per-sec` below (which is non-"
-        "deterministic). Note that the number of requests added per step is "
-        "additionally limited by the inference context's `max_requests`, "
-        "`max_tokens`, and KV buffer size.",
-    )
-    group.add_argument(
-        "--incoming-requests-per-sec",
-        type=float,
-        default=100.0,
-        help="Simulated number of requests per second. Set to -1 to add all requests together.",
-    )
-    group.add_argument(
-        "--incoming-requests-duration",
-        type=float,
-        default=10.0,
-        help="Total amount of time to simulate that requests are "
-        "arriving. Multiply this value with "
-        "`--incoming-requests-per-sec` to get the approximate "
-        "total number of requests. Set to -1 to add all requests together.",
-    )
-    group.add_argument(
-        "--model-provider",
-        choices=["mamba", "gpt"],
-        default="gpt",
-        help="Model provider",
-    )
-    group.add_argument(
-        "--skip-prompt-log-probs",
-        action='store_true',
-        default=False,
-        help='Skip prompt log probs.',
-    )
-    group.add_argument(
-        "--stop-words",
-        metavar='WORD',
-        type=str,
-        nargs='+',
-        default=None,
-        help='Stop words to terminate generation. Each word should be quoted and '
-        'separated by space. Example: --stop-words "\\n\\n" "END" "###"',
-    )
-    group.add_argument(
-        "--output-path",
-        type=str,
-        default=None,
-        help="Path to save generations as JSON",
-    )
-    group.add_argument(
-        "--output-every-n-results",
-        type=int,
-        default=1,
-        help="To minimize the output file size of larger runs, only write the "
-        "results of every `n` requests.",
-    )
-    group.add_argument(
-        "--prompt-file",
-        help='Jsonl file containing input prompts, where each item (i.e., line) '
-        'contains the field \'text\' where the value is the prompt. All other '
-        'fields within each item are ignored, and may be customized for each '
-        'application.',
-    )
-    group.add_argument(
-        "--prompt-file-num-truncate",
-        type=int,
-        help='Number of samples to use from the loaded prompt file (see '
-        '`--prompt-file` above). The first `--prompt-file-num-truncate` samples '
-        'will be used, in order.',
-    )
-    group.add_argument(
-        "--use-flashinfer-fused-rope",
-        action='store_true',
-        default=False,
-        help='Use flashinfer fused rope implementation.',
-    )
-    group.add_argument(
-        "--no-record-throughput",
-        action='store_false',
-        dest="record_throughput",
-        help="Disable throughput recording in --output-file"
-        
-    )
-
-    return parser
+from megatron.core.transformer.module import MegatronModule
+from megatron.training import get_args
 
 
 def get_default_sampling_params(termination_id: int = None):
@@ -162,9 +27,10 @@ def get_default_sampling_params(termination_id: int = None):
         top_p=0.0,
         return_log_probs=False,
         num_tokens_to_generate=30,
-        termination_id = termination_id,
+        termination_id=termination_id,
     )
 
+
 def get_curr_time() -> float:
     """Get synchronized time across ranks."""
     curr_time = torch.cuda.LongTensor([time.time_ns()])
@@ -188,7 +54,13 @@ class Request:
         tokenizer (Any): Tokenizer for tokenizing the prompt.
     """
 
-    def __init__(self, prompt_text: str, time_offset: float, tokenizer: Any, sampling_params: SamplingParams = None):
+    def __init__(
+        self,
+        prompt_text: str,
+        time_offset: float,
+        tokenizer: Any,
+        sampling_params: SamplingParams = None,
+    ):
         self.prompt_text = prompt_text
         self.prompt_tokens = tokenizer.tokenize(prompt_text)
         self.output_text = None
@@ -197,8 +69,13 @@ def __init__(self, prompt_text: str, time_offset: float, tokenizer: Any, samplin
         self.time_arrival = None
         self.time_start = None
         self.time_end = None
+        self.ttft = None  # Time-to-first-token in seconds
         self.state = "not-started"
-        self.sampling_params: SamplingParams = sampling_params if sampling_params is not None else get_default_sampling_params(tokenizer.eod)
+        self.sampling_params: SamplingParams = (
+            sampling_params
+            if sampling_params is not None
+            else get_default_sampling_params(tokenizer.eod)
+        )
         self.sampling_params = copy.deepcopy(self.sampling_params)
 
     def __str__(self) -> str:
@@ -225,10 +102,10 @@ def get_time_offsets(
 
     # if num_requests is not None:
     incoming_requests_duration = num_requests / incoming_requests_per_sec
-    incoming_requests_duration *= 2 # extra margin, to accomodate time sampling
+    incoming_requests_duration *= 2  # extra margin, to accomodate time sampling
 
     random.seed(seed)
-    
+
     import simpy  # Guard against this import in test case
 
     # Generate random time offsets.
@@ -241,14 +118,14 @@ def arrival(r):
     env = simpy.Environment()
     env.process(arrival(incoming_requests_per_sec))
     env.run(incoming_requests_duration)
-    
+
     # Ensure at least a single request.
     if len(time_offsets) == 0:
         time_offsets = [0.0]
 
     # Ensure first time is 0.
     time_offsets = [to - time_offsets[0] for to in time_offsets]
-    
+
     # Truncate to num_requests.
     assert len(time_offsets) >= num_requests
     time_offsets = time_offsets[:num_requests]
@@ -257,7 +134,7 @@ def arrival(r):
 
 
 def get_cli_requests(
-        args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
+    args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
 ) -> list[Request]:
 
     # Get time offsets.
@@ -269,7 +146,7 @@ def get_cli_requests(
     )
 
     # Init requests.
-    requests = [Request(p, t, tokenizer, sampling_params) for p,t in zip(args.prompts, t_offsets)]
+    requests = [Request(p, t, tokenizer, sampling_params) for p, t in zip(args.prompts, t_offsets)]
     return requests
 
 
@@ -289,18 +166,14 @@ def get_synthetic_requests(
     # Build prompts with expected lengths.
     assert (
         len(args.num_tokens_to_prompt) == 2
-        and
-        args.num_tokens_to_prompt[1] >= args.num_tokens_to_prompt[0]
+        and args.num_tokens_to_prompt[1] >= args.num_tokens_to_prompt[0]
     )
     max_prompt_length = args.num_tokens_to_prompt[1]
     max_prompt_text = "hi " * max_prompt_length
     max_prompt_tokens = tokenizer.tokenize(max_prompt_text)
-    prompt_lengths = [
-        random.randint(*args.num_tokens_to_prompt)
-        for _ in time_offsets
-    ]
-    prompt_tokens_list = [ max_prompt_tokens[:l] for l in prompt_lengths ]
-    prompt_texts = [ tokenizer.detokenize(tt) for tt in prompt_tokens_list ]
+    prompt_lengths = [random.randint(*args.num_tokens_to_prompt) for _ in time_offsets]
+    prompt_tokens_list = [max_prompt_tokens[:l] for l in prompt_lengths]
+    prompt_texts = [tokenizer.detokenize(tt) for tt in prompt_tokens_list]
 
     # Init requests.
     assert len(prompt_texts) == len(time_offsets)
@@ -340,16 +213,15 @@ def get_requests_from_file(
 
     # Get time offsets.
     time_offsets: list[float] = get_time_offsets(
-        args.seed,
-        args.incoming_requests_per_step,
-        args.incoming_requests_per_sec,
-        len(prompts),
+        args.seed, args.incoming_requests_per_step, args.incoming_requests_per_sec, len(prompts)
     )
 
     # Init requests.
     requests = [
         Request(p, t, tokenizer, sp)
-        for p, t, sp in tqdm(zip(prompts, time_offsets, sampling_params_list), "init requests", total=len(prompts))
+        for p, t, sp in tqdm(
+            zip(prompts, time_offsets, sampling_params_list), "init requests", total=len(prompts)
+        )
     ]
 
     return requests
@@ -411,19 +283,21 @@ def build_dynamic_engine_setup_prefix(
 
     # Prompt description
     prompt_src_str = (
-        "cli" if args.prompts else
-        "file" if args.prompt_file else
-        f"synth({', '.join(map(str, args.num_tokens_to_prompt))})"
+        "cli"
+        if args.prompts
+        else (
+            "file"
+            if args.prompt_file
+            else f"synth({', '.join(map(str, args.num_tokens_to_prompt))})"
+        )
     )
     request_str = (
-        f"requests: {prompt_src_str}, "
-        f"n {len(requests):d}, g {args.num_tokens_to_generate:d}, "
+        f"requests: {prompt_src_str}, " f"n {len(requests):d}, g {args.num_tokens_to_generate:d}, "
     )
     request_str += (
-        f"dur {args.incoming_requests_duration:.1e} "
-        f"r/sec {args.incoming_requests_per_sec:.1e}"
-        if args.incoming_requests_per_step is None else
-        f"r/step {args.incoming_requests_per_step}"
+        f"dur {args.incoming_requests_duration:.1e} " f"r/sec {args.incoming_requests_per_sec:.1e}"
+        if args.incoming_requests_per_step is None
+        else f"r/step {args.incoming_requests_per_step}"
     )
 
     # Buffer limits config
@@ -433,14 +307,7 @@ def build_dynamic_engine_setup_prefix(
         f"[r {context.max_requests}, t {context.max_tokens}]"
     )
 
-    parts = [
-        get_model_size_str(model),
-        "dynamic",
-        cg_str,
-        uvm_str,
-        request_str,
-        buffer_limits_str,
-    ]
+    parts = [get_model_size_str(model), "dynamic", cg_str, uvm_str, request_str, buffer_limits_str]
 
     return " | ".join(parts)
 
@@ -456,4 +323,4 @@ def get_global_peak_memory_stats_bytes() -> dict:
         t = torch.tensor([peak_alloc], device="cuda", dtype=torch.int64)
         torch.distributed.all_reduce(t, op=torch.distributed.ReduceOp.MAX)
         peak_alloc = int(t[0].item())
-    return {"mem-max-allocated-bytes": peak_alloc}
\ No newline at end of file
+    return {"mem-max-allocated-bytes": peak_alloc}
diff --git a/examples/inference/t5/simple_t5_batch_inference.py b/examples/inference/t5/simple_t5_batch_inference.py
index 4b15952e07f..1aca74b3176 100644
--- a/examples/inference/t5/simple_t5_batch_inference.py
+++ b/examples/inference/t5/simple_t5_batch_inference.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
 import os
 import sys
 from argparse import Namespace
@@ -17,7 +19,7 @@
 from megatron.core.inference.text_generation_controllers.encoder_decoder_text_generation_controller import (
     EncoderDecoderTextGenerationController,
 )
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.transformer.module import MegatronModule
 from pretrain_t5 import model_provider
 
@@ -57,7 +59,7 @@ def add_text_generate_args(parser):
         metavar='N',
         type=str,
         nargs='+',
-        help='Encoder input prompts with each prompt within quotes and seperated by space',
+        help='Encoder input prompts with each prompt within quotes and separated by space',
     )
     group.add_argument(
         "--max-batch-size", type=int, default=1, help='Max number of prompts to process at once'
@@ -77,10 +79,8 @@ def get_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngi
     Returns:
         AbstractBackend: The chosen backend
     """
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    # Build tokenizer
+    tokenizer = build_tokenizer(args)
 
     inference_wrapper_config = InferenceWrapperConfig(
         hidden_size=args.hidden_size,
@@ -131,10 +131,9 @@ def main():
         num_tokens_to_generate=args.num_tokens_to_generate,
     )
 
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    # Build tokenizer
+    tokenizer = build_tokenizer(args)
+
     decoder_prompts = [""] * len(
         args.encoder_prompts
     )  # for T5, the prompt is provided as encoder input, hence decoder_prompts is empty
diff --git a/examples/llama/train_llama3_8b_h100_fp8.sh b/examples/llama/train_llama3_8b_h100_fp8.sh
index f791996308e..28227546bc7 100644
--- a/examples/llama/train_llama3_8b_h100_fp8.sh
+++ b/examples/llama/train_llama3_8b_h100_fp8.sh
@@ -69,6 +69,7 @@ MODEL_ARGS=(
     --attention-dropout 0.0
     --hidden-dropout 0.0
     --swiglu
+    --normalization RMSNorm
     --init-method-std 0.0134
     --attention-backend fused
     --apply-layernorm-1p 
diff --git a/examples/mimo/data/energon_avlm_task_encoder.py b/examples/mimo/data/energon_avlm_task_encoder.py
index 32afb1b2cfb..a6a86761720 100644
--- a/examples/mimo/data/energon_avlm_task_encoder.py
+++ b/examples/mimo/data/energon_avlm_task_encoder.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 import argparse
 import logging
 import os
@@ -39,7 +41,6 @@
 )
 from megatron.energon.task_encoder.base import stateless
 from megatron.training import get_args
-from megatron.training.tokenizer.multimodal_tokenizer import mistral_custom_template
 
 IMAGE_TOKEN = "<image>"
 AUDIO_TOKEN = "<audio>"
diff --git a/examples/mimo/data/energon_vlm_task_encoder.py b/examples/mimo/data/energon_vlm_task_encoder.py
index c49bfbff52f..7767f7e80e5 100644
--- a/examples/mimo/data/energon_vlm_task_encoder.py
+++ b/examples/mimo/data/energon_vlm_task_encoder.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 import argparse
 import logging
 import os
@@ -34,7 +36,6 @@
 )
 from megatron.energon.task_encoder.base import stateless
 from megatron.training import get_args
-from megatron.training.tokenizer.multimodal_tokenizer import mistral_custom_template
 
 
 @dataclass
diff --git a/examples/multimodal/energon_util.py b/examples/multimodal/energon_util.py
index 360f97a6a54..8406879ecec 100644
--- a/examples/multimodal/energon_util.py
+++ b/examples/multimodal/energon_util.py
@@ -1,12 +1,9 @@
 # Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
 import torch
-import warnings
 from dataclasses import dataclass
 from typing import Any, List
 
 from megatron.energon import Sample
-from megatron.energon.epathlib.epath import EPath
-from megatron.energon.flavors.webdataset import DefaultDecoderWebdatasetFactory
 
 
 @dataclass
@@ -20,23 +17,6 @@ class SampleListSample(Sample):
     samples: List[Any]
 
 
-class SampleListWebdataset(DefaultDecoderWebdatasetFactory[SampleListSample]):
-    __sample_type__ = SampleListSample
-
-    def __init__(self, path: EPath, **kwargs):
-        warnings.warn(
-            f"{type(self)} is deprecated, use the default instead and set the sample_type:\n"
-            f"To convert, update your {path}/.nv-meta/dataset.yaml to:\n"
-            f"# remove top-level __module__ and __class__\n"
-            f"sample_type:\n"
-            f"  __module__: megatron.energon\n"
-            f"  __class__: {self.__sample_type__.__name__}\n"
-            f"# Keep the remaining content",
-            DeprecationWarning,
-        )
-        super().__init__(path, **kwargs)
-
-
 @dataclass
 class OfflineTargetAspectRatioSample(Sample):
     """Sample type for image + text samples with target aspect ratio computed offline."""
diff --git a/examples/multimodal/evaluation/evaluate_mmmu.py b/examples/multimodal/evaluation/evaluate_mmmu.py
index 90cf141cd54..2bdc2ebbdc5 100644
--- a/examples/multimodal/evaluation/evaluate_mmmu.py
+++ b/examples/multimodal/evaluation/evaluate_mmmu.py
@@ -1,3 +1,4 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 import argparse
 import glob
 import json
@@ -104,7 +105,7 @@ def mmmu_eval(input_path, groundtruth_path):
     print(output.stderr)
     print(output.stdout)
 
-    m = re.search("'Overall': {'num': \d+, 'acc': (\d.\d+)}", output.stdout)
+    m = re.search(r"'Overall': {'num': \d+, 'acc': (\d\.\d+)}", output.stdout)
 
     return float(m.group(1)) * 100.0
 
diff --git a/examples/multimodal/layer_scaling.py b/examples/multimodal/layer_scaling.py
index a82afa7cc5e..b3d2881b315 100644
--- a/examples/multimodal/layer_scaling.py
+++ b/examples/multimodal/layer_scaling.py
@@ -4,6 +4,8 @@
 import torch
 
 from megatron.core.transformer.transformer_layer import TransformerLayer
+from megatron.core.typed_torch import copy_signature
+
 
 def _bias_dropout_add_func_layer_scaling(ls, x_with_bias, residual, prob, training):
     x, bias = x_with_bias  # unpack
@@ -36,6 +38,7 @@ def get_bias_dropout_add_layer_scaling(ls, training, fused):
 
 # Add LayerScaling to our default TransformerLayer.
 class LayerScalingTransformerLayer(TransformerLayer):
+    @copy_signature(TransformerLayer.__init__)
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.ls1 = torch.nn.Parameter(torch.ones(self.config.hidden_size))
diff --git a/examples/multimodal/layer_specs.py b/examples/multimodal/layer_specs.py
index 56821f2cec6..24f6b2f19df 100644
--- a/examples/multimodal/layer_specs.py
+++ b/examples/multimodal/layer_specs.py
@@ -77,7 +77,7 @@ def get_layer_spec(is_vit, normalization) -> ModuleSpec:
     return ModuleSpec(
         module=TransformerLayer,
         submodules=TransformerLayerSubmodules(
-            input_layernorm=norm,
+            input_layernorm=not_none(norm),
             self_attention=ModuleSpec(
                 module=SelfAttention,
                 params={"attn_mask_type": attn_mask_type},
@@ -90,7 +90,7 @@ def get_layer_spec(is_vit, normalization) -> ModuleSpec:
                 ),
             ),
             self_attn_bda=get_bias_dropout_add,
-            pre_mlp_layernorm=norm,
+            pre_mlp_layernorm=not_none(norm),
             mlp=mlp,
             mlp_bda=get_bias_dropout_add,
         ),
@@ -175,8 +175,8 @@ def get_mamba_layer_spec_te(padding=False) -> ModuleSpec:
                     mlp=ModuleSpec(
                         module=MLP,
                         submodules=MLPSubmodules(
-                            linear_fc1=TELayerNormColumnParallelLinear,
-                            linear_fc2=TERowParallelLinear,
+                            linear_fc1=not_none(TELayerNormColumnParallelLinear),
+                            linear_fc2=not_none(TERowParallelLinear),
                         ),
                     ),
                     mlp_bda=get_bias_dropout_add,
@@ -191,8 +191,8 @@ def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec:
     return ModuleSpec(
         module=MLP,
         submodules=MLPSubmodules(
-            linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
-            linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
+            linear_fc1=not_none(TEColumnParallelLinear) if use_te else ColumnParallelLinear,
+            linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear,
         ),
     )
 
@@ -201,6 +201,7 @@ def get_norm_mlp_module_spec_te() -> ModuleSpec:
     return ModuleSpec(
         module=MLP,
         submodules=MLPSubmodules(
-            linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
+            linear_fc1=not_none(TELayerNormColumnParallelLinear),
+            linear_fc2=not_none(TERowParallelLinear),
         ),
     )
diff --git a/examples/multimodal/model.py b/examples/multimodal/model.py
index 212a2cda021..e5aeb221892 100644
--- a/examples/multimodal/model.py
+++ b/examples/multimodal/model.py
@@ -16,7 +16,8 @@
 
 
 def model_provider(
-    pre_process=True, post_process=True, add_encoder=True, add_decoder=True, parallel_output=True
+    pre_process=True, post_process=True, add_encoder=True, add_decoder=True, parallel_output=True,
+    vp_stage=None, config=None, pg_collection=None
 ) -> LLaVAModel:
     """Builds the model.
 
@@ -28,6 +29,9 @@ def model_provider(
         add_decoder (bool): Construct the decoder module (used with pipeline parallelism). Defaults to True. When we use pipelining, the decoder
             will live on only a subset of the pipeline stages (specifically, every stage after the first one).
         parallel_output (bool): Enable parallel model output.
+        vp_stage: Optional virtual pipeline stage. Used with virtual pipeline parallelism.
+        config: Optional transformer config. If None, will be created from args.
+        pg_collection: Optional process group collection. If None, will use default.
 
     Returns:
         model: A multimodal model.
diff --git a/examples/multimodal/nvlm/internvit.py b/examples/multimodal/nvlm/internvit.py
index 9df9af23f05..0018bb5ccb9 100644
--- a/examples/multimodal/nvlm/internvit.py
+++ b/examples/multimodal/nvlm/internvit.py
@@ -34,10 +34,11 @@
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
 from megatron.core.transformer.module import MegatronModule
-from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
+from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
 from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint
+from megatron.core.typed_torch import copy_signature, not_none
 from megatron.core.utils import divide
 
 try:
@@ -61,7 +62,7 @@ class InternViTRMSNorm(MegatronModule):
 
     def __init__(
         self,
-        config,
+        config: TransformerConfig,
         hidden_size: int,
         eps: float = 1e-6,
         sequence_parallel: bool = False,
@@ -93,7 +94,7 @@ def _norm(self, x, var):
 
         return x * torch.rsqrt(var + self.eps)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Run RMSNorm with an option to compute custom statistic."""
         var = None
         if self._compute_var:
@@ -172,6 +173,7 @@ def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec:
 
 # Override a few things that are special in InternViT and not supported by the SelfAttention class.
 class InternViTSelfAttention(SelfAttention):
+    @copy_signature(SelfAttention.__init__)
     def __init__(
         self, config: TransformerConfig, submodules: SelfAttentionSubmodules, *args, **kwargs
     ):
@@ -184,7 +186,7 @@ def __init__(
             self.config.hidden_size,
             self.query_projection_size + 2 * self.kv_projection_size,
             config=self.config,
-            init_method=self.config.init_method,
+            init_method=not_none(self.config.init_method),
             gather_output=False,
             bias=qkv_bias,
             skip_bias_add=False,
@@ -196,26 +198,23 @@ def __init__(
             self.hidden_size_per_attention_head * self.num_attention_heads_per_partition
         )  # 512 for internvit
 
-        self.q_layernorm = build_module(
-            submodules.q_layernorm,
+        self.q_layernorm = not_none(submodules.q_layernorm)(
             hidden_size=qk_layernorm_hidden_size,
             config=self.config,
             eps=self.config.layernorm_epsilon,
-            compute_var=True,
         )
 
-        self.k_layernorm = build_module(
-            submodules.k_layernorm,
+        self.k_layernorm = not_none(submodules.k_layernorm)(
             hidden_size=qk_layernorm_hidden_size,
             config=self.config,
             eps=self.config.layernorm_epsilon,
-            compute_var=True,
         )
 
 
 class InternViTTEDotProductAttention(TEDotProductAttention):
     """Adjusted Attention for InternViT"""
 
+    @copy_signature(TEDotProductAttention.forward)
     def forward(self, *args, **kwargs):
         """Regular TEDotProductAttention + zero-out dummy attention heads."""
         out = super().forward(*args, **kwargs)
@@ -249,8 +248,8 @@ def get_internvit_layer_spec(use_te) -> ModuleSpec:
                     linear_qkv=TEColumnParallelLinear if use_te else ColumnParallelLinear,
                     core_attention=TEDotProductAttention if use_te else DotProductAttention,
                     linear_proj=TERowParallelLinear if use_te else RowParallelLinear,
-                    q_layernorm=InternViTRMSNorm,
-                    k_layernorm=InternViTRMSNorm,
+                    q_layernorm=partial(InternViTRMSNorm, compute_var=True),
+                    k_layernorm=partial(InternViTRMSNorm, compute_var=True),
                 ),
             ),
             self_attn_bda=get_bias_dropout_add_layer_scaling,
diff --git a/examples/multimodal/radio/radio_g.py b/examples/multimodal/radio/radio_g.py
index f139632df86..d39a9083722 100644
--- a/examples/multimodal/radio/radio_g.py
+++ b/examples/multimodal/radio/radio_g.py
@@ -58,8 +58,8 @@ def get_mlp_module_spec(use_te: bool = True) -> ModuleSpec:
     return ModuleSpec(
         module=MLP,
         submodules=MLPSubmodules(
-            linear_fc1=TEColumnParallelLinear if use_te else ColumnParallelLinear,
-            linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
+            linear_fc1=not_none(TEColumnParallelLinear) if use_te else ColumnParallelLinear,
+            linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear,
         ),
     )
 
@@ -68,7 +68,8 @@ def get_norm_mlp_module_spec_te() -> ModuleSpec:
     return ModuleSpec(
         module=MLP,
         submodules=MLPSubmodules(
-            linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
+            linear_fc1=not_none(TELayerNormColumnParallelLinear),
+            linear_fc2=not_none(TERowParallelLinear),
         ),
     )
 
@@ -93,7 +94,7 @@ def get_radio_g_layer_spec(normalization) -> ModuleSpec:
     return ModuleSpec(
         module=LayerScalingTransformerLayer,
         submodules=TransformerLayerSubmodules(
-            input_layernorm=norm,
+            input_layernorm=not_none(norm),
             self_attention=ModuleSpec(
                 module=SelfAttention,
                 params={"attn_mask_type": attn_mask_type},
@@ -106,7 +107,7 @@ def get_radio_g_layer_spec(normalization) -> ModuleSpec:
                 ),
             ),
             self_attn_bda=get_bias_dropout_add_layer_scaling,
-            pre_mlp_layernorm=norm,
+            pre_mlp_layernorm=not_none(norm),
             mlp=mlp,
             mlp_bda=get_bias_dropout_add_layer_scaling,
         ),
diff --git a/examples/post_training/modelopt/conf/arguments.sh b/examples/post_training/modelopt/conf/arguments.sh
index f17b8c9154a..826ce312ae3 100644
--- a/examples/post_training/modelopt/conf/arguments.sh
+++ b/examples/post_training/modelopt/conf/arguments.sh
@@ -1,7 +1,12 @@
 #!/bin/bash
 set -e
 
-MLM_MODEL_CFG=$1
+MLM_MODEL_CFG=${1}
+
+if [ -z $1 ]; then
+    printf "${MLM_ERROR} Model configuration name must be provided as the first argument (e.g. 'meta-llama/Llama-3.2-1B-Instruct')\n"
+    exit 1
+fi
 
 # Bash coloring
 RED='\033[0;31m'
diff --git a/examples/post_training/modelopt/convert_model.py b/examples/post_training/modelopt/convert_model.py
index cf5f6e5bbbb..d8acd49ee92 100644
--- a/examples/post_training/modelopt/convert_model.py
+++ b/examples/post_training/modelopt/convert_model.py
@@ -2,6 +2,7 @@
 
 """Convert a GPTModel."""
 import functools
+import inspect
 import json
 import os
 import sys
@@ -20,11 +21,10 @@
 from megatron.post_training.checkpointing import load_modelopt_checkpoint
 from megatron.post_training.model_builder import modelopt_gpt_mamba_builder
 from megatron.post_training.utils import (
-    modelopt_version_at_least,
     report_current_memory_info,
     to_empty_if_meta,
 )
-from megatron.training import get_args, get_tokenizer
+from megatron.training import get_args
 from megatron.training.checkpointing import save_checkpoint
 from megatron.training.initialize import initialize_megatron
 from megatron.training.utils import print_rank_0, unwrap_model
@@ -91,8 +91,9 @@ def check_arguments():
         exit()
 
     if hasattr(args, 'moe_grouped_gemm') and args.moe_grouped_gemm == True:
-        print_rank_0("WARNING: Forcing moe_grouped_gemm to False for PTQ and export.")
-        args.moe_grouped_gemm = False
+        if not getattr(args, 'export_default_te_spec', False):
+            print_rank_0("WARNING: Forcing moe_grouped_gemm to False for PTQ and export.")
+            args.moe_grouped_gemm = False
 
 
 if __name__ == "__main__":
@@ -140,7 +141,7 @@ def check_arguments():
             "dtype": import_dtype,
             "moe_router_dtype": args.moe_router_dtype,
         }
-        if modelopt_version_at_least("0.41.0"):
+        if "trust_remote_code" in inspect.signature(import_mcore_gpt_from_hf).parameters:
             import_kwargs.update({"trust_remote_code": args.trust_remote_code})
         import_mcore_gpt_from_hf(
             unwrapped_model, args.pretrained_model_path, workspace_dir, **import_kwargs
diff --git a/examples/post_training/modelopt/export.py b/examples/post_training/modelopt/export.py
index 9dc66eecb6d..40b33948453 100755
--- a/examples/post_training/modelopt/export.py
+++ b/examples/post_training/modelopt/export.py
@@ -2,6 +2,7 @@
 
 """Export a GPTModel."""
 import functools
+import inspect
 import os
 import sys
 import warnings
@@ -15,7 +16,6 @@
 from megatron.post_training.arguments import add_modelopt_args
 from megatron.post_training.checkpointing import load_modelopt_checkpoint
 from megatron.post_training.model_builder import modelopt_gpt_mamba_builder
-from megatron.post_training.utils import modelopt_version_at_least
 from megatron.training import get_args, get_model
 from megatron.training.initialize import initialize_megatron
 from megatron.training.utils import unwrap_model
@@ -96,6 +96,6 @@ def add_modelopt_export_args(parser):
         "export_dir": args.export_dir,
         "moe_router_dtype": unwrapped_model.config.moe_router_dtype,
     }
-    if modelopt_version_at_least("0.41.0"):
+    if "trust_remote_code" in inspect.signature(mtex.export_mcore_gpt_to_hf).parameters:
         export_kwargs.update({"trust_remote_code": args.trust_remote_code})
     mtex.export_mcore_gpt_to_hf(unwrapped_model, args.pretrained_model_name, **export_kwargs)
diff --git a/examples/post_training/modelopt/finetune.py b/examples/post_training/modelopt/finetune.py
index 19ece4ef299..20777ba14c8 100755
--- a/examples/post_training/modelopt/finetune.py
+++ b/examples/post_training/modelopt/finetune.py
@@ -444,7 +444,7 @@ def get_batch(data_iterator):
 def non_loss_data_func(model: GPTModel):
     """Callback to compute the acceptance length."""
     args = get_args()
-    if not args.export_offline_model:
+    if not args.export_offline_model and args.context_parallel_size == 1:
         try:
             report_draft_acceptance_length(model)
         except Exception as e:
diff --git a/examples/post_training/modelopt/finetune.sh b/examples/post_training/modelopt/finetune.sh
index e7ba0f022dc..1affc3c04da 100755
--- a/examples/post_training/modelopt/finetune.sh
+++ b/examples/post_training/modelopt/finetune.sh
@@ -38,6 +38,12 @@ if [ -z ${MLM_DATA_ARGS} ]; then
     "
 fi
 
+if [[ -v CP && "$CP" != "1" ]]; then
+  BACKEND="fused"
+else
+  BACKEND="auto"
+fi
+
 if [ -z ${MLM_TRAIN_ARGS} ]; then
     MLM_TRAIN_ARGS=" \
         --no-gradient-accumulation-fusion \
@@ -48,6 +54,7 @@ if [ -z ${MLM_TRAIN_ARGS} ]; then
         --attention-dropout 0.0 \
         --hidden-dropout 0.0 \
         --no-check-for-nan-in-loss-and-grad \
+        --attention-backend ${BACKEND} \
     "
 fi
 
@@ -83,6 +90,7 @@ ${LAUNCH_SCRIPT} ${SCRIPT_DIR}/finetune.py \
     --expert-model-parallel-size ${EP} \
     --pipeline-model-parallel-size ${PP} \
     --context-parallel-size ${CP} \
+    --cp-comm-type p2p \
     --tokenizer-model ${TOKENIZER_MODEL} \
     --load ${MLM_MODEL_CKPT} \
     --save ${MLM_MODEL_SAVE} \
diff --git a/examples/post_training/modelopt/generate.py b/examples/post_training/modelopt/generate.py
index 63d3f241f59..3c35dc76711 100644
--- a/examples/post_training/modelopt/generate.py
+++ b/examples/post_training/modelopt/generate.py
@@ -122,6 +122,8 @@ def get_conversations(example):
         dataset = load_dataset(args.finetune_hf_dataset, split=args.finetune_data_split)
 
     tokenizer = get_tokenizer()._tokenizer
+    if hasattr(tokenizer, "tokenizer"):
+        tokenizer = tokenizer.tokenizer
 
 
     if args.load is not None:
diff --git a/examples/post_training/modelopt/mmlu.py b/examples/post_training/modelopt/mmlu.py
index d475ac9fb30..bad621341dc 100644
--- a/examples/post_training/modelopt/mmlu.py
+++ b/examples/post_training/modelopt/mmlu.py
@@ -169,6 +169,8 @@ def generate_prompt(test_example, dev_examples, few_shots=0, no_subject_prompt=F
     disable_tqdm = args.disable_tqdm or torch.distributed.get_rank() > 0
 
     tokenizer = get_tokenizer()._tokenizer
+    if hasattr(tokenizer, "tokenizer"):
+        tokenizer = tokenizer.tokenizer
 
     if args.load is not None:
         load_modelopt_checkpoint(model, strict=not args.untie_embeddings_and_output_weights)
@@ -177,7 +179,10 @@ def generate_prompt(test_example, dev_examples, few_shots=0, no_subject_prompt=F
     # Fold the scalars into weight for speedup.
     # [TODO]: fold_weight current assumes all weight_quantizer has weight allocated;
     # however, this is not the case when share_embeddings_and_output_weights is False.
-    if not getattr(unwrapped_model, "share_embeddings_and_output_weights", False):
+    # [TODO]: fold_weight does not support TEGroupedMLP (QuantTEColumnParallelGroupedLinear)
+    # which stores per-expert weights as weight0, weight1, etc. instead of a single weight.
+    has_grouped_mlp = any("TEGroupedMLP" in type(m).__name__ for m in unwrapped_model.modules())
+    if not getattr(unwrapped_model, "share_embeddings_and_output_weights", False) and not has_grouped_mlp:
         mtq.fold_weight(unwrapped_model)
 
     all_subjects = get_all_subjects()
diff --git a/examples/post_training/modelopt/prune.py b/examples/post_training/modelopt/prune.py
index 2671b6badd9..79ec931b550 100644
--- a/examples/post_training/modelopt/prune.py
+++ b/examples/post_training/modelopt/prune.py
@@ -6,6 +6,7 @@
 """
 
 import functools
+import inspect
 import os
 import sys
 import warnings
@@ -29,7 +30,6 @@
 from megatron.post_training.generate import simple_generate
 from megatron.post_training.model_builder import modelopt_gpt_mamba_builder
 from megatron.post_training.utils import (
-    modelopt_version_at_least,
     report_current_memory_info,
 )
 from megatron.training import get_args, get_model, get_tokenizer, initialize_megatron
@@ -161,6 +161,8 @@ def get_params(model):
     check_arguments(args)
 
     tokenizer = get_tokenizer()._tokenizer
+    if hasattr(tokenizer, "tokenizer"):
+        tokenizer = tokenizer.tokenizer
     model = get_model(
         functools.partial(model_provider, modelopt_gpt_mamba_builder), wrap_with_ddp=False
     )
@@ -176,7 +178,7 @@ def get_params(model):
         import_dtype = torch.float16 if args.fp16 else torch.bfloat16
         workspace_dir = os.environ.get("MLM_WORK_DIR", "/tmp")
         import_kwargs = {"dtype": import_dtype}
-        if modelopt_version_at_least("0.41.0"):
+        if "trust_remote_code" in inspect.signature(import_mcore_gpt_from_hf).parameters:
             import_kwargs.update({"trust_remote_code": args.trust_remote_code})
         import_mcore_gpt_from_hf(
             unwrapped_model, args.pretrained_model_path, workspace_dir, **import_kwargs
diff --git a/examples/post_training/modelopt/quantize.py b/examples/post_training/modelopt/quantize.py
index ceedce606a5..cfe7f9a8eb9 100644
--- a/examples/post_training/modelopt/quantize.py
+++ b/examples/post_training/modelopt/quantize.py
@@ -4,19 +4,20 @@
 
 import copy
 import functools
+import inspect
+import json
 import os
+import random
 import sys
 import warnings
 
+import modelopt.torch.quantization as mtq
 import torch
 import torch.distributed
-from datasets import load_dataset
+from modelopt.torch.export import import_mcore_gpt_from_hf
+from modelopt.torch.utils.dataset_utils import get_dataset_dataloader
 from tqdm import tqdm
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../")))
-
-import modelopt.torch.quantization as mtq
-
 try:
     import modelopt.torch.quantization.plugins.psx_formats as mtq_psx
 except ImportError:
@@ -24,24 +25,20 @@
     warnings.warn(
         "psx_formats is not installed. PSX formats quantization configs will not be available."
     )
-
 try:
     import modelopt.torch.quantization.plugins.luts as mtq_luts
 except ImportError:
     mtq_luts = None
     warnings.warn("luts is not installed. LUTs quantization configs will not be available.")
 
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../")))
 
-from modelopt.torch.export import import_mcore_gpt_from_hf
-
-from megatron.core import parallel_state
-from megatron.core.transformer.moe.router import TopKRouter
+from megatron.core.utils import get_batch_on_this_cp_rank
 from megatron.post_training.arguments import add_modelopt_args
 from megatron.post_training.checkpointing import load_modelopt_checkpoint
 from megatron.post_training.generate import simple_generate
 from megatron.post_training.model_builder import modelopt_gpt_mamba_builder
 from megatron.post_training.utils import (
-    modelopt_version_at_least,
     print_distributed_quant_summary,
     report_current_memory_info,
 )
@@ -78,13 +75,27 @@ def add_text_generate_ptq_args(parser):
     """Add additional arguments for ModelOpt text generation PTQ."""
     group = parser.add_argument_group(title="ModelOpt text generation ptq")
     group.add_argument(
-        "--calib-size", type=int, default=512, help="Samples to use for ptq calibration."
+        "--calib-size", type=int, default=512, help="Number of samples to use for ptq calibration."
     )
     group.add_argument(
-        "--calib-dataset",
+        "--calib-dataset-path-or-name",
         type=str,
-        default="abisee/cnn_dailymail",
-        help="The default clibration dataset is cnn_dailymail from HF hub.",
+        default="cnn_dailymail",
+        help="Path to local calibration dataset file (.jsonl) or HuggingFace dataset name.",
+    )
+    group.add_argument(
+        "--calib-max-sequence-length",
+        type=int,
+        default=512,
+        help="Maximum sequence length for calibration.",
+    )
+    group.add_argument(
+        "--calib-use-random-offset",
+        action="store_true",
+        help="Use random offsets when slicing sequences for calibration. (Only for local files)",
+    )
+    group.add_argument(
+        "--calib-batch-size", type=int, default=1, help="Batch size for calibration."
     )
     group.add_argument(
         "--prompts",
@@ -200,7 +211,10 @@ def get_last_layers_disabled_config(config, num_layers: int = 1, num_layers_to_d
 def get_modelopt_torch_quantization_config():
     """Return a quantization config."""
     args = get_args()
+    if args.export_quant_cfg not in QUANT_CFG_CHOICES:
+        raise ValueError(f"Unsupported quantization config {args.export_quant_cfg}.")
     mtq_config = QUANT_CFG_CHOICES[args.export_quant_cfg]
+
     fp8_config = {"enable": True, "num_bits": (4, 3), "axis": None}
     fp4_config = {
         "num_bits": (2, 1),
@@ -208,13 +222,13 @@ def get_modelopt_torch_quantization_config():
         "axis": None,
         "enable": True,
     }
-    if args.export_quant_cfg == "fp8":
+    if args.export_quant_cfg == "FP8_DEFAULT_CFG":
         # Enable Medusa heads and kv-cache quantization
         mtq_config["quant_cfg"]["*medusa_heads**"] = fp8_config
-    if "fp4" in args.export_quant_cfg:
+    if "FP4" in args.export_quant_cfg:
         # Enable Medusa heads and kv-cache quantization
         mtq_config["quant_cfg"]["*medusa_heads**"] = fp4_config
-    if "awq" in args.export_quant_cfg:
+    if "AWQ" in args.export_quant_cfg:
         weight_quantizer = mtq_config["quant_cfg"]["*weight_quantizer"]  # type: ignore
         if isinstance(weight_quantizer, list):
             weight_quantizer = weight_quantizer[0]
@@ -251,15 +265,75 @@ def get_modelopt_torch_quantization_config():
     return mtq_config
 
 
-def get_calib_dataloader(calib_size=512, max_sequence_length=512):
-    """Return a dataloader for calibration."""
-    args = get_args()
-    dataset = load_dataset(args.calib_dataset, name="3.0.0", split="train")
-    text_column = "article"
+def get_calib_dataloader(
+    dataset_path_or_name,
+    tokenizer,
+    calib_size=512,
+    max_sequence_length=512,
+    use_random_offset=False,
+    batch_size=1,
+):
+    """Return a dataloader/iterator for calibration using SFT or HF datasets.
 
-    calib_size = min(len(dataset), calib_size)
-    for i in range(calib_size):
-        yield dataset[i][text_column][:max_sequence_length]
+    Supports either a local path (.jsonl) or a HuggingFace dataset name.
+    """
+    if os.path.isfile(dataset_path_or_name):
+        # Local file
+        print_rank_0(f"Loading calibration dataset from local file: {dataset_path_or_name}")
+        all_texts = []
+        with open(dataset_path_or_name) as f:
+            for i, line in enumerate(f):
+                if len(all_texts) == calib_size:
+                    break
+                sample = json.loads(line)
+
+                # Extract text field from various possible keys
+                if isinstance(sample, dict) and "text" in sample:
+                    if not sample["text"]:
+                        warnings.warn(f"Sample {i} has empty text, skipping")
+                        continue
+                    full_text = sample["text"]
+                elif isinstance(sample, dict) and "messages" in sample:
+                    conversations = sample["messages"]
+                    assert "role" in conversations[0] and "content" in conversations[0]
+                    full_text = "".join([f"{msg['role']}: {msg['content']}" for msg in conversations])
+                elif isinstance(sample, list) and isinstance(sample[0], dict):
+                    assert "role" in sample[0] and "content" in sample[0]
+                    full_text = "".join([f"{msg['role']}: {msg['content']}" for msg in sample])
+                else:
+                    raise ValueError(f"Sample {i} has unexpected format")
+
+                # Slice text
+                max_text_length = int(max_sequence_length / 0.75)  # tokenized text is roughtly ~75% length of original
+                start_idx = 0
+                if use_random_offset and len(full_text) > max_text_length:
+                    start_idx = random.randint(0, len(full_text) - max_text_length)
+                text = full_text[start_idx : start_idx + max_text_length]
+                all_texts.append(text)
+
+        print_rank_0(f"Loaded calibration dataset ({dataset_path_or_name}) with {len(all_texts)} samples")
+        print_rank_0(f"Actual num samples: {len(all_texts)}, max seq length: {max_sequence_length}")
+        print_rank_0(f"Sampling Strategy: {'Random Index' if use_random_offset else 'From Beginning'}")
+
+        # Tokenize all texts at once and move to device
+        tokens = tokenizer(
+            all_texts, return_tensors="pt", padding="max_length", max_length=max_sequence_length, truncation=True
+        )
+        all_input_ids = tokens.input_ids.cuda()
+        return [{"input_ids": all_input_ids[i:i+batch_size]} for i in range(0, len(all_input_ids), batch_size)]
+    else:
+        # HuggingFace dataset
+        if use_random_offset:
+            warnings.warn("Random offset is not supported for HuggingFace datasets.")
+        print_rank_0(f"Loading calibration dataset from HuggingFace: {dataset_path_or_name}")
+        return get_dataset_dataloader(
+            dataset_name=dataset_path_or_name,
+            tokenizer=tokenizer,
+            num_samples=calib_size,
+            max_sample_length=max_sequence_length,
+            batch_size=batch_size,
+            device="cuda",
+        )
 
 
 if __name__ == "__main__":
@@ -277,6 +351,8 @@ def get_calib_dataloader(calib_size=512, max_sequence_length=512):
     args = get_args()
 
     tokenizer = get_tokenizer()._tokenizer
+    if hasattr(tokenizer, "tokenizer"):
+        tokenizer = tokenizer.tokenizer
     model = get_model(
         functools.partial(model_provider, modelopt_gpt_mamba_builder), wrap_with_ddp=False
     )
@@ -293,8 +369,9 @@ def get_calib_dataloader(calib_size=512, max_sequence_length=512):
         import_dtype = torch.float16 if args.fp16 else torch.bfloat16
         unwrapped_model = unwrap_model(model)[0]
         workspace_dir = os.environ.get("MLM_WORK_DIR", "/tmp")
+
         import_kwargs = {"dtype": import_dtype}
-        if modelopt_version_at_least("0.41.0"):
+        if "trust_remote_code" in inspect.signature(import_mcore_gpt_from_hf).parameters:
             import_kwargs.update({"trust_remote_code": args.trust_remote_code})
         import_mcore_gpt_from_hf(
             unwrapped_model, args.pretrained_model_path, workspace_dir, **import_kwargs
@@ -315,12 +392,18 @@ def _custom_prompt_forward_loop_func(model):
             if all_references[idx] is not None:
                 assert all_references[idx] == generated_texts[0], all_references[idx]
 
-    def _hf_dataset_forword_loop_func(model):
-        dataloader = get_calib_dataloader(args.calib_size)
-
-        for prompt in tqdm(dataloader, total=args.calib_size, disable=torch.distributed.get_rank()):
-            tokens = tokenizer(prompt, return_tensors="pt")
-            generated_ids = simple_generate(model, tokens.input_ids.cuda(), osl=1)
+    def _dataset_forward_loop_func(model):
+        dataloader = get_calib_dataloader(
+            dataset_path_or_name=args.calib_dataset_path_or_name,
+            tokenizer=tokenizer,
+            calib_size=args.calib_size,
+            max_sequence_length=args.calib_max_sequence_length,
+            use_random_offset=args.calib_use_random_offset,
+            batch_size=args.calib_batch_size,
+        )
+        for sample in tqdm(dataloader, disable=torch.distributed.get_rank()):
+            sample = get_batch_on_this_cp_rank(sample)
+            simple_generate(model, sample["input_ids"], osl=1, calibration_mode=True)
 
     unwrapped_model = unwrap_model(model)[0]
 
@@ -330,20 +413,21 @@ def _hf_dataset_forword_loop_func(model):
         )
 
     if args.export_quant_cfg is not None:
-        if args.export_quant_cfg not in QUANT_CFG_CHOICES:
-            raise ValueError(f"Unsupported quantization config {args.export_quant_cfg}.")
         print_rank_0("Quantizing the model...")
         mtq_config = get_modelopt_torch_quantization_config()
-        ptq_forward_loop_func = _hf_dataset_forword_loop_func
+
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"  # better for calibration
 
         if args.weight_only:
             mtq.quantize(unwrapped_model, mtq_config)
         elif hasattr(unwrapped_model, "calibration_mode"):
             unwrapped_model.calibration_mode = True
-            mtq.quantize(unwrapped_model, mtq_config, ptq_forward_loop_func)
+            mtq.quantize(unwrapped_model, mtq_config, _dataset_forward_loop_func)
             unwrapped_model.calibration_mode = False
         else:
-            mtq.quantize(unwrapped_model, mtq_config, ptq_forward_loop_func)
+            mtq.quantize(unwrapped_model, mtq_config, _dataset_forward_loop_func)
 
         if args.compress:
             mtq.compress(unwrapped_model)
@@ -351,7 +435,8 @@ def _hf_dataset_forword_loop_func(model):
 
         print_distributed_quant_summary(model, "Quantized Model:")
 
-    _custom_prompt_forward_loop_func(unwrapped_model)
-
     if args.save is not None:
         save_checkpoint(1, model, None, None, 0, release=True)
+
+    # Do this after saving in case it causes issues
+    _custom_prompt_forward_loop_func(unwrapped_model)
diff --git a/examples/post_training/modelopt/quantize.sh b/examples/post_training/modelopt/quantize.sh
index 563e532a12f..d42449988e4 100755
--- a/examples/post_training/modelopt/quantize.sh
+++ b/examples/post_training/modelopt/quantize.sh
@@ -16,7 +16,7 @@ MLM_DEFAULT_ARGS="
 QUANT_CFG=$2
 
 if [ -z ${QUANT_CFG} ]; then
-    QUANT_CFG=fp8
+    QUANT_CFG='FP8_DEFAULT_CFG'
     printf "${MLM_WARNING} Variable ${PURPLE}QUANT_CFG${WHITE} is not set (default: ${QUANT_CFG})!\n"
 fi
 
@@ -32,6 +32,7 @@ if [ -z ${MLM_MODEL_CKPT} ]; then
         --expert-tensor-parallel-size ${ETP} \
         --expert-model-parallel-size ${EP} \
         --pipeline-model-parallel-size ${PP} \
+        --context-parallel-size ${CP} \
         --tokenizer-model ${TOKENIZER_MODEL} \
         --pretrained-model-path ${HF_MODEL_CKPT} \
         --save ${MLM_MODEL_SAVE} \
@@ -45,6 +46,7 @@ else
         --expert-tensor-parallel-size ${ETP} \
         --expert-model-parallel-size ${EP} \
         --pipeline-model-parallel-size ${PP} \
+        --context-parallel-size ${CP} \
         --tokenizer-model ${TOKENIZER_MODEL} \
         --load ${MLM_MODEL_CKPT} \
         --save ${MLM_MODEL_SAVE} \
diff --git a/examples/post_training/modelopt/train.sh b/examples/post_training/modelopt/train.sh
new file mode 100755
index 00000000000..1ebb8bf3d76
--- /dev/null
+++ b/examples/post_training/modelopt/train.sh
@@ -0,0 +1,93 @@
+#!/bin/bash
+
+SCRIPT_DIR="$(dirname "$(readlink -f "$0")")"
+
+# Common arguments and base model specific arguments
+source "${SCRIPT_DIR}/conf/arguments.sh"
+
+
+# Set up cache dir for HF to avoid out of space error
+export HF_DATASETS_CACHE="/tmp/hf_datasets_cache"
+
+# Extra arguments of this script
+MLM_DEFAULT_ARGS=" \
+    --modelopt-enabled \
+    --distributed-timeout-minutes 60 \
+    --auto-detect-ckpt-format \
+    --export-te-mcore-model \
+"
+
+
+if [ -z ${MLM_MODEL_SAVE} ]; then
+    MLM_MODEL_SAVE=${MLM_MODEL_CKPT}
+    printf "${MLM_WARNING} Variable ${PURPLE}MLM_MODEL_SAVE${WHITE} is not set (default: ${MLM_MODEL_CKPT})!\n"
+fi
+
+if [ -z ${MLM_DATA_ARGS} ]; then
+    MLM_DATA_ARGS=" \
+        --train-samples 128000 \
+        --lr-decay-samples 128000 \
+        --lr-warmup-samples 0 \
+	--sft \
+	--tokenizer-type SFTTokenizer \
+	--per-split-data-args-path ${BLEND_PATH} \
+    "
+fi
+
+if [ -z ${MLM_TRAIN_ARGS} ]; then
+    MLM_TRAIN_ARGS=" \
+        --no-gradient-accumulation-fusion \
+        --micro-batch-size 1 \
+        --attention-dropout 0.0 \
+        --hidden-dropout 0.0 \
+        --no-check-for-nan-in-loss-and-grad \
+    "
+fi
+
+if [ -z ${MLM_OPTIM_ARGS} ]; then
+    MLM_OPTIM_ARGS=" \
+        --lr 5.0e-5 \
+        --min-lr 1.0e-7 \
+        --lr-decay-style cosine \
+        --clip-grad 1.0 \
+        --weight-decay 0.0 \
+        --adam-beta1 0.9 \
+        --adam-beta2 0.95 \
+        --init-method-std 0.010 \
+        --use-distributed-optimizer \
+    "
+fi
+
+if [ -z ${MLM_EVAL_ARGS} ]; then
+    MLM_EVAL_ARGS=" \
+        --eval-iters 1 \
+        --eval-interval 1000 \
+        --save-interval 1000 \
+        --log-interval 100 \
+    "
+fi
+
+export HF_TOKEN=${HF_TOKEN}
+
+if [[ ${MODEL_ARGS} == *"MambaModel"* ]]; then
+    PRETRAIN_EXE=${SCRIPT_DIR}/../../../pretrain_mamba.py
+else
+    PRETRAIN_EXE=${SCRIPT_DIR}/../../../pretrain_gpt.py
+fi
+
+${LAUNCH_SCRIPT} ${PRETRAIN_EXE} \
+    ${MODEL_ARGS} \
+    --tensor-model-parallel-size ${TP} \
+    --expert-tensor-parallel-size ${ETP} \
+    --expert-model-parallel-size ${EP} \
+    --pipeline-model-parallel-size ${PP} \
+    --context-parallel-size ${CP} \
+    --tokenizer-model ${TOKENIZER_MODEL} \
+    --load ${MLM_MODEL_CKPT} \
+    --save ${MLM_MODEL_SAVE} \
+    ${MLM_DATA_ARGS} \
+    ${MLM_OPTIM_ARGS} \
+    ${MLM_TRAIN_ARGS} \
+    ${MLM_EVAL_ARGS} \
+    ${MLM_RESUME_ARGS} \
+    ${MLM_DEFAULT_ARGS} ${MLM_EXTRA_ARGS}
diff --git a/examples/post_training/modelopt/validate.py b/examples/post_training/modelopt/validate.py
index ee8bf64eccb..22f21245de9 100644
--- a/examples/post_training/modelopt/validate.py
+++ b/examples/post_training/modelopt/validate.py
@@ -115,6 +115,8 @@ def report_current_memory_info():
         ground_truth = [None for _ in range(len(prompts))]
 
     tokenizer = get_tokenizer()._tokenizer
+    if hasattr(tokenizer, "tokenizer"):
+        tokenizer = tokenizer.tokenizer
     model = get_model(functools.partial(model_provider, modelopt_gpt_mamba_builder), wrap_with_ddp=False)
 
     report_current_memory_info()
diff --git a/examples/rl/README.md b/examples/rl/README.md
index 34b6fafa517..b66b2abbc32 100644
--- a/examples/rl/README.md
+++ b/examples/rl/README.md
@@ -94,7 +94,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --disable-bias-linear \
@@ -172,7 +172,6 @@ torchrun \
     --save $CHECKPOINT_DIR \
     --load $CHECKPOINT_DIR \
     --tensorboard-dir $TB_DIR \
-    --langrl-inference-server-type inplace_megatron \
     --seed $SEED \
     --sequence-parallel \
     --finetune \
diff --git a/examples/rl/benchmark_refit.py b/examples/rl/benchmark_refit.py
new file mode 100644
index 00000000000..645cd9e2c15
--- /dev/null
+++ b/examples/rl/benchmark_refit.py
@@ -0,0 +1,345 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+"""
+Benchmark script for model refit performance.
+
+Measures the time to transfer model weights between different parallelism configurations.
+Supports both collocated (models share GPUs) and non-collocated (separate GPU sets) modes.
+"""
+import time
+
+import torch
+
+from megatron.core.resharding.refit import swap_model_weights
+from megatron.training import get_args, get_model as get_training_model, print_rank_0
+from megatron.training.initialize import initialize_megatron
+from megatron.training.arguments import core_transformer_config_from_args
+from megatron.rl.parallel_utils import build_inference_pg_collection
+from gpt_builders import gpt_builder
+from megatron.core.resharding.copy_services.nvshmem_copy_service import NVSHMEMCopyService
+from megatron.core.resharding.copy_services.nccl_copy_service import NCCLCopyService
+from megatron.core.resharding.copy_services.gloo_copy_service import GlooCopyService
+
+
+def add_benchmark_args(parser):
+    """Add benchmark-specific arguments."""
+    group = parser.add_argument_group(title='refit benchmark')
+
+    group.add_argument(
+        '--refit-mode',
+        type=str,
+        required=True,
+        choices=['collocated', 'non-collocated'],
+        help='Collocated: both models share GPUs. Non-collocated: separate GPU sets.'
+    )
+    group.add_argument(
+        '--num-benchmark-warmup',
+        type=int,
+        default=2,
+        help='Number of warmup iterations (first builds refit plan).'
+    )
+    group.add_argument(
+        '--num-benchmark-iterations',
+        type=int,
+        default=10,
+        help='Number of timed benchmark iterations.'
+    )
+
+    return parser
+
+
+def model_provider(pre_process=True, post_process=True, parallel_output=False,
+                   pg_collection=None, config=None):
+    """Build the model."""
+    args = get_args()
+    if config is None:
+        config = core_transformer_config_from_args(args)
+
+    return gpt_builder(
+        args=args,
+        pre_process=pre_process,
+        post_process=post_process,
+        config=config,
+        pg_collection=pg_collection,
+    )
+
+
+def create_refit_service(method):
+    """Create and return a refit service instance."""
+    if method == 'nvshmem':
+        return NVSHMEMCopyService()
+    elif method == 'nccl':
+        return NCCLCopyService()
+    elif method == 'gloo':
+        return GlooCopyService()
+    else:
+        return method
+
+
+def print_config_summary(args, src_config, dst_config, world_size, mode):
+    """Print benchmark configuration."""
+    print_rank_0(f"\n{'='*80}")
+    print_rank_0(f"REFIT BENCHMARK - {mode.upper()} MODE")
+    print_rank_0(f"{'='*80}")
+    print_rank_0(f"World size: {world_size}")
+    print_rank_0(f"Source:      TP={src_config['tp']}, PP={src_config['pp']}, EP={src_config['ep']}, DP={src_config['dp']}")
+    print_rank_0(f"Destination: TP={dst_config['tp']}, PP={dst_config['pp']}, EP={dst_config['ep']}, DP={dst_config['dp']}")
+    print_rank_0(f"Model: {args.num_layers}L, {args.hidden_size}H, {args.num_attention_heads} heads, vocab={args.vocab_size}")
+    if args.num_experts:
+        print_rank_0(f"MoE: {args.num_experts} experts, top-{args.moe_router_topk}")
+    print_rank_0(f"Backend: {args.refit_method}")
+    print_rank_0(f"{'='*80}\n")
+
+
+def run_benchmark(src_model, dst_model, refit_service, num_warmup, num_iterations):
+    """Run warmup and benchmark iterations, return timings."""
+    rank = torch.distributed.get_rank()
+
+    # Warmup (builds refit plan on first iteration)
+    print_rank_0(f"Warmup: {num_warmup} iterations...")
+    for i in range(num_warmup):
+        torch.cuda.synchronize()
+        torch.distributed.barrier()
+        swap_model_weights(src_model, dst_model, refit_method=refit_service)
+        torch.cuda.synchronize()
+        torch.distributed.barrier()
+
+    print_rank_0("Warmup complete. Starting benchmark...\n")
+
+    # Benchmark iterations
+    print_rank_0(f"Benchmark: {num_iterations} iterations...")
+    timings = []
+
+    for i in range(num_iterations):
+        torch.cuda.synchronize()
+        torch.distributed.barrier()
+
+        start_time = time.perf_counter()
+        swap_model_weights(src_model, dst_model, refit_method=refit_service)
+        torch.cuda.synchronize()
+        end_time = time.perf_counter()
+
+        elapsed = end_time - start_time
+        timings.append(elapsed)
+        torch.distributed.barrier()
+
+    return timings
+
+
+def print_results(timings):
+    """Print benchmark results."""
+    if torch.distributed.get_rank() == 0:
+        mean_time = sum(timings) / len(timings)
+        min_time = min(timings)
+        max_time = max(timings)
+
+        print(f"\n{'='*80}")
+        print("RESULTS")
+        print(f"{'='*80}")
+        print(f"Mean: {mean_time*1000:.2f} ms")
+        print(f"Min:  {min_time*1000:.2f} ms")
+        print(f"Max:  {max_time*1000:.2f} ms")
+        print(f"{'='*80}\n")
+
+
+def benchmark_collocated():
+    """Benchmark refit in collocated mode (both models on same GPUs)."""
+    args = get_args()
+    world_size = torch.distributed.get_world_size()
+
+    # Calculate parallelism
+    src_tp = args.tensor_model_parallel_size
+    src_pp = args.pipeline_model_parallel_size
+    src_ep = args.expert_model_parallel_size
+    src_world = src_tp * src_pp * src_ep
+    src_dp = world_size // src_world
+
+    dst_tp = args.rl_inference_tensor_model_parallel_size or src_tp
+    dst_pp = args.rl_inference_pipeline_model_parallel_size or src_pp
+    dst_ep = args.rl_inference_expert_model_parallel_size or src_ep
+    dst_world = dst_tp * dst_pp * dst_ep
+    dst_dp = world_size // dst_world
+
+    # Print config
+    src_config = {'tp': src_tp, 'pp': src_pp, 'ep': src_ep, 'dp': src_dp}
+    dst_config = {'tp': dst_tp, 'pp': dst_pp, 'ep': dst_ep, 'dp': dst_dp}
+    print_config_summary(args, src_config, dst_config, world_size, 'collocated')
+
+    # Build source model
+    print_rank_0("Building source model...")
+    src_model = get_training_model(
+        lambda pre_process, post_process, **kwargs: model_provider(
+            pre_process=pre_process, post_process=post_process, parallel_output=False
+        ),
+        wrap_with_ddp=False
+    )
+    src_model[0] = src_model[0].cuda()
+
+    # Build destination model with custom parallelism
+    print_rank_0("Building destination model...")
+    dst_pg_collection = build_inference_pg_collection(
+        world_size,
+        tp_size=dst_tp,
+        pp_size=dst_pp,
+        ep_size=dst_ep,
+        expt_tp_size=args.rl_inference_expert_tensor_model_parallel_size,
+        use_tp_pp_dp_mapping=args.use_tp_pp_dp_mapping,
+    )
+
+    dst_config = core_transformer_config_from_args(args)
+    if args.num_experts:
+        dst_config.expert_model_parallel_size = dst_ep
+    dst_config.tensor_model_parallel_size = dst_tp
+    if args.rl_inference_expert_tensor_model_parallel_size:
+        dst_config.expert_tensor_parallel_size = args.rl_inference_expert_tensor_model_parallel_size
+
+    dst_model = get_training_model(
+        lambda pre_process, post_process, **kwargs: model_provider(
+            pre_process=pre_process, post_process=post_process,
+            pg_collection=dst_pg_collection, config=dst_config
+        ),
+        wrap_with_ddp=False
+    )
+    dst_model[0] = dst_model[0].cuda()
+
+    torch.distributed.barrier()
+
+    # Create refit service
+    print_rank_0(f"Creating {args.refit_method} service...")
+    refit_service = create_refit_service(args.refit_method)
+    print_rank_0("Service created.\n")
+
+    # Run benchmark
+    timings = run_benchmark(src_model, dst_model, refit_service,
+                           args.num_benchmark_warmup, args.num_benchmark_iterations)
+
+    # Print results
+    print_results(timings)
+
+
+def benchmark_non_collocated():
+    """Benchmark refit in non-collocated mode (separate GPU sets)."""
+    args = get_args()
+    rank = torch.distributed.get_rank()
+    world_size = torch.distributed.get_world_size()
+
+    # Calculate parallelism
+    src_tp = args.tensor_model_parallel_size
+    src_pp = args.pipeline_model_parallel_size
+    src_ep = args.expert_model_parallel_size
+    src_world = src_tp * src_pp * src_ep
+
+    dst_tp = args.rl_inference_tensor_model_parallel_size or src_tp
+    dst_pp = args.rl_inference_pipeline_model_parallel_size or src_pp
+    dst_ep = args.rl_inference_expert_model_parallel_size or src_ep
+    dst_world = dst_tp * dst_pp * dst_ep
+
+    required_size = src_world + dst_world
+    if world_size < required_size:
+        raise ValueError(f"Non-collocated requires {required_size} GPUs, got {world_size}")
+
+    # Determine rank roles
+    is_src_rank = rank < src_world
+    is_dst_rank = src_world <= rank < required_size
+    is_idle_rank = rank >= required_size
+
+    # Print config
+    src_config = {'tp': src_tp, 'pp': src_pp, 'ep': src_ep, 'dp': 1}
+    dst_config = {'tp': dst_tp, 'pp': dst_pp, 'ep': dst_ep, 'dp': 1}
+    print_config_summary(args, src_config, dst_config, world_size, 'non-collocated')
+    if world_size > required_size:
+        print_rank_0(f"Note: Ranks {required_size}-{world_size-1} are idle\n")
+
+    # Create destination process groups (all ranks participate)
+    print_rank_0("Creating process groups...")
+    dst_pg_collection = build_inference_pg_collection(
+        world_size=dst_world,
+        tp_size=dst_tp,
+        pp_size=dst_pp,
+        ep_size=dst_ep,
+        expt_tp_size=args.rl_inference_expert_tensor_model_parallel_size,
+        use_tp_pp_dp_mapping=args.use_tp_pp_dp_mapping,
+        rank_offset=src_world,
+    )
+    torch.distributed.barrier()
+
+    # Idle ranks participate in collectives but have no models
+    if is_idle_rank:
+        src_model = None
+        dst_model = None
+    elif is_src_rank:
+        # Build source model
+        print_rank_0("Building source model...")
+        src_model = get_training_model(
+            lambda pre_process, post_process, **kwargs: model_provider(
+                pre_process=pre_process, post_process=post_process, parallel_output=False
+            ),
+            wrap_with_ddp=False
+        )
+        src_model[0] = src_model[0].cuda()
+        dst_model = None
+    else:  # is_dst_rank
+        # Build destination model
+        print_rank_0("Building destination model...")
+        dst_config = core_transformer_config_from_args(args)
+        if args.num_experts:
+            dst_config.expert_model_parallel_size = dst_ep
+        dst_config.tensor_model_parallel_size = dst_tp
+        if args.rl_inference_expert_tensor_model_parallel_size:
+            dst_config.expert_tensor_parallel_size = args.rl_inference_expert_tensor_model_parallel_size
+
+        dst_model = get_training_model(
+            lambda pre_process, post_process, **kwargs: model_provider(
+                pre_process=pre_process, post_process=post_process,
+                pg_collection=dst_pg_collection, config=dst_config
+            ),
+            wrap_with_ddp=False
+        )
+        dst_model[0] = dst_model[0].cuda()
+        src_model = None
+
+    torch.distributed.barrier()
+
+    # Create refit service
+    print_rank_0(f"Creating {args.refit_method} service...")
+    refit_service = create_refit_service(args.refit_method)
+    print_rank_0("Service created.\n")
+
+    # Run benchmark
+    timings = run_benchmark(src_model, dst_model, refit_service,
+                           args.num_benchmark_warmup, args.num_benchmark_iterations)
+
+    # Print results
+    print_results(timings)
+
+
+def main():
+    """Main benchmark function."""
+    initialize_megatron(
+        extra_args_provider=add_benchmark_args,
+        args_defaults={
+            'tokenizer_type': 'NullTokenizer',
+            'no_load_optim': True,
+            'no_load_rng': True,
+            'no_save_optim': True,
+            'no_save_rng': True,
+        },
+        ignore_unknown_args=False,
+    )
+
+    args = get_args()
+
+    # Set default vocab size if not provided
+    if args.vocab_size is None:
+        args.vocab_size = 50257
+        print_rank_0("Using default vocab_size=50257")
+
+    # Run benchmark
+    if args.refit_mode == 'collocated':
+        benchmark_collocated()
+    else:
+        benchmark_non_collocated()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/rl/environment_configs/gsm8k_nanov3.yaml b/examples/rl/environment_configs/gsm8k_nanov3.yaml
index 30403ed052b..b759423ee5b 100644
--- a/examples/rl/environment_configs/gsm8k_nanov3.yaml
+++ b/examples/rl/environment_configs/gsm8k_nanov3.yaml
@@ -2,8 +2,6 @@
   agent_args:
     answer_format: "boxed"
     format_reward: 0.5
-    assistant_suffix: "Assistant: "
-    chat_mode: true
     negative_reward: 0.0
     partial_end_reward: 0.75
   weight: 1.0
diff --git a/examples/rl/environments/countdown/countdown_agent.py b/examples/rl/environments/countdown/countdown_agent.py
index bd9413a19d0..e14ac6c6d7e 100644
--- a/examples/rl/environments/countdown/countdown_agent.py
+++ b/examples/rl/environments/countdown/countdown_agent.py
@@ -10,17 +10,11 @@
 
 
 class CountdownAgent(RewardOnlyAgent, HFDatasetAgent):
+    env_id: str = "countdown"
 
     def make_prefix(self, target, nums) -> str:
-        if self.chat_mode:
-            prefix = f"""Using the numbers {nums}, create an equation that equals {target}. You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. 
+        prefix = f"""Using the numbers {nums}, create an equation that equals {target}. You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. 
         Return the final answer in <answer> </answer> tags, for example <answer> (1 + 2) / 3 </answer>. Do not include an = sign."""
-        else:
-            prefix = f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer.
-        User: Using the numbers {nums}, create an equation that equals {target}. You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. Show your work in <think> </think> tags. 
-        And return the final answer in <answer> </answer> tags, for example <answer> (1 + 2) / 3 </answer>. Do not include an = sign.
-        Assistant: Let me solve this step by step.
-        <think>"""
         return prefix
 
     def get_dataset(self, validation: bool = False):
diff --git a/examples/rl/environments/math/gsm8k_agent.py b/examples/rl/environments/math/gsm8k_agent.py
index 3bb39bc09f9..6cdfb4f926e 100644
--- a/examples/rl/environments/math/gsm8k_agent.py
+++ b/examples/rl/environments/math/gsm8k_agent.py
@@ -25,16 +25,12 @@
 class GSM8KAgent(MathAgent):
     def __init__(self,
         answer_format: str = "boxed",
-        chat_mode: bool = False,
-        assistant_suffix: str = "Assistant: Let me solve this step by step.\n<think>",
         format_reward: float = 0.0,
         negative_reward: float = 0.0,
         partial_end_reward: float = 0.0,
         **kwargs):
         super().__init__(
             answer_format=answer_format,
-            chat_mode=chat_mode,
-            assistant_suffix=assistant_suffix,
             format_reward=format_reward,
             negative_reward=negative_reward,
             partial_end_reward=partial_end_reward,
diff --git a/examples/rl/environments/math/math_agent.py b/examples/rl/environments/math/math_agent.py
index 67feb3b4adb..027ef242285 100644
--- a/examples/rl/environments/math/math_agent.py
+++ b/examples/rl/environments/math/math_agent.py
@@ -3,7 +3,6 @@
 import re
 import traceback
 
-from megatron.rl.agent.pass_at_evaluation_agent import PassAtEvaluationAgent
 from megatron.rl.agent.reward_only_agent import RewardOnlyAgent
 
 try:
@@ -25,8 +24,6 @@ class MathAgent(RewardOnlyAgent):
     def __init__(self,
         format_reward: float = 0.0,
         answer_format: str = "tagged",
-        assistant_suffix: str = "Assistant: Let me solve this step by step.\n<think>",
-        chat_mode: bool = False,
         negative_reward: float = 0.0,
         partial_end_reward: float = 0.0,
         **kwargs):
@@ -36,9 +33,6 @@ def __init__(self,
                 even if the answer is incorrect or is missing the end-of-text token.
             answer_format (str): Which answer format is expected: "tagged" for <answer> tags,
                 or "boxed" for \boxed{} LaTeX formatting.
-            assistant_suffix (str): The suffix string included in the assistant's response, typically to
-                guide the assistant's output format and "persona". For example, "Let me solve this step by step."
-            chat_mode (bool): If True, agent operates in a chat (conversational) context.
             negative_reward (float): Reward assigned for a clearly incorrect or unparseable answer.
             partial_end_reward (float): Reward when the answer is correct but an expected end token is not matched exactly.
             **kwargs: Additional arguments for the base RewardOnlyAgent.
@@ -49,8 +43,6 @@ def __init__(self,
 
         self.format_reward = format_reward
         self.answer_format = answer_format
-        self.assistant_suffix = assistant_suffix
-        self.chat_mode = chat_mode
         self.negative_reward = negative_reward
         self.partial_end_reward = partial_end_reward
 
@@ -61,7 +53,7 @@ def compute_score(self, response: str, golden: dict, golden_key: str = "answer")
         """
         # Allow <answer> tags or \boxed{} tags (this is a bit of cheating in favor of deepseek distilled models I think)
         matched_format = None
-        end_tokens = ["<|end_of_text|>", "<|endoftext|>", "</s>"]
+        end_tokens = ["<|end_of_text|>", "<|endoftext|>", "</s>", "<|eot_id|>"]
 
         # Only an answer immediately followed by a known end token yields 1.0 reward.
         answer_tag_pattern = r'<answer>(.*?)</answer>'
@@ -134,12 +126,6 @@ def make_prefix(self, problem_key: str = "problem", **kwargs) -> str:
         else:
             raise ValueError(f"Invalid answer format: {self.answer_format}")
 
-        if self.chat_mode:
-            prefix = f"""{kwargs[problem_key]}\n{answer_format}"""
-        else:
-            prefix = f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer.
-    The question will be a word math problem. Show your work in <think> </think> tags. 
-    {answer_format}
-    User: {kwargs[problem_key]}
-    {self.assistant_suffix}"""
+        prefix = f"""{kwargs[problem_key]}\n{answer_format}"""
+
         return prefix
diff --git a/examples/rl/model_configs/common.sh b/examples/rl/model_configs/common.sh
index 4f6ca0e18cf..c37d88fb4df 100644
--- a/examples/rl/model_configs/common.sh
+++ b/examples/rl/model_configs/common.sh
@@ -22,7 +22,7 @@ COMMON_OPTIONS="\
     --attention-backend flash \
     --timing-log-level 1 \
     --log-timers-to-tensorboard \
-    --save-retain-interval 120 \
+    --save-retain-interval 160 \
     --inference-dynamic-batching-num-cuda-graphs 1 \
     --inference-dynamic-batching-unified-memory-level 1 \
     --adam-beta1 0.9 \
diff --git a/examples/rl/model_configs/llama3p1_8b_instruct.sh b/examples/rl/model_configs/llama3p1_8b_instruct.sh
index 24d285a6cf7..ff3b5327710 100644
--- a/examples/rl/model_configs/llama3p1_8b_instruct.sh
+++ b/examples/rl/model_configs/llama3p1_8b_instruct.sh
@@ -77,7 +77,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --add-qkv-bias \
   --normalization RMSNorm \
@@ -101,8 +101,8 @@ MODEL_OPTIONS="\
   --max-position-embeddings 131072  \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model unsloth/Meta-Llama-3.1-8B-Instruct \
-  --langrl-inference-server-type "inplace_megatron_chat" \
-  --langrl-inference-server-conversation-template "unsloth/Meta-Llama-3.1-8B-Instruct" \
+  --tokenizer-hf-use-fast \
+  --tokenizer-hf-include-special-tokens \
   --lr 3e-7 \
   --make-vocab-size-divisible-by 128 \
   --clip-grad 1.0 \
diff --git a/examples/rl/model_configs/nemotron5_56b.sh b/examples/rl/model_configs/nemotron5_56b.sh
index fd2cc4f7212..741cd054b73 100644
--- a/examples/rl/model_configs/nemotron5_56b.sh
+++ b/examples/rl/model_configs/nemotron5_56b.sh
@@ -58,7 +58,7 @@ MODEL_OPTIONS="\
   --calculate-per-token-loss \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
  --fp8-format hybrid \
     --fp8-amax-history-len 1 \
diff --git a/examples/rl/model_configs/nemotron5_8b.sh b/examples/rl/model_configs/nemotron5_8b.sh
index 7b8947ae763..753d4e493a2 100644
--- a/examples/rl/model_configs/nemotron5_8b.sh
+++ b/examples/rl/model_configs/nemotron5_8b.sh
@@ -58,7 +58,7 @@ MODEL_OPTIONS="\
   --calculate-per-token-loss \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --hybrid-override-pattern M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M- \
     --spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
diff --git a/examples/rl/model_configs/nemotron5p5_12b_H.sh b/examples/rl/model_configs/nemotron5p5_12b_H.sh
index 9e97051e087..adbcc8d03f0 100644
--- a/examples/rl/model_configs/nemotron5p5_12b_H.sh
+++ b/examples/rl/model_configs/nemotron5p5_12b_H.sh
@@ -65,7 +65,7 @@ MODEL_OPTIONS="\
   --calculate-per-token-loss \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --fp8-recipe blockwise \
   --fp8-format e4m3 \
diff --git a/examples/rl/model_configs/nemotron6_3b_moe.sh b/examples/rl/model_configs/nemotron6_3b_moe.sh
index eff4f6cf0b3..19891ad7b8b 100644
--- a/examples/rl/model_configs/nemotron6_3b_moe.sh
+++ b/examples/rl/model_configs/nemotron6_3b_moe.sh
@@ -85,7 +85,7 @@ MODEL_OPTIONS="\
   --rl-importance-sampling-truncation-coef 10.0 \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --distributed-timeout-minutes 60 \
   --use-mcore-models \
@@ -104,7 +104,7 @@ MODEL_OPTIONS="\
   --tiktoken-pattern v2 \
   --tokenizer-type TikTokenizer \
   --tokenizer-model ${TOKENIZER_MODEL} \
-  --dist-ckpt-strictness log_unexpected
+  --dist-ckpt-strictness log_unexpected \
   --ckpt-format torch_dist \
   --ckpt-fully-parallel-save \
   --ckpt-fully-parallel-load \
@@ -118,5 +118,4 @@ MODEL_OPTIONS="\
   --lr-warmup-samples 640 \
   --lr-warmup-init 0.3e-7 \
   --no-load-optim \
-  --no-load-rng \
-  "
+  --no-load-rng "
diff --git a/examples/rl/model_configs/qwen3_30b_a3b_moe.sh b/examples/rl/model_configs/qwen3_30b_a3b_moe.sh
index 775a9587ba4..637b431280f 100644
--- a/examples/rl/model_configs/qwen3_30b_a3b_moe.sh
+++ b/examples/rl/model_configs/qwen3_30b_a3b_moe.sh
@@ -37,7 +37,7 @@ ENV_DEPENDENT="\
 MODEL_OPTIONS="
 --seq-length $MAX_SEQ_LENGTH \
 --inference-max-seq-length $MAX_SEQ_LENGTH \
---inference-max-batch-size $MAX_INFERENCE_BS \
+--inference-max-requests $MAX_INFERENCE_BS \
 --pretrained-checkpoint $CHECKPOINT \
 --no-use-tokenizer-model-from-checkpoint-args \
 --seq-length 8192 \
@@ -51,6 +51,7 @@ MODEL_OPTIONS="
 --te-rng-tracker \
 --tokenizer-type HuggingFaceTokenizer \
 --tokenizer-model Qwen/Qwen3-30B-A3B \
+--tokenizer-hf-include-special-tokens \
 --untie-embeddings-and-output-weights \
 --num-layers 48 \
 --hidden-size 2048 \
diff --git a/examples/rl/model_configs/qwen3_32b.sh b/examples/rl/model_configs/qwen3_32b.sh
index cd153a04f3c..fcadb0c4021 100644
--- a/examples/rl/model_configs/qwen3_32b.sh
+++ b/examples/rl/model_configs/qwen3_32b.sh
@@ -38,7 +38,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --num-layers 64 \
@@ -64,6 +64,7 @@ MODEL_OPTIONS="\
   --attention-softmax-in-fp32 \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model Qwen/Qwen3-4B \
+  --tokenizer-hf-include-special-tokens \
   --vocab-size 151936 \
   --make-vocab-size-divisible-by 128 \
   --optimizer adam \
diff --git a/examples/rl/model_configs/qwen3_4b.sh b/examples/rl/model_configs/qwen3_4b.sh
index da238511fd3..81899f2ea7b 100644
--- a/examples/rl/model_configs/qwen3_4b.sh
+++ b/examples/rl/model_configs/qwen3_4b.sh
@@ -38,7 +38,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --num-layers 36 \
   --hidden-size 2560 \
@@ -63,8 +63,6 @@ MODEL_OPTIONS="\
   --attention-softmax-in-fp32 \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model Qwen/Qwen3-4B \
-  --langrl-inference-server-type "inplace_megatron_chat" \
-  --langrl-inference-server-conversation-template "Qwen/Qwen3-4B" \
   --vocab-size 151936 \
   --make-vocab-size-divisible-by 128 \
   --optimizer adam \
diff --git a/examples/rl/model_configs/qwen3_8b.sh b/examples/rl/model_configs/qwen3_8b.sh
index 6758cd84c3d..4b5e1103f0e 100644
--- a/examples/rl/model_configs/qwen3_8b.sh
+++ b/examples/rl/model_configs/qwen3_8b.sh
@@ -38,7 +38,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --num-layers 36 \
@@ -64,8 +64,6 @@ MODEL_OPTIONS="\
   --attention-softmax-in-fp32 \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model Qwen/Qwen3-8B \
-  --langrl-inference-server-type "inplace_megatron_chat" \
-  --langrl-inference-server-conversation-template "Qwen/Qwen3-8B" \
   --vocab-size 151936 \
   --make-vocab-size-divisible-by 128 \
   --optimizer adam \
diff --git a/examples/rl/model_configs/qwen_2p5_32b.sh b/examples/rl/model_configs/qwen_2p5_32b.sh
index d82972ba477..0bfe19ba1bb 100644
--- a/examples/rl/model_configs/qwen_2p5_32b.sh
+++ b/examples/rl/model_configs/qwen_2p5_32b.sh
@@ -59,7 +59,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --disable-bias-linear \
@@ -85,6 +85,7 @@ MODEL_OPTIONS="\
   --max-position-embeddings 131072 \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model unsloth/Qwen2.5-32B \
+  --tokenizer-hf-include-special-tokens \
   --lr 1e-6 \
   --lr-warmup-samples 0 \
   --make-vocab-size-divisible-by 128 \
diff --git a/examples/rl/model_configs/qwen_2p5_3b.sh b/examples/rl/model_configs/qwen_2p5_3b.sh
index 246afae6ad2..4880272d4a6 100644
--- a/examples/rl/model_configs/qwen_2p5_3b.sh
+++ b/examples/rl/model_configs/qwen_2p5_3b.sh
@@ -22,7 +22,7 @@ if [ "$(basename "$ENV_CONFIG")" = "dapo.yaml" ]; then
   GRPO_KL_BETA=${GRPO_KL_BETA:-"0.0"}
   ENTROPY_WEIGHT=${ENTROPY_WEIGHT:-"0.0"}
   TRAINING_BATCH_SIZE=${TRAINING_BATCH_SIZE:-1024}
-  MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-2}
+  MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-1}
   MAX_SEQ_LENGTH=${MAX_SEQ_LENGTH:-8192}
   EXIT_INTERVAL=${EXIT_INTERVAL:-16}
   CHKPT_SAVE_INTERVAL=${CHKPT_SAVE_INTERVAL:-16}
@@ -38,7 +38,7 @@ else
   GRPO_KL_BETA=${GRPO_KL_BETA:-"0.0"}
   ENTROPY_WEIGHT=${ENTROPY_WEIGHT:-"0.0"}
   TRAINING_BATCH_SIZE=${TRAINING_BATCH_SIZE:-512}
-  MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-2}
+  MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-1}
   MAX_SEQ_LENGTH=${MAX_SEQ_LENGTH:-8192}
   EXIT_INTERVAL=${EXIT_INTERVAL:-16}
   CHKPT_SAVE_INTERVAL=${CHKPT_SAVE_INTERVAL:-16}
@@ -62,7 +62,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch_dist \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --disable-bias-linear \
   --add-qkv-bias \
@@ -87,6 +87,7 @@ MODEL_OPTIONS="\
   --max-position-embeddings 32768  \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model unsloth/Qwen2.5-3B \
+  --tokenizer-hf-include-special-tokens \
   --lr 0.000001 \
   --lr-warmup-samples 0 \
   --make-vocab-size-divisible-by 64 \
diff --git a/examples/rl/model_configs/qwen_2p5_distill_7b.sh b/examples/rl/model_configs/qwen_2p5_distill_7b.sh
index 149ac77965f..ed214b3aae9 100644
--- a/examples/rl/model_configs/qwen_2p5_distill_7b.sh
+++ b/examples/rl/model_configs/qwen_2p5_distill_7b.sh
@@ -44,7 +44,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --disable-bias-linear \
@@ -70,8 +70,6 @@ MODEL_OPTIONS="\
   --max-position-embeddings 131072  \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model "unsloth/DeepSeek-R1-Distill-Qwen-7B" \
-  --langrl-inference-server-type "inplace_megatron_chat" \
-  --langrl-inference-server-conversation-template "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" \
   --lr 0.000001 \
   --lr-warmup-samples 0 \
   --make-vocab-size-divisible-by 128 \
diff --git a/examples/rl/model_configs/qwen_2p5_math_7b.sh b/examples/rl/model_configs/qwen_2p5_math_7b.sh
index 1d631fa80a5..b00077bc07a 100644
--- a/examples/rl/model_configs/qwen_2p5_math_7b.sh
+++ b/examples/rl/model_configs/qwen_2p5_math_7b.sh
@@ -58,7 +58,7 @@ MODEL_OPTIONS="\
   --ckpt-format torch \
   --seq-length $MAX_SEQ_LENGTH \
   --inference-max-seq-length $MAX_SEQ_LENGTH \
-  --inference-max-batch-size $MAX_INFERENCE_BS \
+  --inference-max-requests $MAX_INFERENCE_BS \
   --pretrained-checkpoint $CHECKPOINT \
   --untie-embeddings-and-output-weights \
   --disable-bias-linear \
@@ -84,6 +84,7 @@ MODEL_OPTIONS="\
   --max-position-embeddings 4096  \
   --tokenizer-type HuggingFaceTokenizer \
   --tokenizer-model "unsloth/Qwen2.5-Math-7B" \
+  --tokenizer-hf-include-special-tokens \
   --lr 0.000001 \
   --lr-warmup-samples 0 \
   --make-vocab-size-divisible-by 128 \
diff --git a/examples/run_simple_mcore_train_loop.py b/examples/run_simple_mcore_train_loop.py
index f748d74ecec..24aea52b2b2 100644
--- a/examples/run_simple_mcore_train_loop.py
+++ b/examples/run_simple_mcore_train_loop.py
@@ -20,10 +20,10 @@
     BlendedMegatronDatasetBuilder,
 )
 from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset
-from megatron.training.tokenizer.tokenizer import _NullTokenizer
 from megatron.core.distributed import DistributedDataParallel
 from megatron.core.distributed import DistributedDataParallelConfig
 from megatron.core.distributed.finalize_model_grads import finalize_model_grads
+from megatron.core.tokenizers import MegatronTokenizer
 
 
 _SEQUENCE_LENGTH: int = 64
@@ -102,7 +102,10 @@ def get_train_data_iterator() -> Iterator:
         reset_position_ids=False,
         reset_attention_mask=False,
         eod_mask_loss=False,
-        tokenizer=_NullTokenizer(vocab_size=_SEQUENCE_LENGTH),
+        tokenizer=MegatronTokenizer.from_pretrained(
+            metadata_path={"library": "null-text"},
+            vocab_size=_SEQUENCE_LENGTH,
+        ),
         mid_level_dataset_surplus=0.005,
     )
 
diff --git a/mamba_builders.py b/mamba_builders.py
index 6a792ba6ea5..5d31af60475 100644
--- a/mamba_builders.py
+++ b/mamba_builders.py
@@ -8,6 +8,7 @@
 from megatron.training.arguments import core_transformer_config_from_args
 from megatron.core.models.mamba.mamba_layer_specs import mamba_inference_stack_spec
 
+
 def mamba_builder(args, pre_process, post_process, vp_stage=None, config=None, pg_collection=None):
     print_rank_0('building MAMBA model ...')
     if config is None:
@@ -15,8 +16,10 @@ def mamba_builder(args, pre_process, post_process, vp_stage=None, config=None, p
     assert args.use_legacy_models is False, "Mamba only supported in Mcore!"
 
     if config.transformer_impl == "inference_optimized":
-        mamba_stack_spec = mamba_inference_stack_spec 
-        assert not config.inference_fuse_tp_communication, "inference_fuse_tp_communication is not supported for Mamba"
+        mamba_stack_spec = mamba_inference_stack_spec
+        assert (
+            not config.inference_fuse_tp_communication
+        ), "inference_fuse_tp_communication is not supported for Mamba"
     elif args.spec is not None:
         mamba_stack_spec = import_module(args.spec)
     else:
@@ -39,6 +42,7 @@ def mamba_builder(args, pre_process, post_process, vp_stage=None, config=None, p
         rotary_percent=args.rotary_percent,
         rotary_base=args.rotary_base,
         pg_collection=pg_collection,
+        vp_stage=vp_stage,
     )
 
     for l in range(model.decoder.num_layers_per_pipeline_rank):
diff --git a/megatron/core/QuickStart.md b/megatron/core/QuickStart.md
index 4aa966a164d..6b38f58a2bd 100644
--- a/megatron/core/QuickStart.md
+++ b/megatron/core/QuickStart.md
@@ -27,7 +27,7 @@ This guide for Megatron Core walks you through the following tasks:
 
     <br>
 
-For a more comprehensive overview of different installation methods, refer to the [Installation Guide](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/Installation_Guide.md)
+For a more comprehensive overview of different installation methods, refer to the [Installation Guide](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/install.html).
 
 ### Write Your First Training Loop
 
diff --git a/megatron/core/README.md b/megatron/core/README.md
index 6f2bb49a1dd..a9134be41cd 100644
--- a/megatron/core/README.md
+++ b/megatron/core/README.md
@@ -48,4 +48,4 @@ torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
 
 ---
 
-*For complete installation instructions, performance benchmarks, and ecosystem information, see the [main README](../README.md).*
+*For complete installation instructions, performance benchmarks, and ecosystem information, see the [main README](../../README.md).*
diff --git a/megatron/core/datasets/blended_megatron_dataset_config.py b/megatron/core/datasets/blended_megatron_dataset_config.py
index a86efbe4963..76f3ae75857 100644
--- a/megatron/core/datasets/blended_megatron_dataset_config.py
+++ b/megatron/core/datasets/blended_megatron_dataset_config.py
@@ -35,7 +35,7 @@ class BlendedMegatronDatasetConfig:
     """
 
     multiple_validation_sets: Optional[bool] = None
-    """Whether the validation split should be treated as multiple seperate datasets."""
+    """Whether the validation split should be treated as multiple separated datasets."""
 
     full_validation: Optional[bool] = None
     """Whether to run a full epoch of validation each time validation occurs."""
diff --git a/megatron/core/datasets/gpt_dataset.py b/megatron/core/datasets/gpt_dataset.py
index d895eac87d3..cbe0652402d 100644
--- a/megatron/core/datasets/gpt_dataset.py
+++ b/megatron/core/datasets/gpt_dataset.py
@@ -340,7 +340,7 @@ def _query_document_sample_shuffle_indices(
             sample_parts.append(
                 self.dataset.get(
                     self.document_index[doc_index_beg],
-                    offset=doc_index_beg_offset,
+                    offset=int(doc_index_beg_offset),
                     length=doc_index_end_offset
                     - doc_index_beg_offset
                     + self.config.add_extra_token_to_sequence,
@@ -361,7 +361,7 @@ def _query_document_sample_shuffle_indices(
                     else doc_index_end_offset + self.config.add_extra_token_to_sequence
                 )
                 sample_parts.append(
-                    self.dataset.get(self.document_index[i], offset=offset, length=length)
+                    self.dataset.get(self.document_index[i], offset=int(offset), length=length)
                 )
         assert len(document_ids) == len(
             sample_parts
diff --git a/megatron/core/datasets/megatron_tokenizer.py b/megatron/core/datasets/megatron_tokenizer.py
deleted file mode 100644
index a8c2a431e59..00000000000
--- a/megatron/core/datasets/megatron_tokenizer.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
-import json
-import logging
-from abc import ABC, abstractmethod
-from collections import OrderedDict
-from typing import Any
-
-import numpy
-
-logger = logging.getLogger(__name__)
-
-
-class MegatronLegacyTokenizer(ABC):
-    """Abstract class for tokenizer
-
-    Absent a config or class-specific tracking of which objects are uniquely identifying, we must
-    include all key word arguments as unique identifiers
-
-    Args:
-        tokenizer_paths (Tuple[str]): All tokenizer source paths or prefixes
-
-        tokenizer_options (Dict[str, Any]): All tokenizer options
-    """
-
-    def __init__(self, *tokenizer_paths: str, **tokenizer_options: Any):
-        from megatron.core.utils import log_single_rank
-
-        # Deprecation warning
-        log_single_rank(
-            logger,
-            logging.WARNING,
-            "You're using the legacy tokenizer system, which is deprecated "
-            "and will be removed in a future release. Please migrate to the new "
-            "tokenizer system (`megatron.core.tokenizers.MegatronTokenizer`).",
-        )
-        self.unique_identifiers = OrderedDict()
-        self.unique_identifiers["class"] = type(self).__name__
-        self.unique_identifiers["tokenizer_path"] = list(tokenizer_paths)
-        for option in tokenizer_options:
-            self.unique_identifiers[option] = str(tokenizer_options[option])
-
-        self.unique_description = json.dumps(self.unique_identifiers, indent=4)
-
-        super().__init__()
-
-    @abstractmethod
-    def tokenize(self, text: str) -> numpy.ndarray:
-        """Convert text to embedding ids
-
-        Args:
-            text (str): The text to convert
-
-        Returns:
-            numpy.ndarray: The converted embedding ids
-        """
-        pass
-
-    def detokenize(self, ids: numpy.ndarray) -> str:
-        """Convert embedding ids to text
-
-        Args:
-            ids (numpy.ndarray): The ids to convert
-
-        Returns:
-            str: The converted text
-
-        Raises:
-            NotImplementedError: Non-abstract, optional method
-        """
-        raise NotImplementedError("{} has no method 'detokenize'".format(type(self).__name__))
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        """Convert embedding ids to text offsets
-
-        Args:
-            ids (list[int]): The ids to convert
-            text (str): The text to convert
-
-        Returns:
-            list[int]: The converted offsets
-
-        Raises:
-            NotImplementedError: Non-abstract, optional method
-        """
-        raise NotImplementedError("{} has no method 'offsets'".format(type(self).__name__))
-
-    @property
-    @abstractmethod
-    def vocab(self):
-        """Dictionary from vocab text token to id token"""
-        pass
-
-    @property
-    @abstractmethod
-    def inv_vocab(self):
-        """Dictionary from vocab id token to text token"""
-        pass
-
-    @property
-    @abstractmethod
-    def vocab_size(self):
-        """The vocabulary size"""
-        pass
-
-    @property
-    def cls(self):
-        """The CLS token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'cls'".format(type(self).__name__))
-
-    @property
-    def sep(self):
-        """The SEP token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'sep'".format(type(self).__name__))
-
-    @property
-    def pad(self):
-        """The PAD token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'pad'".format(type(self).__name__))
-
-    @property
-    def eod(self):
-        """The EOD token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'eod'".format(type(self).__name__))
-
-    @property
-    def bos(self):
-        """The BOS token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'bos'".format(type(self).__name__))
-
-    @property
-    def eos(self):
-        """The EOS token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'eos'".format(type(self).__name__))
-
-    @property
-    def mask(self):
-        """The MASK token id
-
-        Raises:
-            NotImplementedError: Non-abstract, optional attribute
-        """
-        raise NotImplementedError("{} has no attribute 'mask'".format(type(self).__name__))
diff --git a/megatron/core/datasets/t5_dataset.py b/megatron/core/datasets/t5_dataset.py
index 9ac566dc0e5..07d221e0c80 100644
--- a/megatron/core/datasets/t5_dataset.py
+++ b/megatron/core/datasets/t5_dataset.py
@@ -314,7 +314,7 @@ def __getitem__(self, idx: int) -> Dict[str, Union[int, numpy.ndarray]]:
         # For padded sequences, ensure the embedding layer can map the token ID
         encoder_input[encoder_input == self._pad_token_id] = 0
         decoder_input[decoder_input == self._pad_token_id] = 0
-        labels[labels == self._pad_token_id] = 0
+        decoder_output[decoder_output == self._pad_token_id] = 0
 
         return {
             "text_enc": encoder_input,
diff --git a/megatron/core/dist_checkpointing/dict_utils.py b/megatron/core/dist_checkpointing/dict_utils.py
index eecf7674ef0..1af4ae81af5 100644
--- a/megatron/core/dist_checkpointing/dict_utils.py
+++ b/megatron/core/dist_checkpointing/dict_utils.py
@@ -217,8 +217,25 @@ def dict_list_map_outplace(f: Callable[[U], V], x: Union[Dict, List, U]) -> Unio
         return f(x)
 
 
+def _is_optimizer_param_state_key(key: Tuple) -> bool:
+    """Check if key path matches (..., 'optimizer', ..., 'param_state', ...)."""
+    try:
+        idx = key.index("optimizer")
+    except ValueError:
+        return False
+    try:
+        key.index("param_state", idx + 1)
+    except ValueError:
+        return False
+    return True
+
+
 def merge(x1: Union[dict, list], x2: Union[dict, list], key: Tuple[Union[str, int], ...] = ()):
-    """Merges dicts and lists recursively."""
+    """Merges dicts and lists recursively.
+
+    For optimizer param_state paths, allows x1 to be longer than x2
+    (dp_reshardable padding entries are truncated).
+    """
     if isinstance(x1, dict) and isinstance(x2, dict):
         for k, v2 in x2.items():
             if k not in x1:
@@ -227,10 +244,13 @@ def merge(x1: Union[dict, list], x2: Union[dict, list], key: Tuple[Union[str, in
                 x1[k] = merge(x1[k], v2, key=key + (k,))
     elif isinstance(x1, list) and isinstance(x2, list):
         if len(x1) != len(x2):
-            raise ValueError(
-                f"Cannot merge two lists with different lengths ({len(x1)} and {len(x2)}, "
-                f"encountered at level {key})"
-            )
+            if _is_optimizer_param_state_key(key) and len(x1) > len(x2):
+                del x1[len(x2):]
+            else:
+                raise ValueError(
+                    f"Cannot merge two lists with different lengths ({len(x1)} and {len(x2)}, "
+                    f"encountered at level {key})"
+                )
         for i, v2 in enumerate(x2):
             x1[i] = merge(x1[i], v2, key=key + (i,))
     else:
diff --git a/megatron/core/dist_checkpointing/mapping.py b/megatron/core/dist_checkpointing/mapping.py
index dfe7e7df55b..64c4c399ef9 100644
--- a/megatron/core/dist_checkpointing/mapping.py
+++ b/megatron/core/dist_checkpointing/mapping.py
@@ -131,7 +131,12 @@ class with `from_rank_offsets` or `from_rank_offsets_flat` constructors.
                     )
 
         if self.flattened_range is not None:
-            raise CheckpointingException("ShardedTensor.flattened_range is not supported.")
+            if not _logged_deprecations.get("flattened_range", False):
+                logger.warning(
+                    "ShardedTensor.flattened_range is deprecated."
+                    " Use latest DistributedOptimizer formats."
+                )
+                _logged_deprecations["flattened_range"] = True
 
     @property
     def has_regular_grid(self):
diff --git a/megatron/core/dist_checkpointing/strategies/base.py b/megatron/core/dist_checkpointing/strategies/base.py
index 53422b362f6..e4d80517d93 100644
--- a/megatron/core/dist_checkpointing/strategies/base.py
+++ b/megatron/core/dist_checkpointing/strategies/base.py
@@ -213,3 +213,4 @@ def save(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Union[str,
         """Each async strategy can be trivially used as a sync strategy."""
         async_request = self.async_save(sharded_state_dict, checkpoint_dir)
         async_request.execute_sync()
+        del async_request
diff --git a/megatron/core/dist_checkpointing/strategies/common.py b/megatron/core/dist_checkpointing/strategies/common.py
index 41c21d93d7d..ef80f72d6fe 100644
--- a/megatron/core/dist_checkpointing/strategies/common.py
+++ b/megatron/core/dist_checkpointing/strategies/common.py
@@ -86,7 +86,7 @@ def load_common(self, checkpoint_dir: Union[str, Path]):
                 msc = MultiStorageClientFeature.import_package()
                 return msc.torch.load(load_path, map_location='cpu')
             else:
-                return torch.load(load_path, map_location='cpu')
+                return torch.load(load_path, map_location='cpu', weights_only=False)
         except FileNotFoundError as e:
             err_msg = f'Common file {load_path} does not exist'
             if MultiStorageClientFeature.is_enabled():
diff --git a/megatron/core/dist_checkpointing/strategies/filesystem_async.py b/megatron/core/dist_checkpointing/strategies/filesystem_async.py
index b23c4e9893d..6bec564d72a 100644
--- a/megatron/core/dist_checkpointing/strategies/filesystem_async.py
+++ b/megatron/core/dist_checkpointing/strategies/filesystem_async.py
@@ -83,6 +83,7 @@ def __init__(
         *args,
         separation_hint: Optional[str] = None,
         use_msc: bool = False,
+        sequential: bool = False,
         **kwargs,
     ):
         self.checkpoint_dir = path
@@ -100,6 +101,7 @@ def __init__(
         self.write_buckets: Optional[List[WriteBucket]] = None
         self.results_queue: Optional[mp.Queue] = None
         self.separation_hint = separation_hint
+        self.sequential = sequential
 
     def prepare_write_data(self, plan: SavePlan, planner: SavePlanner) -> None:
         """
@@ -203,7 +205,9 @@ def get_save_function_and_args(self) -> Tuple[Optional[Callable], Optional[Calla
             return None, None, []
         transform_list = [self.transforms] if hasattr(self, "transforms") else []
         return (
-            partial(self.write_preloaded_data_multiproc, transform_list, self.use_msc),
+            partial(
+                self.write_preloaded_data_multiproc, transform_list, self.use_msc, self.sequential
+            ),
             partial(self.preload_tensors, self.write_buckets, True),
             [torch.distributed.get_rank(), self.write_buckets, self.results_queue],
         )
@@ -222,10 +226,13 @@ def preload_tensors(write_buckets: List[WriteBucket], non_blocking=True) -> List
 
         for bucket in write_buckets:
             file_name, storage_key, (bytes_data, tensor_data) = bucket
-            tensor_data = [
-                (item, tensor.to("cpu", non_blocking=non_blocking)) for item, tensor in tensor_data
-            ]
-            result.append((file_name, storage_key, (bytes_data, tensor_data)))
+            tensor_list = []
+            for item, tensor in tensor_data:
+                # we belive these tensors are detached from the model trainers
+                tensor_list.append((item, tensor.to("cpu", non_blocking=non_blocking)))
+                # This is required for `PersistentAsyncCaller` to remove reference
+                del tensor
+            result.append((file_name, storage_key, (bytes_data, tensor_list)))
         if non_blocking:
             torch.cuda.synchronize()
         return result
@@ -235,6 +242,7 @@ def preload_tensors(write_buckets: List[WriteBucket], non_blocking=True) -> List
     def write_preloaded_data_multiproc(
         transform_list: List[_StorageWriterTransforms],
         use_msc: bool,
+        sequential: bool,
         rank: int,
         write_buckets: List[WriteBucket],
         global_results_queue: mp.Queue,
@@ -267,10 +275,18 @@ def write_preloaded_data_multiproc(
         local_results_queue = ctx.Queue()
         count_queue = ctx.JoinableQueue()
         p_list = []
+
+        def check_local_output(local_results_or_exc, local_proc_idx):
+            if isinstance(local_results_or_exc, Exception):
+                err_msg = (
+                    f"Local process {local_proc_idx} encountered"
+                    f" an error: {local_results_or_exc}"
+                )
+                logger.error(err_msg)
+            assert isinstance(local_results_or_exc, list), type(local_results_or_exc)
+
         for i, write_bucket in enumerate(write_buckets):
             try:
-                count_queue.put(i)
-
                 kwargs = {
                     "local_proc_idx": i,
                     "write_bucket": write_bucket,
@@ -285,20 +301,38 @@ def write_preloaded_data_multiproc(
                     # Remove the inspect after the test_async_save.py is fixed.
                     signature = inspect.signature(FileSystemWriterAsync.write_preloaded_data)
                     if len(signature.parameters) > 6:
-                        kwargs["use_msc"] = use_msc
-
-                p_list.append(
-                    ctx.Process(
-                        target=partial(FileSystemWriterAsync.write_preloaded_data, transform_list),
-                        kwargs=kwargs,
+                        kwargs['use_msc'] = use_msc
+                # Parallel Writers are required
+                if i < len(write_buckets) - 1 and not sequential:
+                    count_queue.put(i)
+                    p_list.append(
+                        ctx.Process(
+                            target=partial(
+                                FileSystemWriterAsync.write_preloaded_data, transform_list
+                            ),
+                            kwargs=kwargs,
+                        )
                     )
-                )
+                else:
+                    kwargs['count_queue'] = None
+                    kwargs['results_queue'] = None
+                    logger.debug('FileSystemWriterAsync: master worker started')
+                    local_output = FileSystemWriterAsync.write_preloaded_data(
+                        transform_list, **kwargs
+                    )
+                    if local_output is not None:
+                        logger.debug(
+                            'FileSystemWriterAsync: master worker results successfully collected'
+                        )
+                        check_local_output(local_output[1], local_output[0])
+                        write_results_or_exc[local_output[0]] = local_output[1]
+
             except Exception as e:
                 err_msg = f"An error is caught while a proc {i} is created, error: {e}"
                 logger.error(err_msg)
                 write_results_or_exc = RuntimeError(err_msg)
 
-        if not isinstance(write_results_or_exc, Exception):
+        if not isinstance(write_results_or_exc, Exception) and len(p_list) > 0 and not sequential:
             for p in p_list:
                 p.start()
 
@@ -308,7 +342,7 @@ def write_preloaded_data_multiproc(
             count_queue.join()
             # At this point, all workers completed, so the queue should have exactly
             # `len(write_buckets)` items
-            for proc_idx in range(len(write_buckets)):
+            for proc_idx in range(0, len(write_buckets) - 1):
                 try:
                     local_proc_idx, local_results_or_exc = local_results_queue.get()
                 except queue.Empty:
@@ -318,19 +352,10 @@ def write_preloaded_data_multiproc(
                     )
                     break
                 else:
-                    if isinstance(local_results_or_exc, Exception):
-                        err_msg = (
-                            f"Local process {local_proc_idx} encountered"
-                            f" an error: {local_results_or_exc}"
-                        )
-                        logger.error(err_msg)
-                        write_results_or_exc = local_results_or_exc
-                        break
-                    assert isinstance(local_results_or_exc, list), type(local_results_or_exc)
+                    check_local_output(local_results_or_exc, local_proc_idx)
                     write_results_or_exc[local_proc_idx] = local_results_or_exc
                     p_list[local_proc_idx].join()
-
-            logger.debug("FileSystemWriterAsync: collected worker results successfully")
+            logger.debug('FileSystemWriterAsync: collected worker results successfully')
 
         global_results_queue.put(write_results_or_exc)
 
@@ -347,7 +372,7 @@ def write_preloaded_data(
         count_queue: mp.JoinableQueue,
         use_fsync: bool,
         **kwargs,
-    ) -> None:
+    ) -> Union[Tuple[int, Exception], None]:
         """
         Performs actual data saving to storage.
 
@@ -405,17 +430,19 @@ def write_preloaded_data(
         except Exception as e:
             logger.debug(f"{local_proc_idx} failed")
             local_output = (local_proc_idx, e)  # type: ignore[assignment]
-
-        results_queue.put(local_output)
-        # Signal this process is done.
-        count_queue.get()
-        count_queue.task_done()
+        if results_queue is not None:
+            results_queue.put(local_output)
+        if count_queue is not None:
+            # Signal this process is done.
+            count_queue.get()
+            count_queue.task_done()
 
         mem_after = _process_memory()
         logger.debug(
             f"{local_proc_idx} consumed: {mem_after - mem_before},"
             f" before: {mem_before}, after: {mem_after}"
         )
+        return local_output
 
     def write_data(self, plan: SavePlan, planner: SavePlanner) -> Future[List[WriteResult]]:
         """Write all items from ``plan``."""
diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py
index a5b6c009ba4..ec5f50a14ce 100644
--- a/megatron/core/dist_checkpointing/strategies/torch.py
+++ b/megatron/core/dist_checkpointing/strategies/torch.py
@@ -503,10 +503,12 @@ def __init__(
     def _validate_global_shapes(self, metadata, sharded_tensors):
         for sh_ten in sharded_tensors:
             if sh_ten.key not in metadata.state_dict_metadata:
-                raise KeyError(
-                    f"{sh_ten.key} from model not in state dict:"
-                    f" {sorted(metadata.state_dict_metadata.keys())}"
-                )
+                # raise KeyError(
+                #     f"{sh_ten.key} from model not in state dict:"
+                #     f" {sorted(metadata.state_dict_metadata.keys())}"
+                # )
+                print(f"{sh_ten.key} from model not in state dict, will skip")
+                continue
             loaded_shape = metadata.state_dict_metadata[sh_ten.key].size
             expected_shape = sh_ten.global_shape
             if loaded_shape != expected_shape:
@@ -530,7 +532,7 @@ def _temporarily_bypass_shape_validation(self):
         tensor_metadata = self.metadata.state_dict_metadata
         metadata_with_sizes = [
             (tensor_metadata[key], tensor_metadata[key].size, sharded_tensor)
-            for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items()
+            for key, sharded_tensor in self.allow_shape_mismatch_sharded_tensors.items() if key in tensor_metadata
         ]
         try:
             # Temporarily set sizes to expected shapes
@@ -599,7 +601,7 @@ def __init__(
         backend: str,
         version: int,
         keep_only_main_replica: bool = True,
-        thread_count: int = 2,
+        thread_count: int = 1,
         cached_metadata: bool = False,
         separation_hint: Optional[str] = None,
     ):
@@ -658,12 +660,19 @@ def async_save(
             )
         )
         pyt_state_dict = mcore_to_pyt_state_dict(sharded_state_dict, False)
+
+        sequential = False
+        if self.separation_hint is not None and self.thread_count <= 1:
+            self.thread_count = 2
+            sequential = True
+
         # Use PyT saving mechanism
         writer = FileSystemWriterAsync(
             checkpoint_dir,
             separation_hint=self.separation_hint,
             thread_count=self.thread_count,
             use_msc=MultiStorageClientFeature.is_enabled(),
+            sequential=sequential,
         )
         # This should be set differently if we run in a smaller process group than the default
         coordinator = 0
@@ -697,8 +706,12 @@ def async_save(
             writer,
             None,
             coordinator,
+            # flatten_sharded_tensors=False: MCore doesn't use nested ShardedTensors (FSDP 2D),
+            # so skip the expensive traverse_state_dict copy in _flatten_sharded_tensors
             planner=MCoreSavePlanner(
-                dedup_replicated_tensors=not self.keep_only_main_replica, flatten_state_dict=False
+                dedup_replicated_tensors=not self.keep_only_main_replica,
+                flatten_state_dict=False,
+                flatten_sharded_tensors=False,
             ),
             cached_ckpt_structure=args_cached_plans,
             loaded_all_plans=loaded_all_plans,
@@ -802,6 +815,9 @@ def load(self, sharded_state_dict: ShardedStateDict, checkpoint_dir: Path) -> St
             planner=MCoreLoadPlanner(
                 shapes_validation_sharded_tensors=flexible_shape_sharded_tensors,
                 allow_shape_mismatch_sharded_tensors=allow_shape_mismatch_sharded_tensors,
+                flatten_state_dict=False,
+                flatten_sharded_tensors=False,
+                allow_partial_load=True,
             ),
         )
 
diff --git a/megatron/core/dist_checkpointing/validation.py b/megatron/core/dist_checkpointing/validation.py
index 48f2bda8737..0e5d6a011a8 100644
--- a/megatron/core/dist_checkpointing/validation.py
+++ b/megatron/core/dist_checkpointing/validation.py
@@ -433,19 +433,25 @@ def validate_sharding_integrity(
     for rank, rank_shardings in enumerate(global_metadata):
         for sharding in rank_shardings:
             key_shardings[sharding.key].append((rank, sharding))
+    errors = []
     for key, shardings in key_shardings.items():
         if isinstance(shardings[0][1], ShardedObject):
-            _validate_objects_for_key(shardings)
+            errors.extend(_validate_objects_for_key(shardings))
         else:
-            _validate_sharding_for_key(shardings)
+            errors.extend(_validate_sharding_for_key(shardings))
 
+    if errors:
+        errors = '\n'.join(str(e) for e in errors)
+        raise CheckpointingException(f'Invalid sharding pattern validation. Errors: {errors}')
 
-def _validate_sharding_for_key(rank_sharding: List[Tuple[int, ShardedTensor]]):
+
+def _validate_sharding_for_key(
+    rank_sharding: List[Tuple[int, ShardedTensor]]
+) -> List[CheckpointingException]:
     some_rank_shard = rank_sharding[0][1]
     global_shape = some_rank_shard.global_shape
     local_shape = some_rank_shard.local_shape
     dtype = some_rank_shard.dtype
-    has_flattened_range = some_rank_shard.flattened_range is not None
     has_regular_sharding_grid = some_rank_shard.has_regular_grid
     for rank, sharding in rank_sharding:
         assert sharding.dtype == dtype, (sharding.dtype, dtype, some_rank_shard)
@@ -465,16 +471,21 @@ def _validate_sharding_for_key(rank_sharding: List[Tuple[int, ShardedTensor]]):
                 some_rank_shard,
             )
 
+    errors = []
     if not has_regular_sharding_grid:
         # In case of uneven sharding we defer the validation to DCP
-        return
+        return errors
 
     shard_access_cnt = _compute_shards_access(rank_sharding)
     if not torch.all(shard_access_cnt == 1):
-        raise CheckpointingException(
-            f"Invalid access pattern for {rank_sharding[0][1]}: {shard_access_cnt}"
+        errors.append(
+            CheckpointingException(
+                f'Invalid access pattern for {rank_sharding[0][1]}: {shard_access_cnt}'
+            )
         )
 
+    return errors
+
 
 def _compute_shards_access(rank_sharding):
     shard_access_cnt = torch.zeros(
@@ -486,20 +497,24 @@ def _compute_shards_access(rank_sharding):
     return shard_access_cnt
 
 
-def _validate_objects_for_key(sharded_objects: List[ShardedObject]):
+def _validate_objects_for_key(sharded_objects: List[ShardedObject]) -> List[CheckpointingException]:
     """Ensure uniqueness of saved objects."""
     unique_keys = [
         sh_obj.unique_key for _, sh_obj in sharded_objects if is_main_replica(sh_obj.replica_id)
     ]
+    errors = []
     if len(unique_keys) != len(set(unique_keys)):
         duplicates = {k: cnt for k, cnt in Counter(unique_keys).items() if cnt > 1}
         logger.error(f"Duplicate ShardedObject keys and counts: {duplicates}")
-        raise CheckpointingException(f"Duplicate ShardedObject keys: {list(duplicates.keys())}")
+        errors.append(
+            CheckpointingException(f'Duplicate ShardedObject keys: {list(duplicates.keys())}')
+        )
     expected_shard_num = np.prod(sharded_objects[0][1].global_shape)
     if len(unique_keys) != expected_shard_num:
         err_msg = f"Invalid access pattern: {expected_shard_num - len(unique_keys)} ShardedObject are missing."
         logger.error(f"{err_msg} Existing shards: {unique_keys}")
-        raise CheckpointingException(err_msg)
+        errors.append(CheckpointingException(err_msg))
+    return errors
 
 
 def determine_global_metadata(
diff --git a/megatron/core/distributed/distributed_data_parallel_config.py b/megatron/core/distributed/distributed_data_parallel_config.py
index 7e2d19e5ce9..468bfafbff7 100644
--- a/megatron/core/distributed/distributed_data_parallel_config.py
+++ b/megatron/core/distributed/distributed_data_parallel_config.py
@@ -122,7 +122,24 @@ class DistributedDataParallelConfig:
       This option will cause additional memory overhead, however, it is necessary for
       to register user buffer (nccl_ub=True) for the Megatron FSDP. 
       This option will be automatically set to True when nccl_ub=True.
-   """
+    """
+
+    fsdp_db_use_persist_buf_on_alloc_fail: bool = False
+    """Whether to fall back to persistent buffer when a bucket does not
+       fit FSDP double buffer size. If true, FSDP will use the persistently 
+       allocated buffer for the bucket that does not fit, it will enable NCCL 
+       user buffer with the cost of more memory usage. If false, FSDP will use
+       Dynamic memory allocator, NCCL user buffer won't not enabled, which 
+       usually leads to low performance. 
+    """
+
+    fsdp_all_gather_in_start_param_sync: bool = True
+    """
+    If True, use all-gather during the initial Megatron-FSDP parameter
+    synchronization step. This can increase overlap between the first
+    parameter all-gather and computation, helping to better hide the
+    initial communication cost.
+    """
 
     fsdp_all_gather_in_start_param_sync: bool = True
     """
diff --git a/megatron/core/distributed/finalize_model_grads.py b/megatron/core/distributed/finalize_model_grads.py
index a52592bb269..ca6bdd354ce 100644
--- a/megatron/core/distributed/finalize_model_grads.py
+++ b/megatron/core/distributed/finalize_model_grads.py
@@ -299,7 +299,11 @@ def _update_router_expert_bias(model: List[torch.nn.Module], config: Transformer
     expert_bias_list = []
     for model_chunk in model:
         for module in get_attr_wrapped_model(model_chunk, 'modules')():
-            if hasattr(module, 'expert_bias'):
+            # Only update expert_bias if this module is in the training mode. There are special
+            # cases where only the student is in training mode but the teacher is in eval mode
+            # when using online knoweldge-distillation with Model-Optimizer. In this case, we want
+            # to avoid updating teacher's expert_bias.
+            if hasattr(module, 'expert_bias') and module.training:
                 tokens_per_expert_list.append(module.local_tokens_per_expert)
                 expert_bias_list.append(module.expert_bias)
     # For hybrid models with both MoE and Dense layers, this list can be empty.
diff --git a/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py b/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
index 5bf543fdc5c..8848d93666e 100644
--- a/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
+++ b/megatron/core/distributed/fsdp/mcore_fsdp_adapter.py
@@ -42,7 +42,7 @@
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.transformer_layer import TransformerLayer
-from megatron.core.utils import log_single_rank
+from megatron.core.utils import is_te_min_version, log_single_rank
 
 try:
     from megatron.core.distributed.fsdp.src.megatron_fsdp import FSDPDistributedIndex, MegatronFSDP
@@ -85,6 +85,18 @@ def __init__(
 
         self.megatron_fsdp_dist_index = self._init_dist_index(pg_collection)
 
+        if config.gradient_accumulation_fusion:
+            assert (
+                self.megatron_fsdp_dist_index.get_dp_group(is_expert_parallel=True).size() == 1
+            ), (
+                "Megatron-FSDP with gradient_accumulation_fusion does not support "
+                "data parallelism when expert parallelism is enabled."
+            )
+            assert is_te_min_version("2.10"), (
+                "Megatron-FSDP with gradient_accumulation_fusion requires "
+                "Transformer Engine version 2.10 or higher."
+            )
+
         self.bucket_size = self.ddp_config.bucket_size
         if disable_bucketing:
             self.bucket_size = None
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
index 32c0ffde2ad..fd61fb6ce2c 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/distributed_data_parallel_config.py
@@ -127,6 +127,15 @@ class DistributedDataParallelConfig:
     initial communication cost.
     """
 
+    fsdp_db_use_persist_buf_on_alloc_fail: bool = False
+    """Whether to fall back to persistent buffer when a bucket does not
+       fit FSDP double buffer size. If true, FSDP will use the persistently 
+       allocated buffer for the bucket that does not fit, it will enable NCCL 
+       user buffer with the cost of more memory usage. If false, FSDP will use
+       Dynamic memory allocator, NCCL user buffer won't not enabled, which 
+       usually leads to low performance. 
+    """
+
     outer_dp_sharding_strategy: str = 'no_shard'
     """
     Sharding strategy for outer data parallel group in Hybrid Sharded Data Parallel (HSDP) mode.
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
index 7a118a8424b..921d3d40c37 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/fully_shard.py
@@ -20,6 +20,7 @@
 import torch
 from torch.distributed import DeviceMesh
 from torch.distributed.device_mesh import init_device_mesh
+from torch.distributed.tensor import DTensor
 
 from .megatron_fsdp import MegatronFSDP
 from .uneven_dtensor import preprocess_state_dict_for_uneven_dtensor
@@ -97,6 +98,7 @@ def fully_shard_model(
     keep_fp8_transpose_cache: bool = False,
     nccl_ub: bool = False,
     fsdp_double_buffer: bool = False,
+    fsdp_db_use_persist_buf_on_alloc_fail: bool = False,
     disable_symmetric_registration: bool = False,
     enable_fine_grained_param_gather: bool = False,
 ) -> torch.nn.Module:
@@ -231,6 +233,10 @@ class that schedules the sharding lifecycle of the model parameters and gradient
         fsdp_double_buffer (bool):
             Whether to use double buffer for FSDP. Defaults to False.
 
+        fsdp_db_use_persist_buf_on_alloc_fail (bool):
+            Whether to fall back to persistent buffer allocator when a bucket does not
+            fit FSDP double buffer size.
+
         disable_symmetric_registration (bool):
             Whether to disable symmetric (window) registration for NCCL UB registration.
             This option forces conventional (local) UB registration when nccl_ub is set.
@@ -336,6 +342,7 @@ class that schedules the sharding lifecycle of the model parameters and gradient
         keep_fp8_transpose_cache=keep_fp8_transpose_cache,  # pylint: disable=C0301
         nccl_ub=nccl_ub,
         fsdp_double_buffer=fsdp_double_buffer or nccl_ub,
+        fsdp_db_use_persist_buf_on_alloc_fail=fsdp_db_use_persist_buf_on_alloc_fail,
         disable_symmetric_registration=disable_symmetric_registration,
         check_for_nan_in_grad=check_for_nan_in_grad,
     )
@@ -450,6 +457,20 @@ def fully_shard_optimizer(
     optimizer_step_base_func = type(optimizer).step
     optimizer_zero_grad_base_func = type(optimizer).zero_grad
 
+    # Pre-initialize the optimizer state for checkpoint loading via DCP.
+    for group in optimizer.param_groups:
+        for param in group["params"]:
+            if param.numel() == 0 or (
+                hasattr(param, "_local_tensor") and param._local_tensor.numel() == 0
+            ):
+                # Avoid FusedAdam errors on empty tensor input.
+                continue
+            # Optimizer state is built from wgrad.
+            param.grad = torch.zeros_like(param)
+    # Non-lazy optimizer state initialization.
+    optimizer.step()
+    optimizer.zero_grad()
+
     # Define a new optimizer.step() method that distributes optimizer state and gradients,
     # waits for asynchronous gradient reduce-scatter work to be completed, and updates
     # model weights. These options can be turned off via arguments in optimizer.step().
@@ -503,13 +524,88 @@ def megatron_fsdp_optimizer_zero_grad(optimizer, *args, **kwargs):
     optimizer.zero_grad = types.MethodType(megatron_fsdp_optimizer_zero_grad, optimizer)
 
     if preproc_state_dict_for_dcp_ckpt:
-        # Requires a non-empty, DTensor-type optimizer state dictionary.
-        # This is satisfied naturally by calling optimizer.state_dict()
-        # after the first optimizer.step() initializes the state to match
-        # the Megatron-FSDP
-        optimizer_state_dict = optimizer.state_dict()
+
+        def dict_nested_shallow_copy(d: dict):
+            """Create a nested shallow copy of a dict. Same values, different pointers."""
+            if not isinstance(d, dict):
+                return d
+            return {
+                k: dict_nested_shallow_copy(v) if isinstance(v, dict) else v for k, v in d.items()
+            }
+
+        def preprocess_optimizer_state_dict_for_uneven_dtensor(optimizer, state_dict):
+            """
+            Hook that mocks the global optimizer state for unevenly-distributed
+            DTensors, as the optimizer state is only initialized for non-empty
+            parameters, and preprocesses the optimizer `state_dict` DTensors
+            in-place for Torch DCP.
+            """
+            # Retrieve a template optimizer state.
+            optim_state_template = next(iter(optimizer.state.values())) if optimizer.state else {}
+            # All-gather the optimizer state keys as this rank could have empty state.
+            optim_state_dtensor_keys = [None] * torch.distributed.get_world_size()
+            torch.distributed.all_gather_object(
+                optim_state_dtensor_keys,
+                [
+                    # Only track keys associated with DTensors for DCP.
+                    key
+                    for key, val in optim_state_template.items()
+                    if isinstance(val, DTensor)
+                ],
+            )
+            optim_state_dtensor_keys = list(
+                set([key for state in optim_state_dtensor_keys for key in state])
+            )
+
+            # NOTE(@cspades): Re-construct the Megatron-FSDP distributed parameter
+            # to index mapping as implemented in torch.optim.Optimizer.state_dict():
+            # https://github.com/pytorch/pytorch/blob/main/torch/optim/optimizer.py
+            # Simply put, the index maps to the very first appearance of id(param)
+            # looping through all parameters in all groups with memory address
+            # equivalent to the distributed parameter managed by Megatron-FSDP.
+            param_state_idx = {}
+            idx = 0
+            # For all empty parameters, mock empty DTensors for all empty parameters
+            # of Megatron-FSDP's unevenly-distributed optimizer state into a shallow
+            # copy of the state dictionary to synchronize and pre-process a global
+            # variant of the optimizer state in preparation for Torch DCP. This allows
+            # us to sync the non-empty DTensor shard metadata across sharding groups
+            # while excluding empty DTensor shards from the optimizer checkpoint.
+            optim_state_extended = dict_nested_shallow_copy(state_dict)
+            for param_group in optimizer.param_groups:
+                for param in param_group["params"]:
+                    # Update the parameter state index.
+                    # For shared params, use same index.
+                    if id(param) not in param_state_idx:
+                        # New parameter, assign an index.
+                        param_state_idx[id(param)] = idx
+                        idx += 1
+                    if param in optimizer.state or not isinstance(param, DTensor):
+                        # Only mock optimizer state for parameters that are missing state.
+                        # No need to mock for non-DTensor params. Not relevant to DCP.
+                        continue
+                    for key in optim_state_dtensor_keys:
+                        # Construct a mock DTensor state for the empty DTensor parameter.
+                        param_idx = param_state_idx[id(param)]
+                        optim_state_extended["state"].setdefault(param_idx, {})[key] = (
+                            DTensor.from_local(
+                                local_tensor=torch.empty(0, dtype=param.dtype, device=param.device),
+                                device_mesh=param.device_mesh,
+                                placements=param.placements,
+                                shape=param.shape,
+                                stride=param.stride(),
+                            )
+                        )
+
+            # Synchronize and preprocess DTensor metadata for Torch DCP.
+            preprocess_state_dict_for_uneven_dtensor(optim_state_extended)
+
+        # Attach the optimizer state_dict() post-hook to prepare DTensors for Torch DCP.
+        # args = (optimizer, state_dict)
         optimizer.register_state_dict_post_hook(
-            lambda *args, **kwargs: preprocess_state_dict_for_uneven_dtensor(optimizer_state_dict)
+            lambda *args, **kwargs: preprocess_optimizer_state_dict_for_uneven_dtensor(
+                args[0], args[1]
+            )
         )
 
     # Return the in-place modified optimizer.
@@ -544,6 +640,7 @@ def fully_shard(
     keep_fp8_transpose_cache: bool = False,
     nccl_ub: bool = False,
     fsdp_double_buffer: bool = False,
+    fsdp_db_use_persist_buf_on_alloc_fail: bool = False,
     disable_symmetric_registration: bool = False,
     enable_fine_grained_param_gather: bool = False,
 ) -> tuple[MegatronFSDP, torch.optim.Optimizer]:
@@ -592,6 +689,7 @@ def fully_shard(
         keep_fp8_transpose_cache=keep_fp8_transpose_cache,
         nccl_ub=nccl_ub,
         fsdp_double_buffer=fsdp_double_buffer,
+        fsdp_db_use_persist_buf_on_alloc_fail=fsdp_db_use_persist_buf_on_alloc_fail,
         disable_symmetric_registration=disable_symmetric_registration,
         enable_fine_grained_param_gather=enable_fine_grained_param_gather,
     )
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
index 671487a30eb..40198673ee7 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py
@@ -136,6 +136,8 @@ class MegatronFSDP(torch.nn.Module):
         fsdp_double_buffer (bool): Whether to use persistently allocated double buffers
             for the temporary memory needed in the FSDP communication. This flag is
             automatically set to True when nccl_ub is True.
+        fsdp_db_use_persist_buf_on_alloc_fail (bool): Whether to fall back to persistent buffer
+            allocator when a bucket does not fit FSDP double buffer size.
         disable_symmetric_registration (bool): Whether to disable symmetric (window) registration
             for NCCL userbuffer registration. This option will force to use conventional (local)
             userbuffer registration when nccl_ub is set.
@@ -155,6 +157,7 @@ class MegatronFSDP(torch.nn.Module):
         ...     keep_fp8_transpose_cache=False,
         ...     nccl_ub=False,
         ...     fsdp_double_buffer=False,
+        ...     fsdp_db_use_persist_buf_on_alloc_fail=False,
         ...     disable_symmetric_registration=False,
         ... )
     """
@@ -173,6 +176,7 @@ def __init__(
         keep_fp8_transpose_cache: bool = False,
         nccl_ub: bool = False,
         fsdp_double_buffer: bool = False,
+        fsdp_db_use_persist_buf_on_alloc_fail: bool = False,
         disable_symmetric_registration: bool = False,
         enable_fine_grained_param_gather_hook: bool = False,
     ):
@@ -217,6 +221,7 @@ def __init__(
                 keep_fp8_transpose_cache=keep_fp8_transpose_cache,  # pylint: disable=C0301
                 nccl_ub=nccl_ub,
                 fsdp_double_buffer=fsdp_double_buffer or nccl_ub,
+                fsdp_db_use_persist_buf_on_alloc_fail=fsdp_db_use_persist_buf_on_alloc_fail,
                 disable_symmetric_registration=disable_symmetric_registration,
             )
         else:
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
index aabdd010ed9..d1b3e6b4a99 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/param_and_grad_buffer.py
@@ -658,7 +658,13 @@ class FixedPoolAllocator(TemporaryBucketAllocator):
     deallocation of temporary buffers during FSDP operations.
     """
 
-    def __init__(self, name: str, fsdp_param_groups: List["ParameterGroup"], size: int = 2):
+    def __init__(
+        self,
+        name: str,
+        fsdp_param_groups: List["ParameterGroup"],
+        size: int = 2,
+        fallback_to_persistent_buffer: bool = False,
+    ):
         self.name = name
         self.fsdp_param_groups = fsdp_param_groups
         self.size = size  # Number of buffers in the pool (default is 2 for double buffering)
@@ -691,6 +697,29 @@ def __init__(self, name: str, fsdp_param_groups: List["ParameterGroup"], size: i
         ), "Found no FSDP units to use fixed-size buffering"
         self.fsdp_double_buffer_units = fsdp_units_to_double_buffer
 
+        if torch.distributed.get_rank() == 0:
+            for bucket_id, param_group in enumerate(fsdp_param_groups):
+                if (
+                    param_group.fsdp_unit_id == -1
+                    or param_group.fsdp_unit_id is None
+                    or param_group.fsdp_unit_id not in self.fsdp_double_buffer_units
+                ):
+                    logging.info(
+                        f"FSDP unit (id={param_group.fsdp_unit_id}) does not fit "
+                        "in FixedPoolAllcator"
+                    )
+                    if fallback_to_persistent_buffer is False:
+                        logging.info(
+                            "It will fall back to dynamic memory allocator, NCCL user "
+                            "buffer is not supported"
+                        )
+                    else:
+                        logging.info(
+                            "It will be allocated a persistent buffer. If the memory "
+                            "budget is tight, set "
+                            "trainer.strategy.ddp.fsdp_db_use_persist_buf_on_alloc_fail to False."
+                        )
+
         # Initialize buffer group status.
         # Each buffer group represents a set of buffers associated with an FSDP unit's bucket group.
         self.idle_buffer = []  # List of available (buf_group_id, offset) tuples.
@@ -703,6 +732,7 @@ def __init__(self, name: str, fsdp_param_groups: List["ParameterGroup"], size: i
                 self.idle_buffer.append((buf_group_id, bucket_offset))
 
         # Fallback allocator used if the fixed pool allocator cannot fulfill a request.
+        self.fallback_to_persistent_buffer = fallback_to_persistent_buffer
         self.backup_allocator = TemporaryBucketAllocator()
 
     def _is_two_bucket_group_equal(self, group_a, group_b):
@@ -755,28 +785,31 @@ def allocate(
                 f"current using_buffer: {self.using_buffer} \n"
                 f"current idle_buffer: {self.idle_buffer}"
             )
-            # Synchronization is required before the allocation for the user buffer
-            if mem_alloc_context is not None and mem_alloc_context != nullcontext:
-                # Check if a new buffer allocation is required
-                if (
-                    self.allocation_tracker.get((buffer_name, dtype), None) is None
-                    or self.allocation_tracker[(buffer_name, dtype)] < size
-                ):
-                    # Requires synchronization for new buffer allocation
-                    self.allocation_tracker[(buffer_name, dtype)] = size
-                    torch.cuda.synchronize()
-            return Bucket(
-                data=get_global_memory_buffer().get_tensor(
-                    [size], dtype=dtype, name=buffer_name, mem_alloc_context=mem_alloc_context
-                )
+        elif self.fallback_to_persistent_buffer is True:
+            buffer_name = f"{self.name}_not_fit_in_fixed_pool_{bucket_id}_{size}_{dtype}_{device}"
+        else:
+            # If the bucket is not eligible for fixed pool buffering, or no buffer is available,
+            # fall back to dynamic allocation via the backup allocator. This means that we
+            # will do dynamic memory allocation.
+            logging.debug(f"[FSDP] Using backup allocator for {bucket_id} {fsdp_unit_id}")
+            return self.backup_allocator.allocate(
+                bucket_id=bucket_id, size=size, dtype=dtype, device=device
             )
 
-        # If the bucket is not eligible for fixed pool buffering, or no buffer is available,
-        # fall back to dynamic allocation via the backup allocator. This means that we
-        # will do dynamic memory allocation.
-        logging.debug(f"[FSDP] Using backup allocator for {bucket_id} {fsdp_unit_id}")
-        return self.backup_allocator.allocate(
-            bucket_id=bucket_id, size=size, dtype=dtype, device=device
+        # Use buffer_name to get memory from global memory.
+        if mem_alloc_context is not None and mem_alloc_context != nullcontext:
+            # Check if a new buffer allocation is required
+            if (
+                self.allocation_tracker.get((buffer_name, dtype), None) is None
+                or self.allocation_tracker[(buffer_name, dtype)] < size
+            ):
+                # Requires synchronization for new buffer allocation
+                self.allocation_tracker[(buffer_name, dtype)] = size
+                torch.cuda.synchronize()
+        return Bucket(
+            data=get_global_memory_buffer().get_tensor(
+                [size], dtype=dtype, name=buffer_name, mem_alloc_context=mem_alloc_context
+            )
         )
 
     def _get_gbuf_name(self, buf_group_id: int, bucket_index: int):
@@ -795,9 +828,10 @@ def free(self, bucket_id: int):
             self.idle_buffer.append(self.using_buffer[bucket_id])
             del self.using_buffer[bucket_id]
             return
-        # If not managed by fixed pool allocator, delegate to the backup allocator.
-        logging.debug(f"[FSDP] Free from the backup allocator for {bucket_id} {fsdp_unit_id}")
-        self.backup_allocator.free(bucket_id)
+        if self.fallback_to_persistent_buffer is False:
+            # If not managed by fixed pool allocator, delegate to the backup allocator.
+            logging.debug(f"[FSDP] Free from the backup allocator for {bucket_id} {fsdp_unit_id}")
+            self.backup_allocator.free(bucket_id)
 
 
 class DataParallelBuffer:
@@ -1874,7 +1908,10 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
         if self.ddp_config.fsdp_double_buffer and len(self.bucketing_policy.fsdp_unit_modules) > 0:
             UB_BUFFER_NUM = 2
             self.weight_alloc = FixedPoolAllocator(
-                name="fsdp_params", fsdp_param_groups=self.parameter_groups, size=UB_BUFFER_NUM
+                name="fsdp_params",
+                fsdp_param_groups=self.parameter_groups,
+                size=UB_BUFFER_NUM,
+                fallback_to_persistent_buffer=self.ddp_config.fsdp_db_use_persist_buf_on_alloc_fail,
             )
             self.transpose_weight_alloc = FixedPoolAllocator(
                 name="fsdp_fp8_transpose_params",
@@ -1882,7 +1919,10 @@ def _init_each_parameter_group_buffers(self, meta_device_init_fp8_params):
                 size=UB_BUFFER_NUM,
             )
             self.main_grad_alloc = FixedPoolAllocator(
-                name="fsdp_grads", fsdp_param_groups=self.parameter_groups, size=UB_BUFFER_NUM
+                name="fsdp_grads",
+                fsdp_param_groups=self.parameter_groups,
+                size=UB_BUFFER_NUM,
+                fallback_to_persistent_buffer=self.ddp_config.fsdp_db_use_persist_buf_on_alloc_fail,
             )
             self.double_buf_units = self.weight_alloc.fsdp_double_buffer_units
         else:
@@ -3124,9 +3164,6 @@ def wait_for_previous_grad_reduce(
                 grad_reduce_event.wait()
                 free_up_grad_bucket()
 
-        if suggested_queue_size == 0 and self.outer_fsdp_group_grad_reduce:
-            torch.cuda.current_stream().wait_stream(self.outer_fsdp_group_grad_reduce_stream)
-
     def _enforce_double_buffer_limit(self, add_buckets):
         if not self.buffer.ddp_config.fsdp_double_buffer:
             return
diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/uneven_dtensor.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/uneven_dtensor.py
index f0b93056c9c..5df9c2e95c0 100644
--- a/megatron/core/distributed/fsdp/src/megatron_fsdp/uneven_dtensor.py
+++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/uneven_dtensor.py
@@ -58,6 +58,7 @@ def _update_offsets_and_cumulative_shape(
         # TODO: add documentation for the offset calculation
         # Add on the offset of the current mesh dimension
         offsets[shard_dim] += offset
+        # Calculate the global shape using the sum of the sharding dim sizes.
         cumulative_shape[shard_dim] = sum(s[shard_dim] for s in global_shapes)
 
     # Get the shard placements order.
@@ -240,7 +241,9 @@ def preprocess_state_dict_for_uneven_dtensor(state_dict: dict) -> dict:
     visit_dtensor = filter_unflattened_state_dict(
         state_dict, visit_condition=lambda x: isinstance(x, DTensor)
     )
-    for key_chain in visit_dtensor:
+    # Sort the keys, since some state dictionaries are mocked
+    # and extended to include empty global keys.
+    for key_chain in sorted(visit_dtensor):
         # Get the DTensor at the key chain
         dtensor = get_unflattened_state_dict(state_dict, key_chain)
         update_uneven_dtensor_chunk_metadata(dtensor)
diff --git a/megatron/core/distributed/fsdp/src/pyproject.toml b/megatron/core/distributed/fsdp/src/pyproject.toml
index abeff53322e..783030cc809 100644
--- a/megatron/core/distributed/fsdp/src/pyproject.toml
+++ b/megatron/core/distributed/fsdp/src/pyproject.toml
@@ -30,7 +30,6 @@ keywords = [
     "gpu",
     "language",
     "learning",
-    "learning",
     "machine",
     "nvidia",
     "pytorch",
@@ -60,7 +59,7 @@ classifiers = [
 
 [project.urls]
 Download = "https://github.com/NVIDIA/Megatron-LM/releases"
-Homepage = "https://github.com/NVIDIA/Megatron-LM/megatron/core"
+Homepage = "https://github.com/NVIDIA/Megatron-LM"
 
 
 [tool.uv]
diff --git a/megatron/core/enums.py b/megatron/core/enums.py
index 9b76bc52a87..cb378d88e0f 100644
--- a/megatron/core/enums.py
+++ b/megatron/core/enums.py
@@ -8,14 +8,6 @@ class ModelType(enum.Enum):
 
     encoder_or_decoder = 1
 
-    @property
-    def encoder_and_decoder(self):
-        """Deprecated property - use encoder_or_decoder instead."""
-        raise ValueError(
-            "ModelType.encoder_and_decoder is deprecated. Please use ModelType.encoder_or_decoder "
-            "instead."
-        )
-
 
 class Fp8Recipe(str, enum.Enum):
     """FP8 recipe names: delayed, tensorwise, mxfp8, blockwise, custom."""
diff --git a/megatron/core/export/trtllm/trtllm_layers.py b/megatron/core/export/trtllm/trtllm_layers.py
index b777fe40809..bc3ba33d92c 100644
--- a/megatron/core/export/trtllm/trtllm_layers.py
+++ b/megatron/core/export/trtllm/trtllm_layers.py
@@ -90,7 +90,7 @@ def rename_input_layer_names_to_trtllm_layer_names(
         map the original layer name to equivalent trtllm layer name and add layer number back.
         CPU Conversion will pass in model state dict without layer numbers
         (i.e decoder.layers.mlp.linear_fc1.weight of shape [num_layers, hidden_dim, 4 * hidden_dim]) .
-        GPU conversion will pass model state dict with each layer seperated
+        GPU conversion will pass model state dict with each layer separated
         (i.e decoder.layers.2.mlp.linear_fc1.weight of shape [hidden_dim, 4 * hidden_dim]).
 
         Args:
diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py
index ef8527e9e5e..c0bf681fabd 100644
--- a/megatron/core/extensions/transformer_engine.py
+++ b/megatron/core/extensions/transformer_engine.py
@@ -8,13 +8,14 @@
 import pickle
 import warnings
 from contextlib import nullcontext
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, cast
 
 import torch
 import torch.nn.functional as F
 from packaging.version import Version as PkgVersion
 from torch import Tensor
 from torch.nn.parameter import Parameter
+from typing_extensions import override
 
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
 from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding
@@ -43,12 +44,14 @@
 from megatron.core.tensor_parallel.utils import divide
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.mlp import MLP
+from megatron.core.transformer.torch_norm import LayerNormInterface
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.utils import (
     ensure_metadata_has_dp_cp_group,
     is_layer_window_attention,
     make_sharded_tensors_for_checkpoint,
 )
+from megatron.core.typed_torch import copy_signature
 from megatron.core.utils import (
     get_pg_rank,
     get_pg_size,
@@ -435,7 +438,9 @@ class TENorm:
     Transformer-Engine's `LayerNorm` or `RMSNorm` based on input."""
 
     # TODO should we ditch normalization config and just use spec to choose LayerNorm vs RMSNorm?
-    def __new__(cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5):
+    def __new__(
+        cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5
+    ) -> LayerNormInterface:
         if not HAVE_TE:
             raise ImportError(
                 "Transformer Engine is not installed. "
@@ -464,7 +469,7 @@ def __new__(cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5)
         else:
             raise Exception("Only LayerNorm and RMSNorm are curently supported")
 
-        return instance
+        return cast(LayerNormInterface, instance)
 
 
 class TELinear(te.pytorch.Linear):
@@ -639,6 +644,7 @@ def __init__(
         self.te_quant_params: Optional[TEQuantizationParams] = None
 
         for param in self.parameters():
+            setattr(param, "parallel_mode", parallel_mode)
             if is_expert:
                 # Reduce the gradient on the expert_data_parallel group for expert linear layers
                 setattr(param, "allreduce", not self.expert_parallel)
@@ -649,6 +655,8 @@ def __init__(
                     # Reduce the gradient further on the TP group since the weight is
                     # duplicated across TP ranks
                     setattr(param, "sequence_parallel", self.config.sequence_parallel)
+                    # Mark as NOT tensor parallel since weight is duplicated
+                    setattr(param, "tensor_model_parallel", False)
 
         tp_group = get_tensor_model_parallel_group_if_none(tp_group, is_expert=is_expert)
         self._tp_group = tp_group
@@ -919,10 +927,14 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
             dp_cp_group=metadata["dp_cp_group"],
         )
 
-    def __repr__(self):
+    @override
+    def extra_repr(self) -> str:
+        """Extra context to add to the module's string representation."""
         return (
-            f"{type(self).__name__}(in_features={self.in_features}, "
-            f"out_features={self.out_features}, bias={self.use_bias}, TP={self.tp_size})"
+            f"in_features={self.in_features}, "
+            f"out_features={self.out_features}, "
+            f"bias={self.use_bias}, "
+            f"TP={self.tp_size}"
         )
 
     def backward_dw(self):
@@ -1025,10 +1037,14 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
             dp_cp_group=metadata["dp_cp_group"],
         )
 
-    def __repr__(self):
+    @override
+    def extra_repr(self) -> str:
+        """Extra context to add to the module's string representation."""
         return (
-            f"{type(self).__name__}(in_features={self.in_features}, "
-            f"out_features={self.out_features}, bias={self.use_bias}, TP={self.tp_size})"
+            f"in_features={self.in_features}, "
+            f"out_features={self.out_features}, "
+            f"bias={self.use_bias}, "
+            f"TP={self.tp_size}"
         )
 
     def backward_dw(self):
@@ -1125,10 +1141,14 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
             dp_cp_group=metadata["dp_cp_group"],
         )
 
-    def __repr__(self):
+    @override
+    def extra_repr(self) -> str:
+        """Extra context to add to the module's string representation."""
         return (
-            f"{type(self).__name__}(in_features={self.in_features}, "
-            f"out_features={self.out_features}, bias={self.use_bias}, TP={self.tp_size})"
+            f"in_features={self.in_features}, "
+            f"out_features={self.out_features}, "
+            f"bias={self.use_bias}, "
+            f"TP={self.tp_size}"
         )
 
     def backward_dw(self):
@@ -1455,6 +1475,61 @@ def sharded_state_dict(
 
 
 if HAVE_TE and is_te_min_version("1.9.0.dev0"):
+    def ceil_div(x: int, y: int) -> int:
+        return (x + y - 1) // y
+
+    class _FakeInt4QuantizationSTE(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x, group_size):
+            m, n = x.shape
+            block_size_m, block_size_n = 1, group_size
+
+
+            m_padded = ceil_div(m, block_size_m) * block_size_m
+            n_padded = ceil_div(n, block_size_n) * block_size_n
+
+            x_padded = torch.zeros(
+                (m_padded, n_padded),
+                dtype=x.dtype, device=x.device
+            )
+            x_padded[:m, :n] = x
+
+            x_view = x_padded.view(
+                m_padded // block_size_m,
+                block_size_m,
+                n_padded // block_size_n,
+                block_size_n
+            )
+
+            x_max = x_view.abs().float().amax(dim=(1, 3), keepdim=True)
+            q_max = 7
+            x_scale = x_max / q_max
+
+            x_scale = x_scale.clamp(min=1e-5)
+
+            x_div = x_view / x_scale
+            x_round = torch.round(x_div)
+
+            x_q_clamped = x_round.clamp(-q_max, q_max)
+
+            x_dequant_view = x_q_clamped * x_scale
+
+            x_dequant_full = x_dequant_view.view_as(x_padded)
+            x_out = x_dequant_full[:m, :n].contiguous().to(x.dtype)
+
+            return x_out
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            return grad_output, None
+
+    def fake_int4_quantization_ste(x, group_size):
+        x_out = _FakeInt4QuantizationSTE.apply(x, group_size)
+
+        if hasattr(x, 'main_grad'):
+            x_out.main_grad = x.main_grad
+
+        return x_out
 
     class TEGroupedLinear(te.pytorch.GroupedLinear):
         """
@@ -1671,6 +1746,20 @@ def forward(self, x, m_splits):
                 return out
             return out, None
 
+        def _get_weight_tensors(self):
+            """Get the weight tensors of the module."""
+            weight_tensors = super()._get_weight_tensors()
+
+            if os.getenv("OPEN_TRAINING_INT4_FAKE_QAT_FLAG", "0") == "1":
+                group_size = int(os.getenv("OPEN_TRAINING_INT4_GROUP_SIZE", "128"))
+
+                weight_tensors = [
+                    fake_int4_quantization_ste(w, group_size)
+                    for w in weight_tensors
+                ]
+
+            return weight_tensors
+
         def _encode_extra_state(self, state):
             # TE 2.0 changed the format of extra_state to be a byte tensor
             if is_te_min_version("2.0.0"):
@@ -1907,6 +1996,7 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
     class TEFusedMLP(MLP):
         """MLP wrapper using Transformer Engine's operation-based API."""
 
+        @copy_signature(MLP.__init__)
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
 
@@ -2417,6 +2507,14 @@ def fused_apply_rotary_pos_emb_thd(
     fused_sort_chunks_by_index_with_probs = None
     fused_unpermute = None
 
+try:
+    from transformer_engine.pytorch.permutation import moe_permute_and_pad_with_probs
+
+    fused_permute_and_pad_with_probs = moe_permute_and_pad_with_probs
+
+except ImportError:
+    fused_permute_and_pad_with_probs = None
+
 try:
     from transformer_engine.pytorch.cross_entropy import parallel_cross_entropy
 
diff --git a/megatron/core/extensions/transformer_engine_spec_provider.py b/megatron/core/extensions/transformer_engine_spec_provider.py
index 6f8947078b9..ab87f8760b5 100644
--- a/megatron/core/extensions/transformer_engine_spec_provider.py
+++ b/megatron/core/extensions/transformer_engine_spec_provider.py
@@ -1,7 +1,8 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from __future__ import annotations
 
 import warnings
-from typing import Optional, Tuple
+from typing import Optional, cast
 
 from megatron.core.extensions.transformer_engine import (
     TEActivationOp,
@@ -18,8 +19,14 @@
 from megatron.core.models.backends import BackendSpecProvider
 from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from megatron.core.transformer.dot_product_attention import DotProductAttention
-from megatron.core.transformer.mlp import MLPSubmodules
-from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLP
+from megatron.core.transformer.mlp import MLPSubmodules, TEActivationFunctionBuilder
+from megatron.core.transformer.moe.experts import (
+    GroupedMLP,
+    SequentialMLP,
+    TEGroupedMLP,
+    TEGroupedMLPSubmodules,
+)
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.utils import get_te_version, is_te_min_version
 
 
@@ -50,7 +57,7 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         """Which module for sequential layernorm and linear"""
         return TELayerNormColumnParallelLinear
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> type:
+    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if for_qk and not is_te_min_version("1.9.0"):
             # TENorm significantly harms convergence when used
@@ -67,14 +74,18 @@ def core_attention(self) -> type:
 
     def grouped_mlp_modules(
         self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> Tuple[type, Optional[MLPSubmodules]]:
+    ) -> (
+        tuple[type[TEGroupedMLP], TEGroupedMLPSubmodules]
+        | tuple[type[SequentialMLP], MLPSubmodules]
+        | tuple[type[GroupedMLP], None]
+    ):
         """Which module and submodules to use for grouped mlp"""
         if (
             moe_use_grouped_gemm
             and TEColumnParallelGroupedLinear is not None
             and not moe_use_legacy_grouped_gemm
         ):
-            return TEGroupedMLP, MLPSubmodules(
+            return TEGroupedMLP, TEGroupedMLPSubmodules(
                 linear_fc1=TEColumnParallelGroupedLinear, linear_fc2=TERowParallelGroupedLinear
             )
         elif moe_use_grouped_gemm:
@@ -97,6 +108,8 @@ def grouped_mlp_modules(
                 linear_fc1=TEColumnParallelLinear, linear_fc2=TERowParallelLinear
             )
 
-    def activation_func(self) -> type:
+    def activation_func(self) -> TEActivationFunctionBuilder | None:
         """Which module to use for activation function"""
-        return TEActivationOp
+        # transformer_engine.BasicOperation.forward has an overly permissive return type, but by
+        # design these classes always meet the interface.
+        return cast(TEActivationFunctionBuilder, TEActivationOp)
diff --git a/megatron/core/fusions/fused_bias_dropout.py b/megatron/core/fusions/fused_bias_dropout.py
index 336452562b3..2eb4007f75c 100644
--- a/megatron/core/fusions/fused_bias_dropout.py
+++ b/megatron/core/fusions/fused_bias_dropout.py
@@ -26,12 +26,14 @@ def _bias_dropout_add_func(x_with_bias, residual, prob, training):
         and (bias is None or not bias.requires_grad)
     )
 
-    # If we want to train mixed precision, then the output of this function
-    # should be half precision. However, in AMP O1, the input (residual) is
-    # in fp32, and it will up-cast the result to fp32, causing pipeline parallel
-    # GPU communication to hang. Therefore, we need to cast residual to the same
-    # dtype as x.
-    residual = residual if residual.dtype == x.dtype else residual.to(x.dtype)
+    # For fp32 residual connections: upcast x (and bias) to residual's dtype so that
+    # the addition and output remain in fp32, preserving numerical precision in the
+    # residual stream across layers. When fp32_residual_connection is enabled,
+    # pipeline parallel communication dtype should be set to fp32 accordingly.
+    if x.dtype != residual.dtype:
+        x = x.to(residual.dtype)
+        if bias is not None:
+            bias = bias.to(residual.dtype)
 
     # The Dropout operation, Residual Addition and the tensor returning can be
     # done generically outside the if statement, but that stops fusing of Bias
diff --git a/megatron/core/fusions/fused_mla_yarn_rope_apply.py b/megatron/core/fusions/fused_mla_yarn_rope_apply.py
index 1fd5dcfae37..75e1072d54b 100644
--- a/megatron/core/fusions/fused_mla_yarn_rope_apply.py
+++ b/megatron/core/fusions/fused_mla_yarn_rope_apply.py
@@ -385,6 +385,7 @@ def rotary_fwd_kv_kernel(
     SIN,
     emb_dim: tl.constexpr,
     k_dim: tl.constexpr,
+    k_dim_ceil: tl.constexpr,
     v_dim: tl.constexpr,
     head_num: tl.constexpr,
     batch_size,
@@ -434,21 +435,27 @@ def rotary_fwd_kv_kernel(
     cos_right = tl.load(COS + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2))
     sin_right = tl.load(SIN + token_idx * emb_dim + emb_dim // 2 + tl.arange(0, emb_dim // 2))
 
-    KV_ptr = KV + pid_m * stride_kv_seq + pid_head * BLOCK_H * stride_kv_nheads
-    kv_off = tl.arange(0, BLOCK_H)[:, None] * stride_kv_nheads
-    mask = kv_off < head_num * stride_kv_nheads
-    k_in_off = kv_off + tl.arange(0, k_dim)[None, :]
-    v_in_off = kv_off + k_dim + tl.arange(0, v_dim)[None, :]
-    k = tl.load(KV_ptr + k_in_off, mask=mask)
-    v = tl.load(KV_ptr + v_in_off, mask=mask)
+    KV_ptr = KV + pid_m * stride_kv_seq # + pid_head * BLOCK_H * stride_kv_nheads
+    ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H
+    kj_range = tl.arange(0, k_dim_ceil)[None, :]
+    mask_k = (ki_range < head_num) & (kj_range < k_dim)
+    mask_v = ki_range < head_num
+    k_off = ki_range * stride_kv_nheads + kj_range
+    if v_dim > 0:
+        v_off = ki_range * stride_kv_nheads + k_dim + tl.arange(0, v_dim)[None, :]
+        v = tl.load(KV_ptr + v_off, mask=mask_v)
+    else:
+        v = tl.zeros((BLOCK_H, 1), dtype=KV.dtype.element_ty)
+    k = tl.load(KV_ptr + k_off, mask=mask_k)
 
-    K_ptr = O_KEY + pid_m * stride_k_seq + pid_head * BLOCK_H * stride_k_nheads
-    V_ptr = O_VALUE + pid_m * stride_v_seq + pid_head * BLOCK_H * stride_v_nheads
+    K_ptr = O_KEY + pid_m * stride_k_seq # + pid_head * BLOCK_H * stride_k_nheads
+    V_ptr = O_VALUE + pid_m * stride_v_seq # + pid_head * BLOCK_H * stride_v_nheads
 
-    k_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads + tl.arange(0, k_dim)[None, :]
-    v_out_off = tl.arange(0, BLOCK_H)[:, None] * stride_v_nheads + tl.arange(0, v_dim)[None, :]
-    tl.store(K_ptr + k_out_off, k, mask=mask)
-    tl.store(V_ptr + v_out_off, v, mask=mask)
+    k_out_off = ki_range * stride_k_nheads + kj_range
+    tl.store(K_ptr + k_out_off, k, mask=mask_k)
+    if v_dim > 0:
+        v_out_off = ki_range * stride_v_nheads + tl.arange(0, v_dim)[None, :]
+        tl.store(V_ptr + v_out_off, v, mask=mask_v)
 
     EMB = K_POS_EMB + pid_m * stride_emb_seq
     # x1 = t[..., 0::2], x2 = t[..., 1::2]
@@ -460,14 +467,16 @@ def rotary_fwd_kv_kernel(
     x_left = x_left.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2)
     x_right = x_right.expand_dims(0).broadcast_to(BLOCK_H, emb_dim // 2)
 
+    x_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H
+    mask_x = x_range < head_num
     x_left_off = (
-        tl.arange(0, BLOCK_H)[:, None] * stride_k_nheads
+        x_range * stride_k_nheads
         + k_dim
         + tl.arange(0, emb_dim // 2)[None, :]
     )
     x_right_off = x_left_off + emb_dim // 2
-    tl.store(K_ptr + x_left_off, x_left, mask=mask)
-    tl.store(K_ptr + x_right_off, x_right, mask=mask)
+    tl.store(K_ptr + x_left_off, x_left, mask=mask_x)
+    tl.store(K_ptr + x_right_off, x_right, mask=mask_x)
 
 
 @triton.autotune(
@@ -493,6 +502,7 @@ def rotary_bwd_kv_kernel(
     SIN,
     emb_dim: tl.constexpr,
     k_dim: tl.constexpr,
+    k_dim_ceil: tl.constexpr,
     v_dim: tl.constexpr,
     head_num: tl.constexpr,
     batch_size,
@@ -533,27 +543,32 @@ def rotary_bwd_kv_kernel(
     else:
         token_idx = _get_thd_token_idx(cu_seqlens_kv, pid_m, seq_num, cp_rank, cp_size)
 
-    dKV_ptr = dKV + pid_m * stride_dkv_seq + pid_head * BLOCK_H * stride_dkv_nheads
-    dkv_off = tl.arange(0, BLOCK_H)[:, None] * stride_dkv_nheads
-    mask = dkv_off < head_num * stride_dkv_nheads
-    dk_out_off = dkv_off + tl.arange(0, k_dim)[None, :]
-    dv_out_off = dkv_off + k_dim + tl.arange(0, v_dim)[None, :]
-
-    dK_ptr = dK + pid_m * stride_dk_seq + pid_head * BLOCK_H * stride_dk_nheads
-    dV_ptr = dV + pid_m * stride_dv_seq + pid_head * BLOCK_H * stride_dv_nheads
-    dk_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + tl.arange(0, k_dim)[None, :]
-    dv_in_off = tl.arange(0, BLOCK_H)[:, None] * stride_dv_nheads + tl.arange(0, v_dim)[None, :]
-    dk = tl.load(dK_ptr + dk_in_off, mask=mask)
-    dv = tl.load(dV_ptr + dv_in_off, mask=mask)
-    tl.store(dKV_ptr + dk_out_off, dk, mask=mask)
-    tl.store(dKV_ptr + dv_out_off, dv, mask=mask)
+    dKV_ptr = dKV + pid_m * stride_dkv_seq # + pid_head * BLOCK_H * stride_dkv_nheads
+    ki_range = tl.arange(0, BLOCK_H)[:, None] + pid_head * BLOCK_H
+    kj_range = tl.arange(0, k_dim_ceil)[None, :]
+    mask_k = (ki_range < head_num) & (kj_range < k_dim)
+    mask_v = ki_range < head_num
+    dk_out_off = ki_range * stride_dkv_nheads + kj_range
+
+    dK_ptr = dK + pid_m * stride_dk_seq # + pid_head * BLOCK_H * stride_dk_nheads
+    dV_ptr = dV + pid_m * stride_dv_seq # + pid_head * BLOCK_H * stride_dv_nheads
+    dk_in_off = ki_range * stride_dk_nheads + kj_range
+
+    dk = tl.load(dK_ptr + dk_in_off, mask=mask_k)
+    tl.store(dKV_ptr + dk_out_off, dk, mask=mask_k)
+
+    if v_dim > 0:
+        dv_out_off = ki_range * stride_dkv_nheads + k_dim + tl.arange(0, v_dim)[None, :]
+        dv_in_off = ki_range * stride_dv_nheads + tl.arange(0, v_dim)[None, :]
+        dv = tl.load(dV_ptr + dv_in_off, mask=mask_v)
+        tl.store(dKV_ptr + dv_out_off, dv, mask=mask_v)
 
     if pid_head == 0:
         x_left_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32)
         x_right_accum = tl.zeros((BLOCK_H, emb_dim // 2), dtype=tl.float32)
         for i in tl.static_range(triton.cdiv(head_num, BLOCK_H)):
-            dK_ptr = dK + pid_m * stride_dk_seq + i * BLOCK_H * stride_dk_nheads
-            x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim
+            dK_ptr = dK + pid_m * stride_dk_seq # + i * BLOCK_H * stride_dk_nheads
+            x_off = tl.arange(0, BLOCK_H)[:, None] * stride_dk_nheads + k_dim + i * BLOCK_H * stride_dk_nheads
             mask = x_off < head_num * stride_dk_nheads
             x_left_off = x_off + tl.arange(0, emb_dim // 2)[None, :]
             x_right_off = x_left_off + emb_dim // 2
@@ -632,6 +647,7 @@ def forward(
 
         o_key = kv.new_empty(total_seqlen, nheads, emb_dim + k_dim)
         o_value = kv.new_empty(total_seqlen, nheads, v_dim)
+        k_dim_ceil = triton.next_power_of_2(k_dim)
 
         grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"]))
         rotary_fwd_kv_kernel[grid](
@@ -643,6 +659,7 @@ def forward(
             sin,
             emb_dim,
             k_dim,
+            k_dim_ceil,
             v_dim,
             nheads,
             batch_size,
@@ -700,6 +717,7 @@ def backward(ctx, dk, dv):
 
         d_kv = dk.new_empty(total_seqlen, nheads, ctx.k_dim + ctx.v_dim)
         d_emb = dk.new_empty(total_seqlen, 1, ctx.emb_dim)
+        k_dim_ceil = triton.next_power_of_2(ctx.k_dim)
 
         grid = lambda META: (total_seqlen, triton.cdiv(nheads, META["BLOCK_H"]))
         rotary_bwd_kv_kernel[grid](
@@ -711,6 +729,7 @@ def backward(ctx, dk, dv):
             sin,
             ctx.emb_dim,
             ctx.k_dim,
+            k_dim_ceil,
             ctx.v_dim,
             nheads,
             batch_size,
diff --git a/megatron/core/inference/batch_dimensions_utils.py b/megatron/core/inference/batch_dimensions_utils.py
index e6ea32a6df8..1a202c35af5 100644
--- a/megatron/core/inference/batch_dimensions_utils.py
+++ b/megatron/core/inference/batch_dimensions_utils.py
@@ -25,7 +25,6 @@ class InferenceBatchDimensions:
         token_count : number of total input tokens
         prefill_req_count : number of prefill requests
         decode_req_count : number of decode requests
-        has_explicit_chunked_prefill_req : whether the batch has an explicit chunked prefill request
 
     The batch dimensions are ordered by token_count, then by prefill_req_count,
     then by decode_req_count.
@@ -35,7 +34,6 @@ class InferenceBatchDimensions:
     token_count: int = 0
     prefill_req_count: int = 0
     decode_req_count: int = 0
-    has_explicit_chunked_prefill_req: bool = False
 
     def __str__(self):
         """
@@ -55,9 +53,6 @@ def is_applicable_for_batch_dim(
         for prefill or decode requests. Otherwise, prefill slots
         can only be used for prefill requests.
         """
-        if real_batch_dim.has_explicit_chunked_prefill_req != self.has_explicit_chunked_prefill_req:
-            return False
-
         if real_batch_dim.prefill_req_count == 0:
             return (
                 self.token_count >= real_batch_dim.token_count
@@ -104,10 +99,6 @@ def is_valid(self, max_requests: int, max_sequence_length: int) -> bool:
         if self.token_count > self.prefill_req_count * max_sequence_length + self.decode_req_count:
             return False
 
-        # Check if there is an invalid chunked prefill request.
-        if self.prefill_req_count == 0 and self.has_explicit_chunked_prefill_req:
-            return False
-
         return True
 
     def __hash__(self):
@@ -115,14 +106,7 @@ def __hash__(self):
         Returns a hash of the batch dimension.
         In cuda graph quick matching, the batch dimension is used as a key in a dictionary.
         """
-        return hash(
-            (
-                self.token_count,
-                self.prefill_req_count,
-                self.decode_req_count,
-                self.has_explicit_chunked_prefill_req,
-            )
-        )
+        return hash((self.token_count, self.prefill_req_count, self.decode_req_count))
 
     def __eq__(self, other: "InferenceBatchDimensions") -> bool:
         """
@@ -130,16 +114,10 @@ def __eq__(self, other: "InferenceBatchDimensions") -> bool:
         """
         if other is None:
             return False
-        return (
-            self.token_count,
-            self.prefill_req_count,
-            self.decode_req_count,
-            self.has_explicit_chunked_prefill_req,
-        ) == (
+        return (self.token_count, self.prefill_req_count, self.decode_req_count) == (
             other.token_count,
             other.prefill_req_count,
             other.decode_req_count,
-            other.has_explicit_chunked_prefill_req,
         )
 
     @property
@@ -154,6 +132,7 @@ def adjust_batch_dims_for_expert_parallelism(
         local_batch_dims,
         strict: bool,
         decode_only_cuda_graphs: bool,
+        explicit_chunked_prefill: bool,
         ep_group: Optional[torch.distributed.ProcessGroup] = None,
     ) -> Optional["InferenceBatchDimensions"]:
         """Adjusted cuda graph batch dimensions for expert parallelism.
@@ -163,6 +142,7 @@ def adjust_batch_dims_for_expert_parallelism(
             local_batch_dims: The local batch dimensions to adjust.
             strict: Whether to use strict matching for batch dimensions.
             decode_only_cuda_graphs: Whether CUDA graphs are only used for decode steps.
+            explicit_chunked_prefill: Whether chunked prefill is enabled with explicit requests
             ep_group: Optional expert parallel process group. If None, uses global parallel state.
                       When using different EP sizes for inference vs training, pass the
                       inference EP group explicitly.
@@ -176,13 +156,13 @@ def adjust_batch_dims_for_expert_parallelism(
             return local_batch_dims
         # all reduce local work across expert model parallel group
 
-        has_explicit_chunked_prefill_req = local_batch_dims.has_explicit_chunked_prefill_req
         is_non_decode = local_batch_dims.prefill_req_count > 0
         sync_tensor = torch.tensor(
             [
                 local_batch_dims.token_count,
                 int(is_non_decode),
-                int(has_explicit_chunked_prefill_req),
+                local_batch_dims.prefill_req_count,
+                local_batch_dims.decode_req_count,
             ],
             dtype=torch.int32,
             device=torch.cuda.current_device(),
@@ -192,7 +172,6 @@ def adjust_batch_dims_for_expert_parallelism(
 
         sync_tensor = sync_tensor.cpu()
         is_any_ep_rank_in_non_decode = sync_tensor[1].item() == 1
-        any_ep_rank_has_explicit_chunked_prefill_req = sync_tensor[2].item() == 1
 
         # We force eager mode for scenarios where some ranks will run with CUDA graphs
         # while others will not. Without this check, the all-to-all communication in the
@@ -202,17 +181,23 @@ def adjust_batch_dims_for_expert_parallelism(
         #   1. If we only allow decode CUDA graphs but some ranks are running non-decode batches
         #   2. Some ranks are running explicit chunked prefill requests
         #       (graphs are not recorded for batches with explicit chunked prefill requests)
-        if (
-            decode_only_cuda_graphs and is_any_ep_rank_in_non_decode
-        ) or any_ep_rank_has_explicit_chunked_prefill_req:
+        if is_any_ep_rank_in_non_decode and (decode_only_cuda_graphs or explicit_chunked_prefill):
             return None  # indicate no match, run in eager mode
 
-        assert not has_explicit_chunked_prefill_req
+        # If strict matching is enabled, we sync the request counts across EP ranks
+        # to ensure the graph captures the maximum needed capacity.
+        # TODO(ksanthanam): Add functional test for this scenario
+        adjusted_prefill_req_count = (
+            int(sync_tensor[2].item()) if strict else local_batch_dims.prefill_req_count
+        )
+        adjusted_decode_req_count = (
+            int(sync_tensor[3].item()) if strict else local_batch_dims.decode_req_count
+        )
+
         adjusted_batch_dim = InferenceBatchDimensions(
             token_count=int(sync_tensor[0].item()),
-            prefill_req_count=local_batch_dims.prefill_req_count,
-            decode_req_count=local_batch_dims.decode_req_count,
-            has_explicit_chunked_prefill_req=False,
+            prefill_req_count=adjusted_prefill_req_count,
+            decode_req_count=adjusted_decode_req_count,
         )
         return adjusted_batch_dim
 
@@ -250,6 +235,25 @@ def _calculate_cuda_graph_token_counts(
             (tp_size=2, num_cuda_graphs=4, cuda_graph_max_tokens=1000)
             [1000, 752, 504, 256]
         """
+        if num_cuda_graphs == -1:
+            # automatically determine the number of CUDA graphs to
+            # capture based on the `max_requests` value
+            cuda_graph_token_counts = (
+                [1, 2, 4] + list(range(8, 256, 8)) + list(range(256, cuda_graph_max_tokens + 1, 16))
+            )
+            # Align each entry to TP size
+            cuda_graph_token_counts = list(
+                dict.fromkeys(math.ceil(s / tp_size) * tp_size for s in cuda_graph_token_counts)
+            )
+            # Clamp to max tokens
+            cuda_graph_token_counts = [
+                s for s in cuda_graph_token_counts if s <= cuda_graph_max_tokens
+            ]
+            if not cuda_graph_token_counts or cuda_graph_token_counts[-1] != cuda_graph_max_tokens:
+                cuda_graph_token_counts.append(cuda_graph_max_tokens)
+            cuda_graph_token_counts.reverse()
+            return cuda_graph_token_counts
+
         assert num_cuda_graphs >= 1, f"num_cuda_graphs must be >= 1, got {num_cuda_graphs}"
         assert (
             cuda_graph_max_tokens > 0
@@ -355,7 +359,12 @@ def add_if_valid(token_count: int, prefill_req_count: int, decode_req_count: int
                 or cuda_graph_max_tokens <= 0
             ):
                 cuda_graph_max_tokens = max_tokens
-            num_cuda_graphs = min(max(num_cuda_graphs, 1), cuda_graph_max_tokens)
+
+            if num_cuda_graphs != -1:
+                # if -1, no need to adjust. This will be taken care of in
+                # the _calculate_cuda_graph_token_counts function where we will generate
+                # the token counts based on the max_tokens value and the step size.
+                num_cuda_graphs = min(max(num_cuda_graphs, 1), cuda_graph_max_tokens)
 
             # Calculate token counts for prefill and mixed graphs.
             # These need the full cuda_graph_max_tokens to handle variable-length sequences.
@@ -449,6 +458,7 @@ def match_graph_config(
         cuda_graph_batch_dimensions_list: List[InferenceBatchDimensions],
         strict: bool = False,
         decode_only_cuda_graphs: bool = False,
+        explicit_chunked_prefill: bool = False,
         ep_group: Optional[torch.distributed.ProcessGroup] = None,
     ) -> Optional[InferenceBatchDimensions]:
         """
@@ -462,6 +472,7 @@ def match_graph_config(
             decode_only_cuda_graphs: Used by expert parallel matching. If this is true,
             and one of the EP ranks is running a non-decode step, we elect to run in
             eager mode instead of matching a decode-only cuda graph.
+            explicit_chunked_prefill: Whether chunked prefill is enabled with explicit requests
             ep_group: Optional expert parallel process group. If None, uses global parallel state.
                       When using different EP sizes for inference vs training, pass the
                       inference EP group explicitly.
@@ -477,6 +488,7 @@ def match_graph_config(
             real_batch_dim,
             strict=strict,
             decode_only_cuda_graphs=decode_only_cuda_graphs,
+            explicit_chunked_prefill=explicit_chunked_prefill,
             ep_group=ep_group,
         )
 
@@ -486,6 +498,9 @@ def match_graph_config(
             # in that case, all ranks have to run in eager mode
             return None
 
+        if explicit_chunked_prefill and real_batch_dim.prefill_req_count > 0:
+            return None
+
         # first filter out batch dimensions with smaller token count, prefill req count,
         # or decode req count, as they are not applicable
         graph_batch_dims_applicable = [
diff --git a/megatron/core/inference/config.py b/megatron/core/inference/config.py
new file mode 100644
index 00000000000..54800a3fbff
--- /dev/null
+++ b/megatron/core/inference/config.py
@@ -0,0 +1,217 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Optional, Tuple
+
+import torch
+
+from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.utils import get_attr_wrapped_model
+
+
+@dataclass
+class MambaInferenceStateConfig:
+    """
+    Config for initializing Mamba model inference state tensors.
+
+    Note that we maintain separate metadata for decode, regular prefill, and
+    chunked prefill requests because the Mamba kernels do not yet support mixing
+    these. Once the kernels have been updated we can simplify this code.
+    """
+
+    layer_type_list: List[str]
+    """
+    A list of strings that indicates the layer type (Mamba / Attention / MLP) for each layer.
+    See `megatron/core/ssm/mamba_hybrid_layer_allocation.py` for the list of symbols.
+    """
+
+    mamba_conv_states_shape: Tuple[int]
+    """Mamba conv states shape per request."""
+
+    mamba_ssm_states_shape: Tuple[int]
+    """Mamba ssm states shape per request."""
+
+    @classmethod
+    def from_model(cls, model: MegatronModule) -> Optional["MambaInferenceStateConfig"]:
+        """Returns Mamba inference state config from the model if it is a hybrid model."""
+        from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
+
+        decoder = get_attr_wrapped_model(model, "decoder")
+        layer_type_list = getattr(decoder, "layer_type_list", None)
+        if layer_type_list is not None and Symbols.MAMBA in layer_type_list:
+            (mamba_conv_states_shape, mamba_ssm_states_shape) = (
+                decoder.mamba_state_shapes_per_request()
+            )
+            return cls(
+                layer_type_list=layer_type_list,
+                mamba_conv_states_shape=mamba_conv_states_shape,
+                mamba_ssm_states_shape=mamba_ssm_states_shape,
+            )
+        return None
+
+
+class KVCacheManagementMode(str, Enum):
+    """Mode for handling large tensors (KV cache, Mamba states) during suspend/resume."""
+
+    PERSIST = "persist"
+    """Do not deallocate and reallocate large tensors; keep them on GPU."""
+
+    OFFLOAD = "offload"
+    """Offload large tensors to CPU during deallocation; onload during allocation."""
+
+    RECOMPUTE = "recompute"
+    """Deallocate large tensors and recompute them from scratch during allocation."""
+
+
+@dataclass
+class InferenceConfig:
+    """
+    Config for inference.
+
+    NOTE: Must remain mutually exclusive with the `TransformerConfig`.
+    """
+
+    # =================================
+    # KV cache config
+    # =================================
+    block_size_tokens: int = 256
+    """Size of KV cache block size."""
+
+    buffer_size_gb: int = 20
+    """
+    Buffer size reserved on the GPU for the KV cache.
+    If `unified_memory_level` >= 1, then CPU memory is additionally utilized, resulting in a total
+    buffer size of `buffer_size_gb + paused_buffer_size_gb`.
+    """
+
+    paused_buffer_size_gb: Optional[int] = None
+    """
+    Portion of buffer reserved for paused requests. Active requests are paused when there are not
+    enough active blocks available to continue generating a request. The total buffer size
+    (active + paused) depends on `unified_memory_level` (uvm):
+        - uvm 0: buffer_size_gb (paused buffer is inclusive)
+        - uvm 1: buffer_size_gb + paused_buffer_size_gb
+    """
+
+    mamba_memory_ratio: Optional[float] = None
+    """
+    Percentage of memory buffer to allocate for Mamba states. If not specified, allocates Mamba
+    state tensors for each KV cache block. Only used for hybrid models.
+    """
+
+    max_requests: Optional[int] = None
+    """
+    Max number of active requests to use for decode-only forward passes.
+    This is primarily limited by the combination of `buffer_size_gb` and `max_sequence_length`.
+    """
+
+    max_tokens: Optional[int] = None
+    """
+    Max number of tokens to use for forward passes. This is primarily limited by prefill activation
+    memory usage. (Defaults to 16384).
+    """
+
+    unified_memory_level: int = 0
+    """
+    Sets unified memory usage within the dynamic inference context.
+    The levels are:
+        0) no unified memory (default)
+        1) allocate `memory_buffer` in unified memory.
+    Eventually, additional levels will be included to control other tensors within the context.
+    """
+
+    kv_cache_management_mode: KVCacheManagementMode = KVCacheManagementMode.PERSIST
+    """
+    Mode used to determine how large tensors are handled by the allocate and deallocate methods.
+    See `KVCacheManagementMode` for options.
+    """
+
+    # =================================
+    # CUDA graph config
+    # =================================
+    num_cuda_graphs: Optional[int] = None
+    """
+    Maximum number of cuda graphs to capture, where the cuda graph batch sizes range from 1 to
+    `max_requests`. Due to rounding, the actual number of cuda graphs may not equal this argument.
+    """
+
+    cuda_graph_mixed_prefill_count: Optional[int] = 16
+    """ 
+    The number of mixed prefill graphs to capture if mixed prefill/decode graphs are enabled.
+    """
+
+    use_cuda_graphs_for_non_decode_steps: bool = True
+    """
+    Whether to use CUDA graphs for non-decode steps.
+    """
+
+    static_kv_memory_pointers: bool = False
+    """
+    Whether the KV cache (and Mamba states) will reside at the same memory addresses
+    after suspend/resume as before. When True, CUDA graphs that reference these buffers
+    remain valid across suspend/resume cycles and do not need to be recaptured.
+    Requires either UVM or `torch_memory_saver` when `kv_cache_management_mode` is not PERSIST.
+    """
+
+    # =================================
+    # Model config
+    # =================================
+    max_sequence_length: int = 2560
+    """Max possible sequence length (prompt + output) that will occur."""
+
+    mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None
+    """The Mamba inference state config if the model is a hybrid model."""
+
+    pg_collection: Optional[ProcessGroupCollection] = None
+    """A `ProcessGroupCollection` for distributed execution."""
+
+    use_flashinfer_fused_rope: Optional[bool] = False
+    """
+    If True, use flashinfer's fused rope implementation.
+    If None, defaults to using flash-infer if available.
+    """
+
+    materialize_only_last_token_logits: bool = True
+    """
+    Whether to only materialize logits for the last token. This should be set to False
+    if returning log probs.
+    """
+
+    # =================================
+    # Engine config
+    # =================================
+    enable_chunked_prefill: bool = False
+    """Whether to enable chunked prefill."""
+
+    # =================================
+    # Logging config
+    # =================================
+    track_paused_request_events: bool = False
+    """
+    Whether to track paused request events. If True, `add_event_pause()` is called on
+    requests when they are paused during bookkeeping.
+    """
+
+    track_generated_token_events: bool = False
+    """
+    Whether to track per-token events with timestamps for each generated token.
+    When enabled, each generated token creates a GENERATED_TOKEN event with a
+    timestamp, useful for per-token latency analysis.
+    """
+
+    metrics_writer: Optional["WandbModule"] = None
+    """Wandb module for writing metrics."""
+
+    logging_step_interval: int = 0
+    """
+    The step interval at which to log inference metrics to wandb.
+    Defaults to 0, which means no logging.
+    """
+
+    request_metadata_types: Optional[List[Tuple[str, torch.dtype, bool]]] = None
+    """
+    A list of the per-request metadata types to track. Each entry is a tuple
+    consisting of the string label, the target dtype, and whether to store the data on GPU.
+    """
diff --git a/megatron/core/inference/contexts/attention_context/mamba_metadata.py b/megatron/core/inference/contexts/attention_context/mamba_metadata.py
index 6cf45aeb9e1..d7fcf7436a2 100644
--- a/megatron/core/inference/contexts/attention_context/mamba_metadata.py
+++ b/megatron/core/inference/contexts/attention_context/mamba_metadata.py
@@ -1,36 +1,12 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
-from dataclasses import dataclass
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import torch
 
 from megatron.core.inference.batch_dimensions_utils import InferenceBatchDimensions
 
 
-@dataclass
-class MambaInferenceStateConfig:
-    """
-    Config for initializing Mamba model inference state tensors.
-
-    Note that we maintain separate metadata for decode, regular prefill, and
-    chunked prefill requests because the Mamba kernels do not yet support mixing
-    these. Once the kernels have been updated we can simplify this code.
-    """
-
-    layer_type_list: List[str]
-    """
-    A list of strings that indicates the layer type (Mamba / Attention / MLP) for each layer.
-    See `megatron/core/ssm/mamba_hybrid_layer_allocation.py` for the list of symbols.
-    """
-
-    mamba_conv_states_shape: Tuple[int]
-    """Mamba conv states shape per request."""
-
-    mamba_ssm_states_shape: Tuple[int]
-    """Mamba ssm states shape per request."""
-
-
 class MambaMetadata:
     """Manages the metadata tensors required for Mamba layers during inference."""
 
@@ -81,8 +57,8 @@ def __init__(self, max_requests: int, max_tokens: int):
         )
 
         # Tuple of (
-        #   total prefill sequence length excluding chunked prefill,
-        #   chunked prefill sequence length
+        #   chunked prefill sequence length,
+        #   total regular prefill sequence length
         # )
         self._device_chunked_prefill_buffer = torch.zeros(
             (2,), dtype=torch.int32, device=self.device
@@ -94,6 +70,8 @@ def __init__(self, max_requests: int, max_tokens: int):
         )
         self.mamba_state_free_slot_count = self.max_requests
 
+        self.reset_varlen_metadata()
+
     def reset(self) -> None:
         """
         Resets all Mamba states and frees all allocated slots.
@@ -125,6 +103,7 @@ def update(
         cu_seqlens: torch.Tensor,
         batch_dimensions: InferenceBatchDimensions,
         padded_batch_dimensions: InferenceBatchDimensions,
+        enable_chunked_prefill: bool,
     ) -> None:
         """
         Updates the dedicated CUDA graph mapping tensor with the indices
@@ -133,20 +112,60 @@ def update(
         Args:
             active_mamba_indices (Tensor): Tensor containing the Mamba slot indices
                                            for active requests.
-            num_active_requests (int): The number of active requests.
+            token_to_request_idx (Tensor): Map from token index to request index.
+            cu_seqlens (Tensor): Cumulative sequence lengths.
+            batch_dimensions (InferenceBatchDimensions): Dimensions of the current batch.
+            padded_batch_dimensions (InferenceBatchDimensions): Dimensions of the padded batch.
+                enable_chunked_prefill (bool): Whether chunked prefill is enabled
         """
         real_decode_count = batch_dimensions.decode_req_count
         real_prefill_count = batch_dimensions.prefill_req_count
-        real_token_count = batch_dimensions.token_count
-        has_explicit_chunked_prefill_req = batch_dimensions.has_explicit_chunked_prefill_req
 
         padded_decode_count = padded_batch_dimensions.decode_req_count
         padded_prefill_count = padded_batch_dimensions.prefill_req_count
         padded_token_count = padded_batch_dimensions.token_count
-        assert (
-            has_explicit_chunked_prefill_req
-            == padded_batch_dimensions.has_explicit_chunked_prefill_req
-        )
+
+        has_chunked_prefill_req = enable_chunked_prefill and real_prefill_count > 0
+
+        # Although the context ensures that the last request is always the designated
+        # chunked prefill request, what we actually care about is ensuring that any
+        # prefill request with non-zero initial states is executed through the
+        # chunked prefill path.
+        #
+        # In the batch arrangement passed to this update function, the logic assumes
+        # the *first* prefill request is the one carrying states.
+        #
+        # There are three scenarios:
+        #
+        # Scenario A: No prefill request has initial states yet, but the last request
+        #             is the designated chunked prefill request (starting a new chunk).
+        #
+        #   [ ... Decode Requests ... ] [ Prefill (start) ]
+        #                               ^
+        #                               |--- First prefill request
+        #                                    Treated as having states.
+        #                                    Harmless because actual initial states are 0.
+        #
+        # Scenario B: There is exactly 1 prefill request which is a continuing
+        #             chunked prefill request with non-zero initial states.
+        #
+        #   [ ... Decode Requests ... ] [ Prefill (cont)  ]
+        #                               ^
+        #                               |--- First prefill request
+        #                                    Has non-zero initial states.
+        #
+        # Scenario C: There is a leftover chunked prefill request that is executing
+        #             its last chunk, followed by additional prefill requests.
+        #
+        #   [ ... Decode Requests ... ] [ Prefill (end)   ] [ Prefill (new) ] ...
+        #                               ^
+        #                               |--- First prefill request
+        #                                    Has non-zero initial states.
+        #
+        # The implementation generalizes to Scenario A as well, where the first prefill
+        # request is treated as if it has non-zero initial states, which is safe.
+        # While this results in a minor inefficiency f there is no continuing chunked prefill
+        # request in a given batch, this case is infrequent.
 
         if padded_decode_count > 0:
             # Update decode indices
@@ -159,10 +178,12 @@ def update(
 
         # Determine if we have a chunked prefill request and adjust counts for regular prefill
         regular_prefill_count = real_prefill_count
-        if has_explicit_chunked_prefill_req:
-            # The last prefill request is the chunked one
+        chunked_req_idx = -1
+
+        if has_chunked_prefill_req:
+            # The first prefill request is the chunked one
             regular_prefill_count -= 1
-            chunked_req_idx = real_decode_count + regular_prefill_count
+            chunked_req_idx = real_decode_count
 
             # Update chunked prefill indices
             self._batch_indices_chunked_prefill_buffer[0] = active_mamba_indices[chunked_req_idx]
@@ -173,10 +194,12 @@ def update(
         if padded_prefill_count > 0:
             # Update prefill indices (excluding chunked prefill from regular prefill buffer)
             if regular_prefill_count > 0:
+                # If chunked prefill exists, regular prefills start after it.
+                # If no chunked prefill, regular prefills start at real_decode_count.
+                start_idx = real_decode_count + (1 if has_chunked_prefill_req else 0)
+
                 self._batch_indices_prefill_buffer[:regular_prefill_count].copy_(
-                    active_mamba_indices[
-                        real_decode_count : real_decode_count + regular_prefill_count
-                    ]
+                    active_mamba_indices[start_idx : start_idx + regular_prefill_count]
                 )
 
             if padded_prefill_count > regular_prefill_count:
@@ -184,30 +207,48 @@ def update(
 
             self.batch_indices_prefill = self._batch_indices_prefill_buffer[:padded_prefill_count]
 
-            # Update seq_idx
-            end_regular_prefill_token_idx = cu_seqlens[real_decode_count + regular_prefill_count]
+            # Update seq_idx for regular prefills
+            # If chunked prefill exists, we need to skip its tokens in seq_idx
 
-            # The length of tokens belonging to regular prefill requests (excluding decode tokens)
-            seq_len = end_regular_prefill_token_idx - real_decode_count
+            # Index where regular prefills end in the batch (decode + chunked + regular)
+            end_regular_prefill_req_idx = (
+                real_decode_count + regular_prefill_count + (1 if has_chunked_prefill_req else 0)
+            )
+            end_regular_prefill_token_idx = cu_seqlens[end_regular_prefill_req_idx]
+
+            # Index where regular prefills start
+            start_regular_prefill_req_idx = real_decode_count + (
+                1 if has_chunked_prefill_req else 0
+            )
+            start_regular_prefill_token_idx = cu_seqlens[start_regular_prefill_req_idx]
+
+            # The length of tokens belonging to regular prefill requests
+            seq_len = end_regular_prefill_token_idx - start_regular_prefill_token_idx
 
             if seq_len > 0:
+                # We subtract start_regular_prefill_req_idx to normalize request IDs to
+                # 0-based relative to this buffer
                 self._seq_idx_buffer[:, :seq_len].copy_(
-                    token_to_request_idx[real_decode_count:end_regular_prefill_token_idx]
-                    - real_decode_count
+                    token_to_request_idx[
+                        start_regular_prefill_token_idx:end_regular_prefill_token_idx
+                    ]
+                    - start_regular_prefill_req_idx
                 )
 
             if padded_token_count > seq_len:
                 self._seq_idx_buffer[:, seq_len:padded_token_count] = -1
             self.seq_idx = self._seq_idx_buffer[:, :padded_token_count]
 
-            # Update cu_seqlens
+            # Update cu_seqlens for regular prefill requests
             self._cu_seqlens_buffer[0] = 0
             if regular_prefill_count > 0:
+                # Copy cu_seqlens for regular prefill requests and normalize by
+                # subtracting the start token index
+                start_req_idx = real_decode_count + (1 if has_chunked_prefill_req else 0)
+                end_req_idx = start_req_idx + regular_prefill_count
+
                 self._cu_seqlens_buffer[1 : regular_prefill_count + 1].copy_(
-                    cu_seqlens[
-                        real_decode_count + 1 : real_decode_count + regular_prefill_count + 1
-                    ]
-                    - real_decode_count
+                    cu_seqlens[start_req_idx + 1 : end_req_idx + 1] - cu_seqlens[start_req_idx]
                 )
 
             # Pad the rest with the last value (effectively length 0 segments)
@@ -219,19 +260,32 @@ def update(
 
         if padded_decode_count > 0 and padded_prefill_count > 0:
             self._device_decode_prefill_buffer[0] = real_decode_count
-            self._device_decode_prefill_buffer[1] = regular_prefill_count
+            # This describes the number of items in the prefill tensor relative to the
+            # decode tensor. If chunked prefill is present, it is included in the
+            # "prefill" part of the main split.
+            self._device_decode_prefill_buffer[1] = regular_prefill_count + (
+                1 if has_chunked_prefill_req else 0
+            )
             self.device_decode_prefill = self._device_decode_prefill_buffer
 
-        # If using chunked prefill for this batch, store the number of regular prefill tokens
-        # and the number of tokens in the chunked prefill request
-        if has_explicit_chunked_prefill_req:
+        # If using chunked prefill for this batch, store the number of chunked tokens
+        # and the number of regular prefill tokens
+        if has_chunked_prefill_req:
+            # Chunked request is the first prefill request (index real_decode_count)
             chunked_prefill_token_count = (
-                cu_seqlens[real_decode_count + real_prefill_count]
-                - cu_seqlens[real_decode_count + real_prefill_count - 1]
+                cu_seqlens[real_decode_count + 1] - cu_seqlens[real_decode_count]
             )
-            assert self.cu_seqlens is not None
-            self._device_chunked_prefill_buffer[0] = self.cu_seqlens[regular_prefill_count]
-            self._device_chunked_prefill_buffer[1] = chunked_prefill_token_count
+
+            # Regular prefill tokens are everything after the chunked request tokens
+            regular_prefill_token_count = 0
+            if regular_prefill_count > 0:
+                regular_prefill_token_count = (
+                    cu_seqlens[real_decode_count + 1 + regular_prefill_count]
+                    - cu_seqlens[real_decode_count + 1]
+                )
+
+            self._device_chunked_prefill_buffer[0] = chunked_prefill_token_count
+            self._device_chunked_prefill_buffer[1] = regular_prefill_token_count
             self.device_chunked_prefill = self._device_chunked_prefill_buffer
 
     def allocate_slot(self) -> Optional[int]:
diff --git a/megatron/core/inference/contexts/base_context.py b/megatron/core/inference/contexts/base_context.py
index 3dfec6de3ad..4f03726fe3d 100644
--- a/megatron/core/inference/contexts/base_context.py
+++ b/megatron/core/inference/contexts/base_context.py
@@ -2,6 +2,8 @@
 
 import abc
 
+from megatron.core.inference.config import InferenceConfig
+
 
 class BaseInferenceContext(abc.ABC):
     """Base class for inference contexts.
@@ -10,13 +12,11 @@ class BaseInferenceContext(abc.ABC):
     Extend this class for any future contexts types.
     """
 
-    def __init__(self, materialize_only_last_token_logits: bool):
+    def __init__(self, inference_config: InferenceConfig):
         """
         Args:
-            materialize_only_last_token_logits (bool):
-                If True, only the last-token logits will be extracted during decode
         """
-        self.materialize_only_last_token_logits = materialize_only_last_token_logits
+        self.config = inference_config
 
     @abc.abstractmethod
     def is_static_batching(self) -> bool:
diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py
index 5dc2d503097..4f07d8b364e 100644
--- a/megatron/core/inference/contexts/dynamic_context.py
+++ b/megatron/core/inference/contexts/dynamic_context.py
@@ -4,22 +4,19 @@
 import math
 import warnings
 from contextlib import nullcontext
-from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple
+from typing import List, Optional, Sequence, Tuple
 
-import torch
-import torch.nn.functional as F
-from packaging.version import Version as PkgVersion
-from torch import Tensor
+import torch  # type: ignore
+import torch.nn.functional as F  # type: ignore
+from torch import Tensor  # type: ignore
 
 from megatron.core import parallel_state
 from megatron.core.inference.batch_dimensions_utils import (
     CUDAGraphBatchDimensionBuilder,
     InferenceBatchDimensions,
 )
+from megatron.core.inference.config import InferenceConfig, KVCacheManagementMode
 from megatron.core.inference.inference_request import DynamicInferenceRequest
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.unified_memory import (
     UnifiedMemoryUnsupportedError,
@@ -28,16 +25,17 @@
 from megatron.core.inference.utils import tensor_swap
 from megatron.core.models.common.embeddings.rope_utils import apply_rotary_pos_emb
 from megatron.core.package_info import __version__ as mcore_version
-from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.ssm.mamba_hybrid_layer_allocation import get_layer_maps_from_layer_type_list
-from megatron.core.transformer import TransformerConfig
+from megatron.core.transformer import MLATransformerConfig, TransformerConfig
+from megatron.core.utils import deprecate_args
 from megatron.core.utils import divide as core_divide
-from megatron.core.utils import get_attr_wrapped_model, get_pg_size, internal_api
+from megatron.core.utils import get_pg_size, internal_api
 
-from .attention_context.mamba_metadata import MambaInferenceStateConfig, MambaMetadata
+from .attention_context.mamba_metadata import MambaMetadata
 from .attention_context.mha_metadata import GraphedMHAMetadata, NonGraphedMHAMetadata
 from .base_context import BaseInferenceContext
 from .dynamic_block_allocator import BlockAllocator
+from .routing_metadata import RoutingMetadata
 
 try:
     from .fused_kv_append_kernel import triton_append_key_value_cache
@@ -45,14 +43,7 @@
     triton_append_key_value_cache = None
 
 try:
-    from packaging.version import Version as PkgVersion
-
-    HAVE_PACKAGING = True
-except:
-    HAVE_PACKAGING = False
-
-try:
-    import flashinfer  # pylint: disable=unused-import
+    import flashinfer  # type: ignore # pylint: disable=unused-import
 
     HAVE_FLASHINFER = True
 except ImportError:
@@ -61,21 +52,41 @@
 try:
     from torch_memory_saver import torch_memory_saver
 
-    torch_memory_saver.hook_mode = "torch"
-    HAVE_TORCH_MEMORY_SAVER = True
-except ImportError:
+    # torch_memory_saver.hook_mode = "torch"
     HAVE_TORCH_MEMORY_SAVER = False
-
-try:
-    import wandb  # pylint: disable=unused-import
-
-    HAVE_WANDB = True
 except ImportError:
-    HAVE_WANDB = False
-    wandb = None
+    HAVE_TORCH_MEMORY_SAVER = False
 
-if TYPE_CHECKING:
-    import wandb as WandbModule
+DEPRECATED_ARGS = [
+    "params_dtype",
+    "num_layers",
+    "kv_channels",
+    "num_attention_heads",
+    "max_sequence_length",
+    "buffer_size_gb",
+    "paused_buffer_size_gb",
+    "max_requests",
+    "max_tokens",
+    "block_size_tokens",
+    "tensor_model_parallel_size",
+    "pipeline_model_parallel_size",
+    "pg_collection",
+    "cache_mla_latent",
+    "kv_lora_rank",
+    "qk_pos_emb_head_dim",
+    "num_cuda_graphs",
+    "materialize_only_last_token_logits",
+    "mamba_inference_state_config",
+    "use_cuda_graphs_for_non_decode_steps",
+    "use_flashinfer_fused_rope",
+    "unified_memory_level",
+    "cuda_graph_max_tokens",
+    "cuda_graph_mixed_prefill_count",
+    "metrics_writer",
+    "request_metadata_types",
+    "persist_cuda_graphs",
+    "offload_kv_cache",
+]
 
 
 class ContextOverflowError(Exception):
@@ -213,130 +224,45 @@ class DynamicInferenceContext(BaseInferenceContext):
     given step, any unassigned blocks equate to unused space.
 
     Args:
-        params_dtype (torch.dtype): Dtype used for KV cache.
-        num_layers (int): Number of layers on this pipeline parallel rank.
-        kv_channels (int): Hidden dimension per attention head.
-        num_attention_heads (int): Number of attention heads.
-        max_sequence_length (int): Max possible sequence length (prompt + output)
-            that will occur.
-        buffer_size_gb (float): Buffer size reserved on the GPU for the KV cache.
-            if `unified_memory_level` >= 1, then CPU memory is additionally
-            utilized, resulting in a total buffer size of `buffer_size_gb +
-            paused_buffer_size_gb`.
-        paused_buffer_size_gb (float | None): Portion of buffer reserved for
-            paused requests. Active requests are paused when there are not enough
-            active blocks available to continue generating a request. The total
-            buffer size (active + paused) depends on `unified_memory_level` (uvm):
-            - uvm 0: buffer_size_gb (paused buffer is inclusive)
-            - uvm 1: buffer_size_gb + paused_buffer_size_gb
-        max_requests (int): Max number of active requests to use for
-            decode-only forward passes. This value is primarily limited by the
-            combination of `buffer_size_gb` and `max_sequence_length`.
-        max_tokens (int): Max number of tokens to use for forward passes. This is
-            primarily limited by prefill activation memory usage. (Defaults to
-            16384).
-        block_size_tokens (int): Size of KV cache block size.
-        tensor_model_parallel_size (Optional[int]): Tensor model parallel size.
-        num_cuda_graphs (Optional[int]): Maximum number of cuda graphs to capture,
-            where the cuda graph batch sizes range from 1 to `max_requests`
-            (as computed below). Due to rounding, the actual number of cuda graphs
-            may not equal this argument.
-        materialize_only_last_token_logits (Optional[bool]): Whether to only
-            materialize logits for the last token. This should be set to False
-            if returning log probs.
-        mamba_inference_state_config (Optional[MambaInferenceStateConfig]): The Mamba
-            inference state config if the model is a hybrid model.
-        use_cuda_graphs_for_non_decode_steps (bool): If True, use cuda graphs for non-decode
-            engine steps.
-        unified_memory_level (Optional[int]): Set unified memory usage within the
-            dynamic inference context. The levels are: 0) no unified memory, 1)
-            allocate `memory_buffer` in unified memory. Eventually, additional
-            levels will be included to control other tensors within the context.
-        use_flashinfer_fused_rope (bool): If True, use flashinfer's fused rope implementation.
-            If None, defaults to using flash-infer if available.
-        metrics_writer (Optional['WandbModule']): Wandb module for writing metrics.
-        request_metadata_types (Optional[List[Tuple[str, torch.dtype, bool]]]): A list of the
-            per-request metadata types to track. Each entry is a tuple consisting of the string
-            label, the target dtype, and whether to store the data on GPU.
+        model_config (TransformerConfig): Model config.
+        inference_config (InferenceConfig): Inference config.
     """
 
     DEFAULT_MAX_TOKENS = 16384
     TOKEN_ROUNDER = 64
     REQUEST_ROUNDER = 4
 
-    def __init__(
-        self,
-        *,
-        params_dtype: torch.dtype,
-        num_layers: int,
-        kv_channels: int,
-        num_attention_heads: int,
-        max_sequence_length: int,
-        buffer_size_gb: float,
-        paused_buffer_size_gb: float | None = None,
-        max_requests: int = None,
-        max_tokens: int = DEFAULT_MAX_TOKENS,
-        block_size_tokens: int = 256,
-        tensor_model_parallel_size: Optional[int] = None,
-        pipeline_model_parallel_size: Optional[int] = None,
-        pg_collection: Optional[ProcessGroupCollection] = None,
-        cache_mla_latent: bool = False,
-        kv_lora_rank: Optional[int] = None,
-        qk_pos_emb_head_dim: Optional[int] = None,
-        num_cuda_graphs: Optional[int] = None,
-        materialize_only_last_token_logits: Optional[bool] = True,
-        mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None,
-        use_cuda_graphs_for_non_decode_steps: bool = True,
-        use_flashinfer_fused_rope: bool = False,
-        unified_memory_level: Optional[int] = 0,
-        cuda_graph_max_tokens: Optional[int] = None,
-        cuda_graph_mixed_prefill_count: Optional[int] = 16,
-        metrics_writer: Optional['WandbModule'] = None,
-        request_metadata_types: Optional[List[Tuple[str, torch.dtype, bool]]] = None,
-        persist_cuda_graphs: Optional[bool] = False,
-        offload_kv_cache: Optional[bool] = False,
-    ):
-        super().__init__(materialize_only_last_token_logits=materialize_only_last_token_logits)
-
-        self.cache_mla_latent = cache_mla_latent
+    @deprecate_args(
+        *DEPRECATED_ARGS,
+        message=(
+            "Argument `{name}` has been deprecated. "
+            "Only pass `model_config` and `inference_config`"
+        ),
+    )
+    def __init__(self, model_config: TransformerConfig, inference_config: InferenceConfig):
+        super().__init__(inference_config=inference_config)
+
+        self.cache_mla_latent = (
+            isinstance(model_config, MLATransformerConfig) and model_config.cache_mla_latents
+        )
         if self.cache_mla_latent:
             assert (
-                block_size_tokens == 64
+                inference_config.block_size_tokens == 64
             ), "Flash MLA requires a block size of 64. Set --inference-dynamic-batching-block-size 64 to fix this assert"
 
-        # give deprecated args warning for cuda_graph_max_tokens
-        if cuda_graph_max_tokens is not None:
-            warnings.warn(
-                "`cuda_graph_max_tokens` is deprecated and will be removed in a future release. "
-                "The context now automatically sets the max tokens for cuda graphs based on "
-                "`max_requests`.",
-                DeprecationWarning,
-            )
-
-        self.metrics_writer = metrics_writer
-
         # Per partition num heads and hidden size.
-        projection_size = kv_channels * num_attention_heads
-        if tensor_model_parallel_size is None:
-            tp_size = (
-                get_pg_size(pg_collection.tp)
-                if pg_collection is not None
-                else parallel_state.get_tensor_model_parallel_world_size()
-            )
+        num_attention_heads = model_config.num_query_groups or model_config.num_attention_heads
+        projection_size = model_config.kv_channels * num_attention_heads
+        pg_collection = inference_config.pg_collection
+        if pg_collection is not None:
+            tp_size = get_pg_size(pg_collection.tp)
+            pp_size = get_pg_size(pg_collection.pp)
         else:
-            tp_size = tensor_model_parallel_size
+            tp_size = model_config.tensor_model_parallel_size
+            pp_size = model_config.pipeline_model_parallel_size
         self.hidden_size_per_attention_head = core_divide(projection_size, num_attention_heads)
         self.num_attention_heads_per_partition = core_divide(num_attention_heads, tp_size)
 
-        if pipeline_model_parallel_size is None:
-            pp_size = (
-                get_pg_size(pg_collection.pp)
-                if pg_collection is not None
-                else parallel_state.get_pipeline_model_parallel_world_size()
-            )
-        else:
-            pp_size = pipeline_model_parallel_size
-
         # Cache the PP group we should use for PP collectives inside the context.
         # If the model provides a pg_collection with a pp group, prefer it.
         # Otherwise:
@@ -357,6 +283,7 @@ def __init__(
             self.expert_model_parallel_group = None
 
         # Mamba states.
+        mamba_inference_state_config = inference_config.mamba_inference_state_config
         self.is_hybrid_model = mamba_inference_state_config is not None
         if self.is_hybrid_model:
             mamba_conv_states_shape = mamba_inference_state_config.mamba_conv_states_shape
@@ -381,7 +308,7 @@ def __init__(
             self.layer_map = attention_layer_map | mamba_layer_map
         else:
             # The layer map is the identity function for pure Transformer models.
-            self.num_attention_layers = num_layers
+            self.num_attention_layers = model_config.num_layers // pp_size
             self.num_mamba_layers = 0
             (self.mamba_conv_states_shape, self.mamba_ssm_states_shape) = (None, None)
             self.layer_map = {i: i for i in range(self.num_attention_layers)}
@@ -392,11 +319,11 @@ def __init__(
             )
 
         # Block size tokens, bytes.
-        dtype_size_bytes = params_dtype.itemsize
-        self.block_size_tokens = block_size_tokens
+        dtype_size_bytes = model_config.params_dtype.itemsize
+        self.block_size_tokens = inference_config.block_size_tokens
         if self.cache_mla_latent:
             #   one vector  c_t  (rank)  +  optional RoPE phase slice
-            self.kv_reduced_dim = kv_lora_rank + qk_pos_emb_head_dim
+            self.kv_reduced_dim = model_config.kv_lora_rank + model_config.qk_pos_emb_head_dim
             self.block_size_bytes = (
                 dtype_size_bytes
                 * self.num_attention_layers
@@ -421,10 +348,12 @@ def __init__(
             mamba_states_memory_per_request *= self.num_mamba_layers
             mamba_states_memory_per_request *= dtype_size_bytes
 
-        # Unified memory.
-        self.unified_memory_level = unified_memory_level
-        self.persist_cuda_graphs = persist_cuda_graphs
-        if unified_memory_level > 0:
+        # Unified memory and general tensor management.
+        self.unified_memory_level = inference_config.unified_memory_level
+        self.static_kv_memory_pointers = inference_config.static_kv_memory_pointers
+        self.kv_cache_management_mode = inference_config.kv_cache_management_mode
+
+        if self.unified_memory_level != 0:
             try:
                 self.unified_memory_mempool = create_unified_mempool()
             except UnifiedMemoryUnsupportedError:
@@ -433,34 +362,76 @@ def __init__(
                         "Unified memory requested but not available; defaulting to GPU memory."
                     )
                 self.unified_memory_level = 0
+        # If we are in a mode that requires static KV memory pointers,
+        # we must have either UVM or torch_memory_saver.
+        if (
+            self.static_kv_memory_pointers
+            and self.kv_cache_management_mode != KVCacheManagementMode.PERSIST
+        ):
+            assert HAVE_TORCH_MEMORY_SAVER or self.unified_memory_level != 0, (
+                "Static KV memory pointers require UVM or torch_memory_saver when not persisted. "
+                "Use --rl-kv-cache-management-mode=persist, UVM, or install torch_memory_saver."
+            )
+
+        # When not using `torch_memory_saver`, we manually offload/restore tensors.
+        # We use storage resize, similar to the logic in `core/distributed/param_and_grad_buffer.py`
+        self._offloadable_tensor_names: set[str] = set()
+        self._offloadable_cpu_backups: dict[str, torch.Tensor] = {}
+        self._offloadable_storage_sizes: dict[str, int] = {}
+        self._uses_torch_memory_saver: bool = False
 
         # Initialize block allocator.
-        buffer_size_bytes = int(buffer_size_gb * 1024**3)
+        buffer_size_bytes = int(inference_config.buffer_size_gb * 1024**3)
         paused_buffer_size_bytes = (
-            0 if paused_buffer_size_gb is None else int(paused_buffer_size_gb * 1024**3)
-        )
-        # TODO: Add parameter to control fraction of memory assigned to KV cache
-        # versus Mamba state.
-        block_count = buffer_size_bytes // (self.block_size_bytes + mamba_states_memory_per_request)
-        block_count = max(2, block_count)  # need >= 1 active block + 1 dummy block
-        paused_block_count = paused_buffer_size_bytes // (
-            self.block_size_bytes + mamba_states_memory_per_request
+            0
+            if inference_config.paused_buffer_size_gb is None
+            else int(inference_config.paused_buffer_size_gb * 1024**3)
         )
 
+        mamba_max_requests = float('inf')
+
+        if (mamba_memory_ratio := inference_config.mamba_memory_ratio) is not None:
+            assert self.is_hybrid_model
+            assert mamba_memory_ratio > 0 and mamba_memory_ratio < 1
+
+            # Calculate total memory before partition
+            total_memory = buffer_size_bytes + paused_buffer_size_bytes
+            mamba_memory_bytes = total_memory * mamba_memory_ratio
+            mamba_max_requests = int(mamba_memory_bytes // mamba_states_memory_per_request)
+
+            # Reduce buffer sizes for KV cache
+            buffer_size_bytes = int(buffer_size_bytes * (1.0 - mamba_memory_ratio))
+            paused_buffer_size_bytes = int(paused_buffer_size_bytes * (1.0 - mamba_memory_ratio))
+
+            block_count = buffer_size_bytes // self.block_size_bytes
+            block_count = max(2, block_count)  # need >= 1 active block + 1 dummy block
+            paused_block_count = paused_buffer_size_bytes // self.block_size_bytes
+        else:
+            block_count = buffer_size_bytes // (
+                self.block_size_bytes + mamba_states_memory_per_request
+            )
+            block_count = max(2, block_count)  # need >= 1 active block + 1 dummy block
+            paused_block_count = paused_buffer_size_bytes // (
+                self.block_size_bytes + mamba_states_memory_per_request
+            )
+
         # If using pipeline parallelism synchronize the total block count in case the
         # pipeline stages have different layer allocations. Non-uniform block counts
         # can lead to some ranks pausing requests earlier than other ranks
         # (i.e., divergence in the scheduling behavior).
         if pp_size > 1:
             block_count_tensor = torch.tensor(
-                block_count, dtype=torch.int32, device=torch.cuda.current_device()
+                (block_count, paused_block_count),
+                dtype=torch.int32,
+                device=torch.cuda.current_device(),
             )
             torch.distributed.all_reduce(
                 block_count_tensor,
                 op=torch.distributed.ReduceOp.MIN,
                 group=self.pipeline_parallel_group,
             )
-            block_count = block_count_tensor.item()
+            block_count = block_count_tensor[0].item()
+            paused_block_count = block_count_tensor[1].item()
 
         self.block_allocator = BlockAllocator(
             context=self,
@@ -471,13 +442,14 @@ def __init__(
         )
 
         # Track request metadata.
+        request_metadata_types = inference_config.request_metadata_types
         if request_metadata_types is None:
             request_metadata_types = DynamicInferenceRequest.get_metadata_types()
         self.request_metadata_types = request_metadata_types
 
         # Initialize context state.
-        self.params_dtype = params_dtype
-        self.max_sequence_length = max_sequence_length
+        self.params_dtype = model_config.params_dtype
+        self.max_sequence_length = inference_config.max_sequence_length
 
         # Request and token counts.
         self.total_request_count = 0
@@ -497,16 +469,25 @@ def __init__(
         self.max_kv_block_count = math.ceil(self.max_sequence_length / self.block_size_tokens)
 
         # Set max_requests, max_tokens.
-        if max_requests is None:
+        if inference_config.max_requests is None:
             # Maximize compute utilization by defaulting to 1 block per request.
             self.max_requests = self.block_allocator.total_count - 1  # -1 for dummy block
+
+            # Adjust max_requests for Mamba memory constraints if necessary
+            if self.is_hybrid_model and mamba_max_requests < self.max_requests:
+                self.max_requests = int(mamba_max_requests)
+
             self.max_requests = self.max_requests // tp_size * tp_size
             self.max_requests = self.max_requests // self.REQUEST_ROUNDER * self.REQUEST_ROUNDER
         else:
             # User can control request overflow via max_requests.
-            self.max_requests = max_requests
+            self.max_requests = inference_config.max_requests
 
-        self.max_tokens = max_tokens or self.DEFAULT_MAX_TOKENS
+        assert (
+            self.max_requests % tp_size == 0
+        ), f"max_requests must be divisible by tp_size ({tp_size}), but got {self.max_requests}"
+
+        self.max_tokens = inference_config.max_tokens or self.DEFAULT_MAX_TOKENS
 
         assert self.max_tokens >= self.max_requests, (
             f"max_tokens ({self.max_tokens}) must be >= "
@@ -520,6 +501,7 @@ def __init__(
         self.graph_attn_metadata = {}
         self.non_graph_attn_metadata = {}
         self.active_attn_metadata = None
+        self.is_creating_cuda_graphs = False
 
         self.graph_attn_metadata["mha_metadata"] = GraphedMHAMetadata(
             block_count_total=self.block_allocator.total_count,
@@ -537,43 +519,46 @@ def __init__(
             max_seqlen=self.max_sequence_length,
         )
 
+        self.moe_enable_routing_replay = model_config.moe_enable_routing_replay
+        if self.moe_enable_routing_replay:
+            assert (
+                model_config.num_moe_experts is not None
+            ), "Router recording/replay requested but no MoE experts specified!"
+            self.moe_routing_metadata = RoutingMetadata(self, model_config.moe_router_topk)
+
         # CUDA graph config list
+        self.use_cuda_graphs_for_non_decode_steps = (
+            inference_config.use_cuda_graphs_for_non_decode_steps
+        )
         self.cuda_graph_batch_dimensions_list, self.cuda_graph_token_counts = (
             CUDAGraphBatchDimensionBuilder.generate_cuda_graph_batch_dimensions_list(
                 tp_size=tp_size,
-                num_cuda_graphs=num_cuda_graphs,
+                num_cuda_graphs=inference_config.num_cuda_graphs,
                 cuda_graph_max_tokens=self.max_requests,
-                cuda_graph_mixed_prefill_count=cuda_graph_mixed_prefill_count,
+                cuda_graph_mixed_prefill_count=inference_config.cuda_graph_mixed_prefill_count,
                 max_requests=self.max_requests,
                 max_tokens=self.max_tokens,
                 max_sequence_length=self.max_sequence_length,
-                use_cuda_graphs_for_non_decode_steps=use_cuda_graphs_for_non_decode_steps,
+                use_cuda_graphs_for_non_decode_steps=self.use_cuda_graphs_for_non_decode_steps,
             )
         )
 
-        # Whether to offload the KV cache. Determines where the KV cache is allocated within memory.
-        self.offload_kv_cache = offload_kv_cache
-        assert not (
-            self.offload_kv_cache and self.unified_memory_level
-        ), "The KV cache should not be instantiated in unified memory when it is offloaded during training."
-
         self._using_cuda_graph_this_step = False
-        self.use_cuda_graphs_for_non_decode_steps = use_cuda_graphs_for_non_decode_steps
         # Deal with chunked prefill
+        self.enable_chunked_prefill = inference_config.enable_chunked_prefill
         self.chunked_prefill_request_id = -1
-        self.has_explicit_chunked_prefill_req = False
 
         # FlashInfer.
-        if use_flashinfer_fused_rope is True:
+        if inference_config.use_flashinfer_fused_rope is True:
             assert HAVE_FLASHINFER, "flashinfer is not installed"
-        elif use_flashinfer_fused_rope is None:
-            use_flashinfer_fused_rope = HAVE_FLASHINFER
-        self.use_flashinfer_fused_rope = use_flashinfer_fused_rope
+        elif inference_config.use_flashinfer_fused_rope is None:
+            inference_config.use_flashinfer_fused_rope = HAVE_FLASHINFER
+        self.use_flashinfer_fused_rope = inference_config.use_flashinfer_fused_rope
 
         # Allocate GPU state.
         self.is_tensor_state_allocated = False
         self.is_symmetric_memory_initialized = False
-        self.allocate_all_tensors(is_init=True)
+        self.initialize_all_tensors()
 
         # Print info.
         logging.info(
@@ -584,23 +569,76 @@ def __init__(
             )
         )
 
-    def allocate_all_tensors(self, *, is_init: bool) -> None:
-        """Allocate GPU state.
-
-        This method is used for both 1) initial allocation, and 2) resuming the
-        GPU state after a suspend.
-
-        Args:
-            is_init (bool): True if this is being called from `__init__()`.
-        """
-
-        # Only allocate tensors when not using unified memory at all (level 0),
-        # or for initial allocation during `__init__()`. For levels 1 and 2, we do
-        # not perform any explicit allocations or deallocations after the initial
-        # call to `__init__()`.
-        if self.unified_memory_level != 0 and not is_init:
-            return
+    def _allocate_memory_buffer(self):
+        """Allocate the KV cache memory buffer."""
+        if self.cache_mla_latent:
+            self.memory_buffer = torch.empty(
+                (
+                    self.num_attention_layers,
+                    self.block_allocator.total_count,
+                    self.block_size_tokens,
+                    self.kv_reduced_dim,
+                ),
+                dtype=self.params_dtype,
+                device=torch.cuda.current_device(),
+            )
+        else:
+            self.memory_buffer = torch.empty(
+                (
+                    2,  # key and value
+                    self.num_attention_layers,
+                    self.block_allocator.total_count,
+                    self.block_size_tokens,
+                    self.num_attention_heads_per_partition,
+                    self.hidden_size_per_attention_head,
+                ),
+                dtype=self.params_dtype,
+                device=torch.cuda.current_device(),
+            )
+        if (
+            self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD
+            and not self._uses_torch_memory_saver
+        ):
+            assert self.unified_memory_level == 0
+            self._offloadable_tensor_names.add("memory_buffer")
+            self._offloadable_cpu_backups["memory_buffer"] = torch.empty_like(
+                self.memory_buffer, device="cpu"
+            ).pin_memory()
+
+    def _allocate_mamba_states(self):
+        """Allocate Mamba states for hybrid models."""
+        if self.is_hybrid_model:
+            self.mamba_metadata = MambaMetadata(
+                max_requests=self.max_requests, max_tokens=self.max_tokens
+            )
+            self.mamba_conv_states = torch.empty(
+                (self.num_mamba_layers, self.max_requests) + self.mamba_conv_states_shape,
+                dtype=self.params_dtype,
+                device=torch.cuda.current_device(),
+            )
+            self.mamba_ssm_states = torch.empty(
+                (self.num_mamba_layers, self.max_requests) + self.mamba_ssm_states_shape,
+                dtype=self.params_dtype,
+                device=torch.cuda.current_device(),
+            )
+            if (
+                self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD
+                and not self._uses_torch_memory_saver
+            ):
+                assert self.unified_memory_level == 0
+                self._offloadable_tensor_names.add("mamba_conv_states")
+                self._offloadable_cpu_backups["mamba_conv_states"] = torch.empty_like(
+                    self.mamba_conv_states, device="cpu"
+                ).pin_memory()
+                self._offloadable_tensor_names.add("mamba_ssm_states")
+                self._offloadable_cpu_backups["mamba_ssm_states"] = torch.empty_like(
+                    self.mamba_ssm_states, device="cpu"
+                ).pin_memory()
+        else:
+            self.mamba_metadata = None
 
+    def initialize_all_tensors(self) -> None:
+        """Allocate all GPU state during initial construction."""
         # Mark allocated.
         if self.is_tensor_state_allocated:
             return
@@ -610,7 +648,7 @@ def allocate_all_tensors(self, *, is_init: bool) -> None:
         for key in vars(self).keys():
             value = getattr(self, key)
             assert not isinstance(value, torch.Tensor), (
-                "All tensors should be allocated within `allocate_all_tensors()."
+                "All tensors should be allocated within `initialize_all_tensors()`. "
                 f"Please move tensor '{key}'."
             )
 
@@ -656,114 +694,90 @@ def allocate_all_tensors(self, *, is_init: bool) -> None:
         self.token_to_position_in_request = torch.empty_like(self.token_to_input_ids)
         self.token_to_local_position_within_kv_block = torch.empty_like(self.token_to_input_ids)
 
-        # Memory buffer.
-        def allocate_memory_buffer():
-            """Allocate the memory buffer. This function is called below within
-            `with ctx_manager:`."""
-            if self.cache_mla_latent:
-                self.memory_buffer = torch.empty(
-                    (
-                        self.num_attention_layers,
-                        self.block_allocator.total_count,
-                        self.block_size_tokens,
-                        self.kv_reduced_dim,
-                    ),
-                    dtype=self.params_dtype,
-                    device=torch.cuda.current_device(),
-                )
-            else:
-                ctx = (
-                    torch_memory_saver.region(tag="kv_cache", enable_cpu_backup=True)
-                    if HAVE_TORCH_MEMORY_SAVER and self.offload_kv_cache
-                    else nullcontext()
-                )
-
-                with ctx:
-                    self.memory_buffer = torch.empty(
-                        (
-                            2,  # key and value
-                            self.num_attention_layers,
-                            self.block_allocator.total_count,
-                            self.block_size_tokens,
-                            self.num_attention_heads_per_partition,
-                            self.hidden_size_per_attention_head,
-                        ),
-                        dtype=self.params_dtype,
-                        device=torch.cuda.current_device(),
-                    )
-
-        # Optional state tensors for hybrid models
-        def allocate_mamba_states():
-            """Allocate Mamba states. This function is called below within
-            `with ctx_manager:`."""
-            if self.is_hybrid_model:
-                self.mamba_metadata = MambaMetadata(
-                    max_requests=self.max_requests, max_tokens=self.max_tokens
-                )
-                self.mamba_conv_states = torch.empty(
-                    (self.num_mamba_layers, self.max_requests) + self.mamba_conv_states_shape,
-                    dtype=self.params_dtype,
-                    device=torch.cuda.current_device(),
-                )
-                self.mamba_ssm_states = torch.empty(
-                    (self.num_mamba_layers, self.max_requests) + self.mamba_ssm_states_shape,
-                    dtype=self.params_dtype,
-                    device=torch.cuda.current_device(),
-                )
-
-            else:
-                self.mamba_metadata = None
-
-        # Allocate `ctx_manager`-managed buffers. (For currently unknown reasons,
-        # `ctx_manager` can only be used once.)
-        ctx_manager = (
-            torch.cuda.use_mem_pool(self.unified_memory_mempool)
-            if self.unified_memory_level > 0
-            else nullcontext()
+        # Allocate large non-graphed buffers.
+        need_static_addr = (
+            self.static_kv_memory_pointers
+            and self.kv_cache_management_mode != KVCacheManagementMode.PERSIST
         )
+
+        ctx_manager = nullcontext()
+        if self.unified_memory_level != 0:
+            ctx_manager = torch.cuda.use_mem_pool(self.unified_memory_mempool)
+        elif HAVE_TORCH_MEMORY_SAVER and need_static_addr:
+            ctx_manager = torch_memory_saver.region(
+                tag="inference_context",
+                enable_cpu_backup=(self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD),
+            )
+            self._uses_torch_memory_saver = True
         with ctx_manager:
-            allocate_memory_buffer()
-            allocate_mamba_states()
+            self._allocate_memory_buffer()
+            self._allocate_mamba_states()
 
         # Reset attention and Mamba state.
         self.reset_attention_state()
         self.reset_mamba_state()
 
-    def deallocate_all_tensors(self):
-        """Deallocate GPU state.
+    def reinitialize_inference_state_buffers(self):
+        """Restore large tensors (KV cache, Mamba states) after a suspend.
 
-        This method is used for suspending the dynamic engine.
+        Called by the engine during `resume()`. Initial allocation is in `initialize_all_tensors()`.
         """
+        if self.is_tensor_state_allocated:
+            return
+        self.is_tensor_state_allocated = True
 
-        # Only deallocate tensors when not using unified memory at all (level 0).
-        # For levels 1 and 2, we do not perform any explicit allocations or
-        # deallocations after the initial call to `__init__()`.
-        if self.unified_memory_level != 0:
+        if self.kv_cache_management_mode == KVCacheManagementMode.PERSIST:
             return
 
-        # Mark deallocated.
+        if self.unified_memory_level != 0 or self._uses_torch_memory_saver:
+            if self.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
+                self.reset()
+            if self._uses_torch_memory_saver:
+                torch_memory_saver.resume("inference_context")
+            return
+
+        if self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD:
+            for name, tensor in ((n, getattr(self, n)) for n in self._offloadable_tensor_names):
+                tensor.storage().resize_(self._offloadable_storage_sizes[name])
+                tensor.copy_(self._offloadable_cpu_backups[name], non_blocking=True)
+        elif self.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
+            self.is_tensor_state_allocated = False
+            self.initialize_all_tensors()
+
+    def deallocate_inference_state_buffers(self):
+        """Deallocate large tensors (KV cache, Mamba states) during suspend.
+
+        Called by the engine during `suspend()`. Mirror to `reinitialize_inference_state_buffers()`.
+        """
         if not self.is_tensor_state_allocated:
             return
         self.is_tensor_state_allocated = False
 
-        # Delete all tensor attributes.
-        # TODO(@lmcafee): check that device == 'cuda'?
-        keys = list(vars(self).keys())
-        for key in keys:
-            value = getattr(self, key)
-            if isinstance(value, torch.Tensor):
-                delattr(self, key)
+        if self.kv_cache_management_mode == KVCacheManagementMode.PERSIST:
+            return
+
+        if self.unified_memory_level != 0:
+            return
+
+        if self._uses_torch_memory_saver:
+            torch_memory_saver.pause("inference_context")
+            return
+
+        if self.kv_cache_management_mode == KVCacheManagementMode.OFFLOAD:
+            for name, tensor in ((n, getattr(self, n)) for n in self._offloadable_tensor_names):
+                self._offloadable_storage_sizes[name] = tensor.storage().size()
+                self._offloadable_cpu_backups[name].copy_(tensor, non_blocking=True)
+                tensor.storage().resize_(0)
+        elif self.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
+            # TODO(@lmcafee): check that device == 'cuda'?
+            for key in list(vars(self).keys()):
+                value = getattr(self, key)
+                if isinstance(value, torch.Tensor):
+                    delattr(self, key)
 
     @classmethod
     def round_up_tokens(cls, value, tp_size=None):
-        """Round up to nearest multiple of `TOKEN_ROUNDER` (above) that is also divisible by tensor model parallel size."""
-        if not HAVE_PACKAGING:
-            raise ImportError(
-                "`packaging` is required for this functionality, please install it with `pip install packaging`"
-            )
-        if PkgVersion(mcore_version) < PkgVersion("0.13"):
-            return cls.round_up(value)
-
+        """Round up to nearest multiple of `TOKEN_ROUNDER` that is also divisible by tensor model parallel size."""
         # Make sure divisible by TP size
         if tp_size is None:
             # Check if parallel state is initialized before trying to get TP size
@@ -775,72 +789,9 @@ def round_up_tokens(cls, value, tp_size=None):
 
         return token_rounder * int(math.ceil(int(value) / token_rounder))
 
-    @classmethod
-    def from_config(
-        cls,
-        inference_config: InferenceWrapperConfig,
-        model,
-        max_batch_size: int,
-        buffer_size_gb: float = 40,
-        num_cuda_graphs: int = None,
-        mamba_inference_state_config: Optional[MambaInferenceStateConfig] = None,
-        unified_memory_level: int = 0,
-    ):
-        """
-        Instantiate a `DynamicInferenceContext` from a `TransformerConfig` and an `InferenceWrapperConfig`.
-        """
-        # TODO: Add other necessary configs from inference_config
-
-        # Max sequence length.
-        position_embedding_type = get_attr_wrapped_model(model, "position_embedding_type")
-        model_max_seq_len = get_attr_wrapped_model(model, "max_sequence_length")
-        inf_max_seq_len = inference_config.inference_max_seq_length
-
-        if position_embedding_type == "learned_absolute":
-            # When using absolute position embeddings, it is critical that the
-            # context's `max_sequence_length` is less than or equal to the model's
-            # `max_sequence_length`. Otherwise, the context's `position_ids` will
-            # contain ids greater than the dimension of the position embedding
-            # tensor, which will result in an index error.
-            if inf_max_seq_len:
-                max_sequence_length = min(model_max_seq_len, inf_max_seq_len)
-            else:
-                max_sequence_length = model_max_seq_len
-            assert max_batch_size <= model_max_seq_len
-        else:
-            max_sequence_length = (
-                inference_config.inference_max_seq_length or model_config.max_sequence_length
-            )
-        max_sequence_length = max(max_sequence_length, max_batch_size)
-
-        # Context.
-        model_config = model.config
-        return cls(
-            params_dtype=inference_config.params_dtype,
-            num_layers=model_config.num_layers // model_config.pipeline_model_parallel_size,
-            kv_channels=model_config.kv_channels,
-            num_attention_heads=model_config.num_query_groups,
-            tensor_model_parallel_size=model_config.tensor_model_parallel_size,
-            pipeline_model_parallel_size=model_config.pipeline_model_parallel_size,
-            max_sequence_length=max_sequence_length,
-            buffer_size_gb=buffer_size_gb,
-            materialize_only_last_token_logits=False,
-            num_cuda_graphs=num_cuda_graphs,
-            use_flashinfer_fused_rope=None,
-            mamba_inference_state_config=mamba_inference_state_config,
-            unified_memory_level=unified_memory_level,
-        )
-
     @classmethod
     def round_up_requests(cls, value, tp_size=None):
-        """Round up to nearest multiple of `REQUEST_ROUNDER` (above) that is also divisible by tensor model parallel size."""
-        if not HAVE_PACKAGING:
-            raise ImportError(
-                "`packaging` is required for this functionality, please install it with `pip install packaging`"
-            )
-        if PkgVersion(mcore_version) < PkgVersion("0.13"):
-            return cls.round_up(value)
-
+        """Round up to nearest multiple of `REQUEST_ROUNDER` that is also divisible by tensor model parallel size."""
         # Make sure divisible by TP size
         if tp_size is None:
             # Check if parallel state is initialized before trying to get TP size
@@ -852,16 +803,6 @@ def round_up_requests(cls, value, tp_size=None):
 
         return request_rounder * int(math.ceil(int(value) / request_rounder))
 
-    @classmethod
-    def round_up(cls, value):
-        """Deprecated in favor of round_up_tokens and round_up_requests."""
-        warnings.warn(
-            "`round_up` is deprecated in favor of `round_up_tokens` or `round_up_requests` "
-            "and will be removed in `megatron-core` 0.14."
-        )
-        ROUNDER = getattr(cls, "ROUNDER", 64)
-        return ROUNDER * int(math.ceil(int(value) / ROUNDER))
-
     def is_static_batching(self) -> bool:
         """Is static batching? False."""
         return False
@@ -882,6 +823,7 @@ def has_unfinished_requests(self) -> bool:
 
     def cu_query_lengths(self) -> Tuple[Tensor, int]:
         """Cumulative query sequence lengths."""
+        assert self.active_attn_metadata is not None
         return (
             self.active_attn_metadata["mha_metadata"].state_data["cu_query_seq_lengths"],
             self.active_attn_metadata["mha_metadata"].state_data["max_seqlen_q"],
@@ -889,6 +831,7 @@ def cu_query_lengths(self) -> Tuple[Tensor, int]:
 
     def cu_kv_lengths(self) -> Tuple[Tensor, Tensor, int]:
         """Cumulative key/value sequence lengths."""
+        assert self.active_attn_metadata is not None
         return (
             self.active_attn_metadata["mha_metadata"].state_data["cu_kv_seq_lengths"],
             self.active_attn_metadata["mha_metadata"].state_data["kv_seq_lengths"],
@@ -958,18 +901,20 @@ def append_key_value_cache(self, layer_number: int, key: Tensor, value: Tensor)
                 : self.padded_active_token_count
             ]
 
-    def key_value_cache(self, layer_number: int) -> Tuple[Tensor, Tensor]:
+    def key_value_cache(self, layer_number: int) -> Tuple[Tensor, Optional[Tensor], Tensor]:
         """Read from KV cache.
 
         Args:
             layer_number (int): Layer number.
 
         Return:
-            (Tuple[Tensor, Tensor]) The key and value pointer tensors that point
-            to blocks within the block-level memory buffer.
+            (Tuple[Tensor, Tensor, Tensor]) The key and value pointer tensors that point
+            to blocks within the block-level memory buffer as well as the block table.
         """
         attention_layer_number = self.layer_map[layer_number - 1]
 
+        assert self.active_attn_metadata is not None
+
         if self.cache_mla_latent:
             return (
                 self.memory_buffer[attention_layer_number],
@@ -1325,15 +1270,16 @@ def initialize_attention_state(
         Return:
             None.
         """
-        # if in recording mode, add dummy requests for cuda graph capture
-        if construct_graph_dimensions is not None:
+        self.is_creating_cuda_graphs = construct_graph_dimensions is not None
+
+        # If in CUDA graph creation mode, add dummy requests for CUDA graph capture
+        if self.is_creating_cuda_graphs:
             self.add_dummy_requests_for_cudagraph_capture(construct_graph_dimensions)
 
         batch_dimensions = InferenceBatchDimensions(
             token_count=self.active_token_count,
             prefill_req_count=self.num_prefill_requests,
             decode_req_count=self.num_decode_requests,
-            has_explicit_chunked_prefill_req=self.has_explicit_chunked_prefill_req,
         )
         self.batch_dimensions = batch_dimensions
         best_graph = CUDAGraphBatchDimensionBuilder.match_graph_config(
@@ -1341,6 +1287,7 @@ def initialize_attention_state(
             self.cuda_graph_batch_dimensions_list,
             strict=self.is_hybrid_model,
             decode_only_cuda_graphs=(not self.use_cuda_graphs_for_non_decode_steps),
+            explicit_chunked_prefill=self.is_chunked_prefill_enabled() and self.is_hybrid_model,
             ep_group=self.expert_model_parallel_group,
         )
         self._using_cuda_graph_this_step = best_graph is not None
@@ -1368,7 +1315,6 @@ def initialize_attention_state(
                 token_count=padded_token_count,
                 prefill_req_count=padded_prefill_req_count,
                 decode_req_count=padded_decode_req_count,
-                has_explicit_chunked_prefill_req=self.has_explicit_chunked_prefill_req,
             )
         self.padded_active_token_count = self.padded_batch_dimensions.token_count
         self.padded_active_request_count = self.padded_batch_dimensions.req_count
@@ -1386,9 +1332,9 @@ def initialize_attention_state(
         ] = 0
 
         self.active_attn_metadata = (
-            self.graph_attn_metadata
+            self.graph_attn_metadata  # type: ignore[assignment]
             if self.using_cuda_graph_this_step()
-            else self.non_graph_attn_metadata
+            else self.non_graph_attn_metadata  # type: ignore[assignment]
         )
 
         # Update cu_query_seq_lengths, max_seqlen_q.
@@ -1399,8 +1345,6 @@ def initialize_attention_state(
 
         attn_dimensions = batch_dimensions
         if self.using_cuda_graph_this_step():
-            assert not self.has_explicit_chunked_prefill_req
-
             # Treat some decode requests as prefill requests to fit the cuda graph batch dimension.
             if batch_dimensions.decode_req_count > self.padded_batch_dimensions.decode_req_count:
                 total_req = batch_dimensions.req_count
@@ -1410,9 +1354,9 @@ def initialize_attention_state(
                     token_count=batch_dimensions.token_count,
                     prefill_req_count=adjusted_prefill_req_count,
                     decode_req_count=adjusted_decode_req_count,
-                    has_explicit_chunked_prefill_req=False,
                 )
 
+        assert self.active_attn_metadata is not None
         self.active_attn_metadata["mha_metadata"].update(
             request_query_lengths=query_lengths_view,
             request_kv_length_offsets=request_kv_length_offsets_view,
@@ -1433,8 +1377,15 @@ def initialize_attention_state(
                 cu_seqlens,
                 batch_dimensions=attn_dimensions,
                 padded_batch_dimensions=self.padded_batch_dimensions,
+                enable_chunked_prefill=self.is_chunked_prefill_enabled(),
             )
 
+        if self.moe_enable_routing_replay:
+            if self.using_cuda_graph_this_step():
+                self.moe_routing_metadata.enable_static_buffer_recording()
+            else:
+                self.moe_routing_metadata.disable_static_buffer_recording()
+
     def reset(self) -> None:
         """Reset entire context.
 
@@ -1489,9 +1440,9 @@ def reset(self) -> None:
 
         # Reset chunked prefill state
         self.chunked_prefill_request_id = -1
-        self.has_explicit_chunked_prefill_req = False
         self.num_prefill_requests = 0
         self._using_cuda_graph_this_step = False
+        self.is_creating_cuda_graphs = False
         self.padded_batch_dimensions = InferenceBatchDimensions(
             token_count=0, prefill_req_count=0, decode_req_count=0
         )
@@ -1545,13 +1496,16 @@ def last_token_logits(self, logits: Tensor) -> Tensor:
 
         return last_token_logits
 
-    def check_availability(self, req: DynamicInferenceRequest) -> (bool, bool, bool):
+    def check_availability(self, req: DynamicInferenceRequest) -> Tuple[bool, bool, bool]:
         """
         Check if the request can be added to the context.
         """
+        # Note that for hybrid models checking the total request count is sufficient
+        # because we allocate a single set of Mamba state tensors for each request
         request_can_be_added = (
             self.total_request_count < self.max_requests and self.paused_request_count == 0
         )
+
         request_tokens_can_be_added = (
             self.active_token_count + req.remaining_prompt_length <= self.max_tokens
         )
@@ -1757,6 +1711,12 @@ def get_index_of_chunked_prefill_request(self) -> int:
         """
         return torch.where(self.request_ids == self.chunked_prefill_request_id)[0][0]
 
+    def is_chunked_prefill_enabled(self) -> bool:
+        """Returns whether chunked prefill is enabled."""
+        if self.is_hybrid_model:
+            return self.enable_chunked_prefill and not self.is_creating_cuda_graphs
+        return self.enable_chunked_prefill
+
     def release_memory_blocks_from_request_indexes(self, request_indexes) -> None:
         """Release memory blocks used by the given request idxs.
 
@@ -1784,7 +1744,7 @@ def resume_paused_requests(
         active_request_count: int,
         newly_paused_request_ids: torch.Tensor,
         next_tokens: torch.Tensor,
-    ) -> tuple[int, int, torch.Tensor]:
+    ) -> tuple[int, torch.Tensor]:
         """Resume as many paused requests as we have space for in the active buffer.
 
         Args:
@@ -1863,7 +1823,7 @@ def resume_paused_requests(
 
     def evict_overflow_paused_requests(
         self, active_request_count: int, next_tokens: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> Optional[tuple[torch.Tensor, torch.Tensor]]:
         """Evict requests that overflow the paused buffer.
 
         Args:
@@ -2017,7 +1977,6 @@ def update_requests(self, active_requests_mask: Tensor, new_tokens: Tensor) -> T
             active_requests_mask[-1] = (
                 1  # must keep this, next iteration will add a new chunk to it
             )
-        self.has_explicit_chunked_prefill_req = False
 
         active_request_count = (active_requests_mask == 1).sum().item()
         finished_request_count = (active_requests_mask == 0).sum().item()
diff --git a/megatron/core/inference/contexts/routing_metadata.py b/megatron/core/inference/contexts/routing_metadata.py
new file mode 100644
index 00000000000..6664681a7ce
--- /dev/null
+++ b/megatron/core/inference/contexts/routing_metadata.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+if TYPE_CHECKING:
+    from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
+
+from megatron.core.transformer.moe.router_replay import RouterReplay
+
+
+class RoutingMetadata:
+    """Manages routing indices metadata for MoE layers during inference.
+
+    This class provides static buffers for CUDA graph compatibility when
+    recording routing decisions. It holds a reference to the inference context
+    to automatically determine whether to use static buffers based on CUDA graph state.
+
+    Args:
+        context (DynamicInferenceContext): The inference context.
+        moe_router_topk (int): Number of experts selected per token.
+    """
+
+    def __init__(self, context: 'DynamicInferenceContext', moe_router_topk: int):
+        self.context = context
+        self.max_tokens = context.max_tokens
+        self.moe_router_topk = moe_router_topk
+        self.device = torch.cuda.current_device()
+
+        # Static buffer allocated lazily in _ensure_buffer_allocated().
+        # We defer allocation because RouterReplay instances don't exist yet at init time.
+        self.routing_indices_buffer: Optional[torch.Tensor] = None
+        self.num_moe_layers: Optional[int] = None
+
+    def _ensure_buffer_allocated(self) -> None:
+        """Allocate the static buffer if not already allocated.
+
+        Gets the actual number of MoE layers from RouterReplay instances.
+        """
+        if self.routing_indices_buffer is not None:
+            return
+
+        self.num_moe_layers = len(RouterReplay.global_router_replay_instances)
+
+        if self.num_moe_layers == 0:
+            return
+
+        # Static buffer for CUDA graph compatibility.
+        # Shape: [max_tokens, num_moe_layers, moe_router_topk]
+        self.routing_indices_buffer = torch.empty(
+            (self.max_tokens, self.num_moe_layers, self.moe_router_topk),
+            dtype=torch.int32,
+            device=self.device,
+        )
+
+    def get_routing_indices(self) -> Optional[torch.Tensor]:
+        """Get the recorded routing indices.
+
+        Automatically uses the static buffer when CUDA graphs are active,
+        otherwise retrieves from RouterReplay utility.
+
+        Returns:
+            Tensor of shape [num_tokens, num_moe_layers, topk] or None if not available.
+        """
+        if self.context.using_cuda_graph_this_step():
+            # Return view of static buffer up to current token count.
+            if self.routing_indices_buffer is None:
+                return None
+            # Only return up to active token count, to skip entries
+            # for padding tokens.
+            return self.routing_indices_buffer[: self.context.active_token_count]
+        else:
+            # Get from RouterReplay and stack into [num_tokens, num_layers, topk].
+            recorded_data = RouterReplay.get_recorded_data()
+            if recorded_data is None or len(recorded_data) == 0:
+                return None
+            if recorded_data[0] is None:
+                return None
+            # Stack: list of [num_tokens, topk] -> [num_tokens, num_layers, topk]
+            return torch.stack(recorded_data, dim=1)
+
+    def enable_static_buffer_recording(self) -> None:
+        """Enable recording into the static buffer for CUDA graph compatibility.
+
+        This sets up RouterReplay instances to copy routing indices into our
+        pre-allocated static buffer instead of creating new tensors.
+        Allocates the buffer lazily on first call.
+        """
+        self._ensure_buffer_allocated()
+        if self.routing_indices_buffer is not None:
+            RouterReplay.set_global_static_buffers(self.routing_indices_buffer)
+
+    def disable_static_buffer_recording(self) -> None:
+        """Disable static buffer recording, reverting to normal tensor assignment."""
+        RouterReplay.clear_global_static_buffers()
diff --git a/megatron/core/inference/contexts/static_context.py b/megatron/core/inference/contexts/static_context.py
index 8c83d2f09b3..a15b33c414a 100644
--- a/megatron/core/inference/contexts/static_context.py
+++ b/megatron/core/inference/contexts/static_context.py
@@ -1,8 +1,6 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
+from megatron.core.inference.config import InferenceConfig
 
 from .base_context import BaseInferenceContext
 
@@ -19,7 +17,8 @@ class StaticInferenceContext(BaseInferenceContext):
     def __init__(
         self, max_batch_size: int, max_sequence_length: int, use_flashinfer_fused_rope: bool = None
     ):
-        super().__init__(materialize_only_last_token_logits=True)
+        config = InferenceConfig(materialize_only_last_token_logits=True)
+        super().__init__(inference_config=config)
         self.max_sequence_length = max_sequence_length
         self.max_batch_size = max_batch_size
         self.sequence_len_offset = 0
@@ -27,13 +26,6 @@ def __init__(
         self.key_value_memory_dict = {}
         self.decode_mode = False
 
-    @classmethod
-    def from_config(cls, config: InferenceWrapperConfig) -> "StaticInferenceContext":
-        """Initialize context from a config."""
-        max_batch_size = config.inference_max_requests
-        max_sequence_length = config.inference_max_seq_length
-        return cls(max_batch_size, max_sequence_length)
-
     def swap_key_value_dict(self, batch_idx):
         "swap between batches"
         if len(self.key_value_memory_dict) == 0:
diff --git a/megatron/core/inference/data_parallel_inference_coordinator.py b/megatron/core/inference/data_parallel_inference_coordinator.py
index 9a1a11a8c2b..d5cf1605d90 100644
--- a/megatron/core/inference/data_parallel_inference_coordinator.py
+++ b/megatron/core/inference/data_parallel_inference_coordinator.py
@@ -74,6 +74,7 @@ def __init__(
         data_parallel_size: int,
         tokenizer,
         inference_coordinator_port: int | None = None,
+        deterministic_mode: bool = False,
     ):
         """
         Initializes the inference coordinator.
@@ -145,6 +146,12 @@ def __init__(
             assert identity not in self.identities_of_data_parallel_ranks
             self.identities_of_data_parallel_ranks.append(identity)
         logging.info("Inference Coordinator: Connected with data parallel ranks...")
+
+        # In deterministic mode, sort identities for consistent scheduling order.
+        if deterministic_mode:
+            self.identities_of_data_parallel_ranks = deque(
+                sorted(self.identities_of_data_parallel_ranks)
+            )
         self.data_parallel_rank_iterator = cycle(self.identities_of_data_parallel_ranks)
         self.data_parallel_pause_acks = set()
         self.data_parallel_stop_acks = set()
@@ -238,6 +245,7 @@ def start(self):
                 Headers.UNPAUSE,
                 Headers.SUSPEND,
                 Headers.RESUME,
+                Headers.INCREMENT_STALENESS,
                 Headers.STOP,
             ]:
                 # control signals for the engine
@@ -343,6 +351,7 @@ def entrypoint(
         data_parallel_size: int,
         tokenizer,
         inference_coordinator_port: int | None = None,
+        deterministic_mode: bool = False,
     ):
         """
         Class method to instantiate and run the coordinator, for use in a separate process.
@@ -356,9 +365,14 @@ def entrypoint(
                 once the coordinator is ready to accept connections.
             inference_coordinator_port (int): The port to bind to.
             data_parallel_size (int): The number of expected TP-coordinators.
+            deterministic_mode (bool): Whether to enable deterministic scheduling.
         """
         coordinator = cls(
-            pipe_connection, data_parallel_size, tokenizer, inference_coordinator_port
+            pipe_connection,
+            data_parallel_size,
+            tokenizer,
+            inference_coordinator_port,
+            deterministic_mode=deterministic_mode,
         )
         ready_event.set()
         try:
diff --git a/megatron/core/inference/engines/dynamic_engine.py b/megatron/core/inference/engines/dynamic_engine.py
index 0a95e8f4a53..a10b7e3d60f 100644
--- a/megatron/core/inference/engines/dynamic_engine.py
+++ b/megatron/core/inference/engines/dynamic_engine.py
@@ -19,6 +19,7 @@
 from torch import Tensor
 from torch.cuda.nvtx import range_pop, range_push
 
+from megatron.core.inference.config import KVCacheManagementMode
 from megatron.core.inference.contexts.dynamic_context import (
     DynamicInferenceContext,
     MaxSequenceLengthOverflowError,
@@ -30,6 +31,8 @@
 from megatron.core.inference.engines.abstract_engine import AbstractEngine
 from megatron.core.inference.headers import Headers, UnknownHeaderError
 from megatron.core.inference.inference_request import (
+    DynamicInferenceEvent,
+    DynamicInferenceEventType,
     DynamicInferenceRequest,
     DynamicInferenceRequestRecord,
     Status,
@@ -41,7 +44,10 @@
 from megatron.core.inference.utils import Counter, await_process_call
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer.cuda_graphs import delete_cuda_graphs
+from megatron.core.transformer.enums import CudaGraphScope
+from megatron.core.transformer.moe.router_replay import RouterReplay, RouterReplayAction
 from megatron.core.utils import (
+    deprecate_args,
     experimental_api,
     get_asyncio_loop,
     get_pg_rank,
@@ -89,10 +95,14 @@
 except ImportError:
     HAVE_PSUTIL = False
 
-from megatron.core.inference.contexts.dynamic_context import HAVE_TORCH_MEMORY_SAVER
-
-if HAVE_TORCH_MEMORY_SAVER:
-    from torch_memory_saver import torch_memory_saver
+DEPRECATED_ARGS = [
+    "enable_cuda_graph",
+    "random_seed",
+    "track_paused_request_events",
+    "enable_chunked_prefill",
+    "inference_logging_step_interval",
+    "pg_collection",
+]
 
 
 class EngineSuspendedError(Exception):
@@ -136,24 +146,13 @@ class DynamicInferenceEngine(AbstractEngine):
             outputs and detokenizer the output tokens.
         inference_context (DynamicInferenceContext): Context for managing in-flight
             batching and a dynamic block-level KV cache (similar to paged attention).
-        random_seed (Optional[int]): Use a random seed if you want deterministic
-            results. Defaults to None.
-        inference_logging_step_interval (int): The step interval at which to log
-        inference metrics to wandb. Defaults to 0, which means no logging.
     """
 
-    def __init__(
-        self,
-        controller: TextGenerationController,
-        context: DynamicInferenceContext,
-        enable_cuda_graph: Optional[bool] = None,
-        random_seed: Optional[int] = None,
-        *,
-        track_paused_request_events: bool = False,
-        enable_chunked_prefill: bool = True,
-        inference_logging_step_interval: int = 0,
-        pg_collection: Optional[ProcessGroupCollection] = None,
-    ):
+    @deprecate_args(
+        *DEPRECATED_ARGS,
+        message="Argument `{name}` has been deprecated. Only pass `controller` and `context`",
+    )
+    def __init__(self, controller: TextGenerationController, context: DynamicInferenceContext):
 
         assert isinstance(
             controller, TextGenerationController
@@ -161,41 +160,29 @@ def __init__(
         assert isinstance(
             context, DynamicInferenceContext
         ), f"context must be a DynamicInferenceContext, got {type(context)}"
-        assert isinstance(random_seed, int), f"random_seed must be an int, got {type(random_seed)}"
 
-        # Deprecate `enable_cuda_graph`.
-        if enable_cuda_graph is not None:
-            warnings.warn(
-                "The `enable_cuda_graph` argument is deprecated and will be "
-                "removed in `megatron-core 0.15`. `enable_cuda_graph` is now "
-                "read directly from the transformer config object."
-            )
-            self.enable_cuda_graph = enable_cuda_graph
-        else:
-            self.enable_cuda_graph = (
-                controller.inference_wrapped_model.model.config.enable_cuda_graph
-            )
+        model_config = controller.inference_wrapped_model.model.config
+        inference_config = context.config
 
-        if pg_collection is not None:
-            self.pg_collection = pg_collection
+        if inference_config.pg_collection is not None:
+            self.pg_collection = inference_config.pg_collection
         else:
             self.pg_collection = ProcessGroupCollection.use_mpu_process_groups()
 
         # Initialization options.
         self.controller = controller
         self.context = context
-        self.random_seed = random_seed
-        self.track_paused_request_events = track_paused_request_events
-        self.enable_chunked_prefill = enable_chunked_prefill
-        self.inference_logging_step_interval = inference_logging_step_interval
-        self.unified_memory_level = context.unified_memory_level
-        self.persist_cuda_graphs = context.persist_cuda_graphs
-
-        if enable_cuda_graph is not None:
-            self.cuda_graph_impl = "local" if enable_cuda_graph else "none"
-        else:
-            self.cuda_graph_impl = controller.inference_wrapped_model.model.config.cuda_graph_impl
-
+        self.track_paused_request_events = inference_config.track_paused_request_events
+        self.track_generated_token_events = inference_config.track_generated_token_events
+        self.enable_chunked_prefill = inference_config.enable_chunked_prefill
+        self.metrics_writer = inference_config.metrics_writer
+        self.logging_step_interval = inference_config.logging_step_interval
+        self.unified_memory_level = inference_config.unified_memory_level
+        self.materialize_only_last_token_logits = (
+            inference_config.materialize_only_last_token_logits
+        )
+        self.cuda_graph_impl = model_config.cuda_graph_impl
+        self.cuda_graph_scope = model_config.cuda_graph_scope
         # Initialize engine.
         self.reset()
 
@@ -205,12 +192,12 @@ def __init__(
         )
 
         # Configure wandb to use separate step counter for inference metrics (only once)
-        if self.inference_logging_step_interval > 0 and self.context.metrics_writer is not None:
+        if self.logging_step_interval > 0 and self.metrics_writer is not None:
             logging.info(
                 f"\033[1;93m[INFERENCE]\033[0m "
                 f"\033[1;95mLogging inference metrics to wandb (rank {self.rank})\033[0m"
             )
-            if HAVE_WANDB and self.context.metrics_writer.__name__ == "wandb":
+            if HAVE_WANDB and self.metrics_writer.__name__ == "wandb":
                 # Make all inference/* metrics use inference_step as their x-axis
                 # This allows inference and training to have independent step counters
                 context.metrics_writer.define_metric(
@@ -285,11 +272,18 @@ def create_cuda_graphs(self, reset_context: bool = True):
         if self.cuda_graph_impl != "local":
             return
 
+        if (
+            CudaGraphScope.full_iteration in self.cuda_graph_scope
+            and CudaGraphScope.full_iteration_inference not in self.cuda_graph_scope
+        ):
+            warnings.warn(
+                "\n\n*** WARNING: 'full_iteration' CUDA graph scope used during inference! "
+                "This will not create inference CUDA graphs. Use '--cuda-graph-scope=full_iteration_inference' instead. ***\n"
+            )
+
         context = self.context
         controller = self.controller
 
-        config = controller.inference_wrapped_model.inference_wrapper_config
-
         time_start = time.time()
         mem_stats_start = torch.cuda.memory_stats()
 
@@ -313,6 +307,12 @@ def create_cuda_graphs(self, reset_context: bool = True):
                     f"{tbar_idx}/{len(context.cuda_graph_batch_dimensions_list)}. {tbar_str}"
                 )
 
+            # Enable routing recording during warmup if routing replay is enabled.
+            # This ensures the record_indices copy operation is captured in the CUDA graph.
+            model_config = controller.inference_wrapped_model.model.config
+            if model_config.moe_enable_routing_replay:
+                RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)
+
             # Forward pass -> logits.
             controller._dynamic_step_forward_logits(input_ids, position_ids)
 
@@ -342,9 +342,6 @@ def create_cuda_graphs(self, reset_context: bool = True):
 
         self.capture_stats = capture_stats
 
-        if HAVE_TORCH_MEMORY_SAVER:
-            torch_memory_saver.pause("kv_cache")
-
     @internal_api
     async def start_listening_to_data_parallel_coordinator(
         self,
@@ -424,6 +421,7 @@ async def start_listening_to_data_parallel_coordinator(
         # Spawn a DP coordinator process and get the connection info.
         if launch_inference_coordinator and self.is_dp_coordinator:
             spawn_context = multiprocessing.get_context('spawn')
+            deterministic_mode = torch.are_deterministic_algorithms_enabled()
             dp_pipe, dp_process_pipe = spawn_context.Pipe()
             coordinator_ready_event = spawn_context.Event()
             self.inference_coordinator_process = spawn_context.Process(
@@ -434,6 +432,7 @@ async def start_listening_to_data_parallel_coordinator(
                     get_pg_size(self.pg_collection.dp),
                     self.controller.tokenizer,
                     inference_coordinator_port,
+                    deterministic_mode,
                 ),
             )
             self.inference_coordinator_process.start()
@@ -529,7 +528,7 @@ async def start_listening_to_data_parallel_coordinator(
             logging.info("Inference co-ordinator is ready to receive requests!")
             logging.info(f"Data parallel coordinator can be found at {dp_addr}")
 
-        # Finally run the engine infinite loop
+        # Finally run the engine infinite loop.
         loop = get_asyncio_loop(loop)
         self.engine_loop_task = loop.create_task(self.run_engine_with_coordinator(loop=loop))
 
@@ -555,12 +554,14 @@ def suspend_resume_ctx(key: str, *, unified_memory_level: int) -> None:
 
             start_mem = torch.cuda.memory_stats()
             start_time = time.time()
+            range_push(f"{key}-inference-context")
             torch.cuda.synchronize()
 
             yield
 
         finally:
 
+            range_pop()
             end_time = time.time()
 
             end_mem = torch.cuda.memory_stats()
@@ -609,22 +610,28 @@ def suspend(self):
         with self.__class__.suspend_resume_ctx(
             "suspended", unified_memory_level=self.unified_memory_level
         ):
-            self.context.deallocate_all_tensors()
+            self.context.deallocate_inference_state_buffers()
 
-        # Delete cuda graphs when not using unified memory at all (level 0) and
-        # `--rl-training-cuda-graphs` is not passed. For UVM levels 1 and 2, the context's tensors
-        # maintain static memory addresses, so the cuda graphs are re-used.
-        if self.unified_memory_level == 0 and not self.persist_cuda_graphs:
+        if (
+            self.context.kv_cache_management_mode != KVCacheManagementMode.PERSIST
+            and not self.context.static_kv_memory_pointers
+        ):
             delete_cuda_graphs()
 
-        # Maintain references to requests before reset.
+        # Build the list of requests to re-add on resume.
+        # All waiting requests are always included; active requests are included
+        # only if they are marked for recompute (their KV cache will be gone).
         waiting_request_ids = list(self.waiting_request_ids)
         active_request_ids = set(self.requests.keys()) - set(waiting_request_ids)
-        self.resume_request_ids = [*active_request_ids, *waiting_request_ids]
+        if self.context.kv_cache_management_mode == KVCacheManagementMode.RECOMPUTE:
+            recompute_active_ids = active_request_ids
+        else:
+            recompute_active_ids = set()
+        self.resume_request_ids = [*recompute_active_ids, *waiting_request_ids]
         self.waiting_request_ids.clear()
 
-        # Suspend requests objects.
-        for request_id in active_request_ids:
+        # Checkpoint active requests that are marked for recompute.
+        for request_id in recompute_active_ids:
             self.requests[request_id].record.checkpoint()
 
     def resume(self):
@@ -644,23 +651,19 @@ def resume(self):
             # Allocate context tensors.
             alloc_time = time.time()
             torch.cuda.synchronize()
-            self.context.allocate_all_tensors(is_init=False)
+            self.context.reinitialize_inference_state_buffers()
             torch.cuda.synchronize()
             alloc_time = time.time() - alloc_time
 
-            # Reset context and request data.
-            self.context.reset()
-
-            # Create cuda graphs (before adding requests, to be in decode mode).
-            # Only create cuda graphs when not using unified memory at all (level
-            # 0). For levels 1 and 2, the context's tensors maintain static
-            # memory addresses, so the cuda graphs are re-used.
             capture_time = time.time()
-            if self.unified_memory_level == 0 and not self.persist_cuda_graphs:
+            if (
+                self.context.kv_cache_management_mode != KVCacheManagementMode.PERSIST
+                and not self.context.static_kv_memory_pointers
+            ):
                 self.create_cuda_graphs()
             capture_time = time.time() - capture_time
 
-            # Add requests.
+            # Re-add requests saved during suspend.
             add_time = time.time()
             torch.cuda.synchronize()
             for request_id in self.resume_request_ids:
@@ -717,6 +720,7 @@ def _add_request(
                 record=DynamicInferenceRequestRecord.from_request(request),
                 future=self._loop.create_future(),
             )
+            request.add_event_add_engine()  # Record when request enters engine
 
         if request.status is None:
             request.status = Status.ACTIVE_AND_GENERATING_TOKENS
@@ -733,7 +737,7 @@ def _add_request(
             request.sampling_params.return_log_probs
             and not request.sampling_params.skip_prompt_log_probs
         ):
-            assert not self.context.materialize_only_last_token_logits, (
+            assert not self.materialize_only_last_token_logits, (
                 "Prompt log probs cannot be calculated if only last token logits are materialized. "
                 "Set materialize_only_last_token_logits to False in DynamicInferenceContext "
                 "or skip_prompt_log_probs to True in SamplingParams."
@@ -743,6 +747,7 @@ def _add_request(
             request.sampling_params.num_tokens_to_generate = (
                 request.sampling_params.num_tokens_total - len(request.prompt_tokens)
             )
+            request.sampling_params.num_tokens_total = None
         if request.sampling_params.num_tokens_to_generate is None:
             request.sampling_params.num_tokens_to_generate = self.context.max_sequence_length - len(
                 request.prompt_tokens
@@ -782,6 +787,10 @@ def _add_request(
             self.waiting_request_ids.append(request_id)
         else:
             self.failed_request_ids.append(request_id)
+            if self.rank == 0:
+                warnings.warn(
+                    f"Request {request_id} failed to be added to the engine due to errors."
+                )
 
         return self.requests[request_id].future
 
@@ -847,6 +856,7 @@ def post_process_requests(
         sample: torch.Tensor,
         log_probs: torch.Tensor,
         top_n_logprobs: Optional[Dict[int, List[Tuple[torch.Tensor, torch.Tensor]]]] = None,
+        routing_indices_per_request: Optional[Dict[int, torch.Tensor]] = None,
     ) -> Tuple[List[DynamicInferenceRequest], List[DynamicInferenceRequest]]:
         """
         Handles post-processing for requests after a step.
@@ -860,6 +870,9 @@ def post_process_requests(
             log_probs: (List): Log probs for each request
             top_n_logprobs: (Dict): Top-n log probs for each request. Maps request_idx to
                 list of (top_n_logprobs, top_n_indices) tuples.
+            routing_indices_per_request: (Dict[int, Tensor]): MoE routing indices
+                pre-mapped by request_id. Each value is a tensor of shape
+                [num_tokens_this_step, num_layers, topk].
 
         Returns:
             A list of active requests and completed requests as `DynamicInferenceRequest` objects
@@ -881,7 +894,21 @@ def post_process_requests(
                 # Skip appending token for requests being finished due to stop words
                 # (they already have their final token from the previous step)
                 if request_id not in self.stop_word_being_finished_ids:
+                    is_first_token = len(request.generated_tokens) == 0
                     request.generated_tokens.append(token)
+                    if self.track_generated_token_events:
+                        event_generated_token = request.add_event_generated_token(token)
+                    if is_first_token:
+                        if self.track_generated_token_events:
+                            first_token_event = event_generated_token
+                        else:
+                            first_token_event = DynamicInferenceEvent(
+                                type=DynamicInferenceEventType.GENERATED_TOKEN,
+                                payload={"token_id": token},
+                            )
+                        request.ttft = (
+                            first_token_event.timestamp - request.event_add_engine.timestamp
+                        )
                     if request.tpot is None:
                         request.tpot = []
                     request.tpot.append(step_time)
@@ -893,6 +920,7 @@ def post_process_requests(
                     # Request finished by normal means (termination_id, max_length, or stop word from previous step)
                     request.generated_length = len(request.generated_tokens)
                     request.status = Status.COMPLETED
+                    request.add_event_finish()
                     finished_entry = self.requests.pop(request_id)
                     finished_request = finished_entry.record[-1]
                     finished_request.generated_length = len(finished_request.generated_tokens)
@@ -922,7 +950,7 @@ def post_process_requests(
                 # For chunked prefill with materialize_only_last_token_logits, discard intermediate log probs
                 if (
                     request_id == self.context.chunked_prefill_request_id
-                    and self.context.materialize_only_last_token_logits
+                    and self.materialize_only_last_token_logits
                 ):
                     request.prompt_log_probs = []
                     request.generated_log_probs = []
@@ -989,6 +1017,23 @@ def post_process_requests(
                     else:
                         request.generated_top_n_logprobs.append(logit_dict)
 
+            # Process routing indices if available (keyed by request_id)
+            # Each step's routing is a tensor of shape [num_tokens_this_step, num_layers, topk]
+            # We concatenate along dim=0 to accumulate: [total_tokens, num_layers, topk]
+            if (
+                routing_indices_per_request is not None
+                and request_id in routing_indices_per_request
+            ):
+                step_routing = routing_indices_per_request[
+                    request_id
+                ]  # [num_tokens, num_layers, topk]
+                if request.routing_indices is None:
+                    request.routing_indices = step_routing.clone()
+                else:
+                    request.routing_indices = torch.cat(
+                        [request.routing_indices, step_routing], dim=0
+                    )
+
         # Handle evicted requests.
         if evict_request_ids is not None and evict_request_ids.numel() > 0:
 
@@ -1084,7 +1129,7 @@ def schedule_non_chunked_prefill(self):
                     self._loop.create_task, self._notify_cond_for_new_request()
                 )
                 req.remaining_prompt_tokens = req.remaining_prompt_tokens.new_empty(0)
-                req.add_event_add()
+                req.add_event_add_context()
                 self.waiting_request_ids.popleft()
             else:
                 break
@@ -1124,34 +1169,32 @@ def schedule_chunked_prefill(self):
 
             if request_can_be_added and kv_cache_available:
                 if token_fully_can_be_added:
-                    # For Mamba models we need to ensure that the last prefill chunk
-                    # is still tagged as a chunked prefill request.
-                    self.context.has_explicit_chunked_prefill_req = (
-                        self.context.is_hybrid_model
-                        and self.context.chunked_prefill_request_id == req.request_id
-                    )
                     self.context.chunked_prefill_request_id = -1
                     self.context.add_request(req)
                     self._loop.call_soon_threadsafe(
                         self._loop.create_task, self._notify_cond_for_new_request()
                     )
                     req.remaining_prompt_tokens = req.remaining_prompt_tokens.new_empty(0)
-                    req.add_event_add()
+                    req.add_event_add_context()
                     # Fully scheduled, so we remove from waiting pool
                     self.waiting_request_ids.popleft()
                     # Only this case we keep checking the rest of the waiting queue
-                    # We break early for Mamba models running a final prefill chunk
-                    # so that no additional requests are scheduled beyond the chunked
-                    # prefill request.
-                    can_schedule = not self.context.has_explicit_chunked_prefill_req
+                    can_schedule = True
                 elif token_partially_can_be_added:
                     chunk_length = self.context.max_tokens - self.context.active_token_count
+
+                    # If this chunk would leave exactly 1 token for the final chunk, reduce this
+                    # chunk by 1 so the final chunk has 2 tokens. This avoids the edge case where
+                    # max_seqlen_q=1 which results in a bug with the Flash Attention kernel
+                    # See https://github.com/Dao-AILab/flash-attention/issues/1537
+                    if remaining_len - chunk_length == 1 and chunk_length > 1:
+                        chunk_length -= 1
+
                     self.context.add_request(req, chunk_length=chunk_length)
                     self._loop.call_soon_threadsafe(
                         self._loop.create_task, self._notify_cond_for_new_request()
                     )
                     self.context.chunked_prefill_request_id = req.request_id
-                    self.context.has_explicit_chunked_prefill_req = self.context.is_hybrid_model
                     req.remaining_prompt_tokens = req.remaining_prompt_tokens[chunk_length:]
                     req.finished_chunk_token_count += chunk_length
                     # Still have tokens to prefill, so we break and keep the
@@ -1202,10 +1245,10 @@ async def async_forward(self) -> Tuple[Dict, Dict, float, int]:
         range_pop()
 
         if (
-            self.inference_logging_step_interval > 0
+            self.logging_step_interval > 0
             and self.step_count > 0
-            and self.step_count % self.inference_logging_step_interval == 0
-            and self.context.metrics_writer is not None
+            and self.step_count % self.logging_step_interval == 0
+            and self.metrics_writer is not None
         ):
             kvcache_util_stats = self.context.get_kvcache_utilization_stats()
         else:
@@ -1258,6 +1301,7 @@ async def async_bookkeep(
             sample = step_result["sample"]
             log_probs = step_result["log_probs"]
             top_n_logprobs = step_result.get("top_n_logprobs", None)
+            routing_indices_per_request = step_result.get("routing_indices_per_request", None)
             cuda_graph_request_count = step_result["cuda_graph_request_count"]
 
             # Add paused events.
@@ -1265,9 +1309,7 @@ async def async_bookkeep(
                 newly_paused_request_ids = newly_paused_request_ids.tolist()
                 [self.get_request(i).add_event_pause() for i in newly_paused_request_ids]
 
-            # Mark requests finished.
-            [self.get_request(i).add_event_finish() for i in finished_request_ids.tolist()]
-            # Add finished events.
+            # Process finished requests (adds FINISH events and returns records).
             (active_request_ids, finished_request_records) = self.post_process_requests(
                 active_request_ids,
                 finished_request_ids,
@@ -1276,6 +1318,7 @@ async def async_bookkeep(
                 sample,
                 log_probs,
                 top_n_logprobs,
+                routing_indices_per_request,
             )
 
         else:
@@ -1338,18 +1381,13 @@ async def async_bookkeep(
                 else:
                     metrics[f'inference/{key}'] = value
 
-            if HAVE_WANDB and self.context.metrics_writer.__name__ == "wandb":
-                self.context.metrics_writer.log(metrics, commit=True)
+            if HAVE_WANDB and self.metrics_writer.__name__ == "wandb":
+                self.metrics_writer.log(metrics, commit=True)
             else:
-                raise ValueError(
-                    f"Unsupported metrics writer type: {type(self.context.metrics_writer)}"
-                )
+                raise ValueError(f"Unsupported metrics writer type: {type(self.metrics_writer)}")
 
         # Print context state.
-        if (
-            self.inference_logging_step_interval > 0
-            and step_count % self.inference_logging_step_interval == 0
-        ):
+        if self.logging_step_interval > 0 and step_count % self.logging_step_interval == 0:
             mem = torch.cuda.memory_stats()
             step_type = "decode" if context_state["is_decode_only"] else "non-decode"
             output_str = (
@@ -1589,6 +1627,10 @@ def schedule_requests(self) -> int:
                 self.suspend_signal = True
             elif header == Headers.RESUME:
                 self.suspend_signal = False
+            elif header == Headers.INCREMENT_STALENESS:
+                waiting = set(self.waiting_request_ids)
+                for request_id, entry in self.requests.items():
+                    entry.record.increment_staleness(policy_only=request_id in waiting)
             elif header == Headers.STOP:
                 self.received_stop = True
             else:
@@ -1704,7 +1746,11 @@ async def run_engine_with_coordinator(
                 if ep_group_has_work and local_pending_requests == 0:
                     # run dummy forward pass if EP group as a whole has work,
                     # but this rank does not have any work.
+                    self.step_start_event.record()
                     self.controller.dummy_forward()
+                    self.step_end_event.record()
+                    self.step_end_event.synchronize()
+                    self.step_count += 1
                     continue
 
                 # 3. No work in EP group
@@ -1738,7 +1784,11 @@ async def run_engine_with_coordinator(
                     await asyncio.sleep(0.02)  # Yield to event loop
                     continue
 
-                await self.async_step()
+                try:
+                    await self.async_step()
+                except EngineSuspendedError:
+                    await asyncio.sleep(0.02)
+                    continue
 
         except asyncio.CancelledError:
             pass
diff --git a/megatron/core/inference/engines/static_engine.py b/megatron/core/inference/engines/static_engine.py
index d4c61965d2b..5ae37d5967e 100644
--- a/megatron/core/inference/engines/static_engine.py
+++ b/megatron/core/inference/engines/static_engine.py
@@ -8,7 +8,8 @@
 import torch
 
 from megatron.core.inference.async_stream import AsyncStream
-from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
+from megatron.core.inference.config import InferenceConfig, MambaInferenceStateConfig
+from megatron.core.inference.contexts import DynamicInferenceContext, StaticInferenceContext
 from megatron.core.inference.engines.abstract_engine import AbstractEngine
 from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine
 from megatron.core.inference.inference_request import InferenceRequest
@@ -17,7 +18,7 @@
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
 )
-from megatron.core.utils import get_asyncio_loop, get_mamba_inference_state_config_from_model
+from megatron.core.utils import get_asyncio_loop
 
 try:
     from tqdm import tqdm
@@ -42,8 +43,6 @@ class StaticInferenceEngine(AbstractEngine):
             controller that will be used to define how to preprocess prompts, generate
             outputs and detokenizer the output tokens.
         max_batch_size (int, optional): The maximum number of requests to process at once.
-            Will be set from the InferenceWrapperConfig in `text_generation_controller` by
-            default.
         random_seed (int, optional): Use a random seed if you want deterministic
             results. Defaults to None.
     """
@@ -69,53 +68,55 @@ def __init__(
                 DeprecationWarning,
             )
 
-        inference_wrapper_config = (
-            text_generation_controller.inference_wrapped_model.inference_wrapper_config
-        )
         self.controller = text_generation_controller
+        self.inference_wrapped_model = self.controller.inference_wrapped_model
+        self.config = self.inference_wrapped_model.config
         self.random_seed = random_seed or 1234
 
-        inference_max_batch_size = inference_wrapper_config.inference_max_requests
+        # Store original context in case we need to fall back to legacy static engine
+        original_context = self.inference_wrapped_model.inference_context
+        assert original_context is not None
+        assert isinstance(original_context, StaticInferenceContext)
+
         if max_batch_size is None:
-            max_batch_size = inference_max_batch_size
-        elif max_batch_size > inference_max_batch_size:
+            max_batch_size = original_context.max_batch_size
+        elif max_batch_size > original_context.max_batch_size:
             warnings.warn(
                 f"Engine `max_batch_size` ({max_batch_size}) > "
-                f"`inference_max_requests` in `inference_wrapper_config` "
-                f"({inference_max_batch_size}); setting `max_batch_size` to "
-                f"{inference_max_batch_size}",
+                f"`context.max_batch_size` in `inference_wrapped_model.inference_context` "
+                f"({original_context.max_batch_size}); setting `max_batch_size` to "
+                f"{original_context.max_batch_size}",
                 UserWarning,
             )
-            max_batch_size = inference_max_batch_size
+            max_batch_size = original_context.max_batch_size
 
         self.scheduler = Scheduler(max_batch_size=max_batch_size)
 
-        # Store original context in case we need to fall back to legacy static engine
-        original_context = text_generation_controller.inference_wrapped_model.inference_context
-
-        mamba_inference_state_config = get_mamba_inference_state_config_from_model(
-            text_generation_controller.inference_wrapped_model.model
+        mamba_inference_state_config = MambaInferenceStateConfig.from_model(
+            self.inference_wrapped_model.model
         )
 
         try:
             if not legacy:
-                dynamic_context = DynamicInferenceContext.from_config(
-                    inference_config=inference_wrapper_config,
-                    model=text_generation_controller.inference_wrapped_model.model,
-                    max_batch_size=max_batch_size,
-                    buffer_size_gb=buffer_size_gb,
-                    num_cuda_graphs=1,
-                    mamba_inference_state_config=mamba_inference_state_config,
+                dynamic_context = DynamicInferenceContext(
+                    model_config=self.config,
+                    inference_config=InferenceConfig(
+                        max_sequence_length=original_context.max_sequence_length,
+                        buffer_size_gb=buffer_size_gb,
+                        mamba_inference_state_config=mamba_inference_state_config,
+                        max_requests=max_batch_size,
+                        num_cuda_graphs=1,
+                        block_size_tokens=256,
+                        unified_memory_level=0,
+                    ),
                 )
+
                 self.controller.inference_wrapped_model.inference_context = dynamic_context
                 self.controller.inference_wrapped_model.prep_model_for_inference()
                 self.controller._init_dynamic_sampling_tensors()
 
                 self.dynamic_engine = DynamicInferenceEngine(
-                    controller=self.controller,
-                    random_seed=self.random_seed,
-                    context=dynamic_context,
-                    enable_cuda_graph=True,
+                    controller=self.controller, context=dynamic_context
                 )
         except Exception as e:
             # Get exception details for better debugging
@@ -229,13 +230,20 @@ def generate_using_dynamic_engine(
         if prompts:
             if add_BOS:
                 sampling_params.add_BOS = True
-            return self.dynamic_engine.generate(prompts=prompts, sampling_params=sampling_params)
+            request_records = self.dynamic_engine.generate(
+                prompts=prompts, sampling_params=sampling_params
+            )
         elif inference_requests:
             prompts = [request.prompt for request in inference_requests]
             sampling_params = inference_requests[0].sampling_params
             if add_BOS:
                 sampling_params.add_BOS = True
-            return self.dynamic_engine.generate(prompts=prompts, sampling_params=sampling_params)
+            request_records = self.dynamic_engine.generate(
+                prompts=prompts, sampling_params=sampling_params
+            )
+
+        # Return the underlying `InferenceRequest` objects from the `DynamicInferenceRequestRecord`s.
+        return [record.merge() for record in request_records]
 
     def generate_using_legacy_static_engine(
         self,
diff --git a/megatron/core/inference/headers.py b/megatron/core/inference/headers.py
index a22d1328679..2551bc54f53 100644
--- a/megatron/core/inference/headers.py
+++ b/megatron/core/inference/headers.py
@@ -17,6 +17,7 @@ class Headers(Enum):
     UNPAUSE = auto()
     SUSPEND = auto()
     RESUME = auto()
+    INCREMENT_STALENESS = auto()
     STOP = auto()
     STOP_ACK = auto()
 
diff --git a/megatron/core/inference/inference_client.py b/megatron/core/inference/inference_client.py
index a927a393b8c..5a4ed24bebb 100644
--- a/megatron/core/inference/inference_client.py
+++ b/megatron/core/inference/inference_client.py
@@ -213,6 +213,11 @@ def unpause_engines(self) -> None:
         self.running.set()
         self._send_signal_to_engines(Headers.UNPAUSE)
 
+    def increment_staleness(self):
+        """Sends a signal to increment staleness on all in-flight requests."""
+        assert self.paused.is_set(), "Can only increment staleness while engines are paused."
+        self._send_signal_to_engines(Headers.INCREMENT_STALENESS)
+
     def suspend_engines(self):
         """Sends a signal to pause all inference engines."""
         self._send_signal_to_engines(Headers.PAUSE)
@@ -220,6 +225,7 @@ def suspend_engines(self):
 
     def resume_engines(self):
         """Sends a signal to unpause all inference engines."""
+        self.paused.clear()
         self._send_signal_to_engines(Headers.RESUME)
         self._send_signal_to_engines(Headers.UNPAUSE)
 
diff --git a/megatron/core/inference/inference_request.py b/megatron/core/inference/inference_request.py
index 6a7354220f9..328a77aeed0 100644
--- a/megatron/core/inference/inference_request.py
+++ b/megatron/core/inference/inference_request.py
@@ -159,7 +159,9 @@ def _post_deserialize(self, obj: dict):
 class DynamicInferenceEventType(Enum):
     """Dynamic inference event type."""
 
-    ADD = auto()
+    ADD_ENGINE = auto()  # When request is added to engine via _add_request()
+    ADD_CONTEXT = auto()  # When request is added to context (scheduled for prefill)
+    GENERATED_TOKEN = auto()  # When an output token is generated (payload = {"token_id": int})
     PAUSE = auto()
     EVICT = auto()
     FINISH = auto()
@@ -202,22 +204,30 @@ def __post_init__(self):
             DynamicInferenceEventType.ERROR_NONTRANSIENT,
         ):
             assert self.payload is not None
+        elif self.type == DynamicInferenceEventType.GENERATED_TOKEN:
+            assert (
+                self.payload is not None
+                and isinstance(self.payload, dict)
+                and "token_id" in self.payload
+            )
         else:
             assert self.payload is None
 
     def __str__(self):
-        payload_str = "" if self.payload is None else f", {type(self.payload).__name__}"
+        if self.type == DynamicInferenceEventType.GENERATED_TOKEN:
+            payload_str = f", token={self.payload['token_id']}"
+        elif self.payload is None:
+            payload_str = ""
+        else:
+            payload_str = f", {type(self.payload).__name__}"
         return f"[{self.timestamp:.3f}] {self.type.name}{payload_str}"
 
     def serialize(self) -> dict:
         """Converts the instance into a serializable dictionary.
 
         Returns:
-            (dict) A dictionary representation of the instance suitable for
-                serialization.
+            dict: Full event dict.
         """
-
-        # Dataclass to dict.
         torch.cuda.nvtx.range_push("DynamicInferenceEvent.serialize")
         # do not use asdict(self) - it has very high CPU overheads
         # and if there are tensors, it will try to deepcopy them
@@ -225,10 +235,15 @@ def serialize(self) -> dict:
         obj["type"] = self.type.name
 
         # Serialize payload.
-        if self.payload:
-            from .contexts.dynamic_context import ContextErrorFactory  # avoid circular import.
+        if self.payload is not None:
+            if self.type in (
+                DynamicInferenceEventType.ERROR_TRANSIENT,
+                DynamicInferenceEventType.ERROR_NONTRANSIENT,
+            ):
+                from .contexts.dynamic_context import ContextErrorFactory  # avoid circular import.
+
+                obj["payload"] = ContextErrorFactory.serialize(self.payload)
 
-            obj["payload"] = ContextErrorFactory.serialize(self.payload)
         torch.cuda.nvtx.range_pop()
         return obj
 
@@ -237,22 +252,25 @@ def deserialize(cls, obj: dict) -> "DynamicInferenceEvent":
         """Deserialize event.
 
         Args:
-            obj (dict): Serialized event data.
+            obj: Serialized event data dict.
 
         Returns:
             (DynamicInferenceEvent) Deserialized event.
         """
+        event_type = DynamicInferenceEventType[obj["type"]]
 
-        # Initialize event.
-        event = cls(**{**obj, "type": DynamicInferenceEventType[obj["type"]]})
-
-        # Deserialize payload.
-        if obj["payload"]:
-            from .contexts.dynamic_context import ContextErrorFactory  # avoid circular import.
+        # Pre-process payload before construction (since __post_init__ validates types).
+        init_obj = {**obj, "type": event_type}
+        if obj["payload"] is not None:
+            if event_type in (
+                DynamicInferenceEventType.ERROR_TRANSIENT,
+                DynamicInferenceEventType.ERROR_NONTRANSIENT,
+            ):
+                from .contexts.dynamic_context import ContextErrorFactory  # avoid circular import.
 
-            event.payload = ContextErrorFactory.deserialize(obj["payload"])
+                init_obj["payload"] = ContextErrorFactory.deserialize(obj["payload"])
 
-        return event
+        return cls(**init_obj)
 
 
 @experimental_api
@@ -265,12 +283,16 @@ class DynamicInferenceRequest(InferenceRequest):
     """
 
     request_id: int
-    generated_tokens: List[int] = field(default_factory=list)
     prompt: Optional[str] = None
     prompt_tokens: Optional[torch.Tensor] = None
     # remaining prompt tokens are used for chunked prefill
     remaining_prompt_tokens: Optional[torch.Tensor] = None
+    policy_staleness: Optional[torch.Tensor] = None
+    kv_cache_staleness: Optional[torch.Tensor] = None
     latency: Optional[float] = None
+    # routing_indices stores MoE routing decisions for all tokens generated so far.
+    # Shape: [total_tokens, num_layers, topk] - accumulated across all generation steps
+    routing_indices: Optional[torch.Tensor] = None
     finished_chunk_token_count: int = 0
     stop_word_ids: Optional[List[List[int]]] = None  # Tokenized stop words (populated internally)
 
@@ -286,7 +308,10 @@ def remaining_prompt_length(self):
         """
         return len(self.remaining_prompt_tokens)
 
+    ttft: Optional[float] = None
     events: List[DynamicInferenceEvent] = field(default_factory=list)
+    event_add_engine: Optional[DynamicInferenceEvent] = field(default=None, repr=False)
+    generated_tokens: List[int] = field(default_factory=list)
 
     def __str__(self):
         return ", ".join(
@@ -299,7 +324,7 @@ def __str__(self):
             )
         )
 
-    def serialize(self) -> dict:
+    def serialize(self):
         """Converts the instance into a serializable dictionary.
 
         Returns:
@@ -309,12 +334,24 @@ def serialize(self) -> dict:
         torch.cuda.nvtx.range_push("DynamicInferenceRequest.serialize")
         obj = super().serialize()
         obj["events"] = [e.serialize() for e in self.events]
+        obj.pop("event_add_engine", None)
+
+        # Sanity check routing_indices: Tensor [total_tokens - 1, num_layers, topk]
+        if self.routing_indices is not None:
+            total_tokens = len(self.prompt_tokens) + len(self.generated_tokens)
+            # the last generated token does not undergo a forward pass
+            # hence we expect routing indices for total_tokens - 1
+            assert self.routing_indices.shape[0] == total_tokens - 1, (
+                f"routing_indices first dimension {self.routing_indices.shape[0]} does not match "
+                f"total tokens {total_tokens-1}."
+            )
+
         torch.cuda.nvtx.range_pop()
         return obj
 
     def _post_deserialize(self, obj):
         super()._post_deserialize(obj)
-        self.events = [DynamicInferenceEvent.deserialize(e) for e in obj["events"]]
+        self.events = [DynamicInferenceEvent.deserialize(e) for e in obj.get("events", [])]
 
     @property
     def tracked_metadata(self) -> List[Any]:
@@ -356,13 +393,30 @@ def get_metadata_types() -> List[Tuple[str, torch.dtype, bool]]:
             ("top_n_logprobs", torch.int32, False),  # CPU for torch sampling
         ]
 
-    def add_event(self, type: DynamicInferenceEventType, payload: Optional[Any] = None) -> None:
+    def add_event(
+        self, type: DynamicInferenceEventType, payload: Optional[Any] = None
+    ) -> DynamicInferenceEvent:
         """Add event."""
-        self.events.append(DynamicInferenceEvent(type=type, payload=payload))
+        event = DynamicInferenceEvent(type=type, payload=payload)
+        self.events.append(event)
+        return event
+
+    def add_event_add_engine(self):
+        """Add 'add_engine' event - called when request enters the engine queue."""
+        self.event_add_engine = self.add_event(DynamicInferenceEventType.ADD_ENGINE)
+        return self.event_add_engine
+
+    def add_event_add_context(self):
+        """Add 'add_context' event - called when request is added to context for prefill."""
+        return self.add_event(DynamicInferenceEventType.ADD_CONTEXT)
 
-    def add_event_add(self):
-        """Add 'add' event."""
-        return self.add_event(DynamicInferenceEventType.ADD)
+    def add_event_generated_token(self, token: int):
+        """Add 'generated_token' event - records each generated token.
+
+        Args:
+            token (int): The token ID that was generated.
+        """
+        return self.add_event(DynamicInferenceEventType.GENERATED_TOKEN, {"token_id": token})
 
     def add_event_pause(self):
         """Add 'pause' event."""
@@ -439,6 +493,53 @@ def request_id(self) -> int:
         """
         return self.requests[0].request_id
 
+    @staticmethod
+    def _update_staleness_tensor(
+        tensor: Optional[torch.Tensor], total_tokens: int, increment: bool = True
+    ) -> torch.Tensor:
+        """Update a per-token staleness tensor, extending with zeros if needed.
+
+        Args:
+            tensor: Existing staleness tensor, or None to create a new one.
+            total_tokens: Expected length of the tensor after update.
+            increment: If True, increment all values by 1 (including new positions).
+        """
+        if tensor is None:
+            tensor = torch.zeros(total_tokens, dtype=torch.int32, device='cpu')
+        elif len(tensor) < total_tokens:
+            tensor = torch.cat(
+                (
+                    tensor,
+                    torch.zeros(
+                        total_tokens - len(tensor), dtype=tensor.dtype, device=tensor.device
+                    ),
+                ),
+                dim=0,
+            )
+        if increment:
+            tensor = tensor + 1
+        return tensor
+
+    def increment_staleness(self, policy_only: bool = False):
+        """Increment per-token staleness counters in-place.
+
+        Each call indicates that a training step has occurred since these tokens
+        were generated. Tokens not yet tracked are initialized to 1.
+
+        Args:
+            policy_only: If True, only increment policy_staleness. Use this for
+                evicted requests that have no KV cache to age.
+        """
+        request = self[-1]
+        total_tokens = len(request.prompt_tokens) + len(request.generated_tokens)
+        request.policy_staleness = self._update_staleness_tensor(
+            request.policy_staleness, total_tokens, increment=True
+        )
+        if not policy_only:
+            request.kv_cache_staleness = self._update_staleness_tensor(
+                request.kv_cache_staleness, total_tokens, increment=True
+            )
+
     def checkpoint(self, tokenizer: MegatronTokenizer | None = None):
         """Maintain reference to previous request, and then append a new request
         that concatenates the previous prompt and generations.
@@ -449,6 +550,24 @@ def checkpoint(self, tokenizer: MegatronTokenizer | None = None):
 
         old_request = self[-1]
 
+        total_tokens = len(old_request.prompt_tokens) + len(old_request.generated_tokens)
+
+        # Carry forward policy_staleness without incrementing.
+        policy_staleness = (
+            self._update_staleness_tensor(
+                old_request.policy_staleness, total_tokens, increment=False
+            )
+            if old_request.policy_staleness is not None
+            else None
+        )
+
+        # Reset kv_cache_staleness to 0.
+        kv_cache_staleness = (
+            self._update_staleness_tensor(None, total_tokens, increment=False)
+            if old_request.kv_cache_staleness is not None
+            else None
+        )
+
         # New prompt (concatenate prompt + generated tokens).
         new_prompt_tokens = torch.cat(
             (
@@ -478,7 +597,15 @@ def checkpoint(self, tokenizer: MegatronTokenizer | None = None):
             request_id=old_request.request_id,
             prompt_tokens=new_prompt_tokens,
             sampling_params=new_sampling_params,
+            policy_staleness=policy_staleness,
+            kv_cache_staleness=kv_cache_staleness,
         )
+        # Preserve event_add_engine from old request if it exists, otherwise set it.
+        # This ensures TTFT calculation works correctly for evicted/resumed requests.
+        if old_request.event_add_engine is not None:
+            new_request.event_add_engine = old_request.event_add_engine
+        else:
+            new_request.add_event_add_engine()
         self.requests.append(new_request)
 
     def merge(self, tokenizer: MegatronTokenizer | None = None) -> DynamicInferenceRequest:
@@ -499,12 +626,24 @@ def merge_lists(key):
 
         prompt_tokens = self.requests[0].prompt_tokens
         prompt_text = self.requests[0].prompt
+        routing_indices = None
+        if self.requests[0].routing_indices is not None:
+            routing_indices = torch.cat([r.routing_indices for r in self.requests])
         generated_tokens = merge_lists("generated_tokens")
         try:
             generated_text = "".join(r.generated_text for r in self.requests)
         except TypeError as e:  # generally means r.generated_text is None
             generated_text = None
 
+        # Ensure staleness tensors are always materialized (zeros if never incremented).
+        total_tokens = len(prompt_tokens) + len(generated_tokens)
+        policy_staleness = self._update_staleness_tensor(
+            self.requests[-1].policy_staleness, total_tokens, increment=False
+        )
+        kv_cache_staleness = self._update_staleness_tensor(
+            self.requests[-1].kv_cache_staleness, total_tokens, increment=False
+        )
+
         # Merged request.
         request = DynamicInferenceRequest(
             request_id=self.requests[0].request_id,
@@ -518,10 +657,14 @@ def merge_lists(key):
             generated_log_probs=merge_lists("generated_log_probs"),
             generated_top_n_logprobs=merge_lists("generated_top_n_logprobs"),
             sampling_params=self.requests[0].sampling_params,
+            policy_staleness=policy_staleness,
+            kv_cache_staleness=kv_cache_staleness,
+            ttft=self.requests[0].ttft,
             tpot=merge_lists("tpot"),
             status=self.requests[-1].status,
             latency=self.latency,
             events=merge_lists("events"),
+            routing_indices=routing_indices,
         )
 
         return request
diff --git a/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py b/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
index 6a17de685bf..bc08c418360 100644
--- a/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
+++ b/megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py
@@ -1,8 +1,6 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
 import abc
-import math
-import warnings
 from typing import Any, Dict, Iterable, Optional, Union
 
 import torch
@@ -15,103 +13,67 @@
     send_to_next_pipeline_rank,
 )
 from megatron.core.inference.contexts import BaseInferenceContext
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.utils import get_attr_wrapped_model, get_model_config
+from megatron.core.utils import deprecate_args, get_attr_wrapped_model, get_model_config
+
+DEPRECATED_ARGS = ["inference_wrapper_config", "pg_collection"]
 
 
-# pylint: disable=line-too-long
 class AbstractModelInferenceWrapper(abc.ABC):
     """Abstract inference wrapper
 
     Extend this to create a version for your model.
 
-    The wrapper prepares the model for inference, provides the required input data and runs the forward pass.
+    The wrapper prepares the model for inference, provides the required input data and
+    runs the forward pass.
 
     Args:
         model (Union[GPTModel, LegacyGPTModel]): The actual GPT model (MCore
             or MLM).
-        inference_wrapper_config (InferenceWrapperConfig): Has info like
-            hidden size, vocab size etc.
         inference_context (BaseInferenceContext): Context for managing KV
             cache and other inference params.
-        pg_collection (ProcessGroupCollection): Process groups for model communication.
     """
 
+    @deprecate_args(*DEPRECATED_ARGS)
     def __init__(
         self,
         model: Union['LegacyGPTModel', GPTModel],  # type: ignore[name-defined]
-        inference_wrapper_config: InferenceWrapperConfig,
-        inference_context: Optional[BaseInferenceContext] = None,
-        pg_collection: Optional[ProcessGroupCollection] = None,
+        inference_context: BaseInferenceContext,
     ):
         assert not isinstance(
             model, Iterable
         ), 'interleaving schedule is not supported for inference'
         self.model = model
-        self.inference_wrapper_config = inference_wrapper_config
+        self.config = get_model_config(self.model)
         self.pipeline_communication_dtype = (
-            torch.float
-            if self.inference_wrapper_config.fp32_residual_connection
-            else self.inference_wrapper_config.params_dtype
+            torch.float if self.config.fp32_residual_connection else self.config.params_dtype
         )
-        model_config = get_model_config(self.model)
-        self.sequence_parallel = model_config.sequence_parallel
-
-        if inference_context is None:
-            warnings.warn(
-                "`inference_context` must be passed in as an argument starting in `megatron-core` 0.13."
-            )
-            from megatron.core.inference.contexts import StaticInferenceContext
-
-            inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+        self.sequence_parallel = self.config.sequence_parallel
 
         self.inference_context = inference_context
 
-        if pg_collection is None:
+        # Get the inference pg_collection from the config if it exists; otherwise the training
+        # pg_collection might be used during RL
+        if (pg_collection := self.inference_context.config.pg_collection) is None:
             pg_collection = ProcessGroupCollection.use_mpu_process_groups()
 
         self.tp_group = pg_collection.tp
         self.pp_group = pg_collection.pp
         self.tp_size = torch.distributed.get_world_size(self.tp_group)
 
-        if self.inference_wrapper_config.fp8 is not None:
+        if self.config.fp8 is not None and self.config.transformer_impl != "inference_optimized":
             self.model = prepare_model_for_fp8_inference(self.model)
 
-    @property
-    def inference_params(self):
-        """Getter for deprecated `inference_params`."""
-        warnings.warn(
-            "`inference_params` renamed to `inference_context`, and will be removed in `megatron-core` 0.13."
-        )
-        return self.inference_context
+        # TODO(ksanthanam): Add support for fp4
 
-    @inference_params.setter
-    def inference_params(self, value):
-        """Setter for deprecated `inference_params`."""
-        warnings.warn(
-            "`inference_params` renamed to `inference_context`, and will be removed in `megatron-core` 0.13."
-        )
-        self.inference_context = value
-
-    def prep_model_for_inference(self, prompts_tokens: Optional[torch.Tensor] = None):
+    def prep_model_for_inference(self):
         """A utility function for preparing model for inference
 
         The function gets called once before the auto regressive inference loop.
         It puts the model in eval mode.
 
-        Args:
-            prompts_tokens (torch.Tensor, optional): Deprecated, will be removed in `megatron-core` 0.13
         """
-        if prompts_tokens is not None:
-            warnings.warn(
-                "Passing `prompts_tokens` is deprecated and this argument will be ignored."
-                "This parameter will be removed in `megatron-core` 0.13."
-            )
-
         self.model.eval()
 
         # For TP only model both is_pp_first_stage and _is_pp_last_stage returns True
@@ -137,7 +99,9 @@ def prep_inference_input(self, prompt_tokens) -> Dict[str, Any]:
     def get_batch_for_context_window(self, *args, **kwargs) -> Dict[str, Any]:
         """Returns the input data for inference
 
-        This function gets called iteratively in the inference loop . It can be used to extract relevant input from the prompt tokens, attention mask etc. required for each step in inference.
+        This function gets called iteratively in the inference loop.
+        It can be used to extract relevant input from the prompt tokens, attention mask etc.
+        required for each step in inference.
 
         """
         raise NotImplementedError()
@@ -183,15 +147,16 @@ def _get_batch_size_and_seq_len(
         self, tokens: torch.Tensor, recv_buffer_seq_len: Optional[int] = None
     ):
         """
-        Returns the batch size and sequence length based on the tokens tensor and recv_buffer_seq_len.
+        Returns the batch size and sequence length based on the tokens tensor and
+        recv_buffer_seq_len.
 
         Args:
             tokens (torch.Tensor): The input tensor of shape (batch_size, seq_len).
             recv_buffer_seq_len (int, optional): An optional recv buffer sequence length.
 
         Returns:
-            tuple: A tuple (batch_size, seq_len), where batch_size is the first dimension of tokens
-                   and seq_len is either the second dimension or recv_buffer_seq_len.
+            tuple: A tuple (batch_size, seq_len), where batch_size is the first dimension of
+                tokens and seq_len is either the second dimension or recv_buffer_seq_len.
         """
         batch_size = tokens.shape[0]
         seq_len = recv_buffer_seq_len if recv_buffer_seq_len is not None else tokens.shape[1]
@@ -204,7 +169,7 @@ def _allocate_recv_buffer(self, batch_size, seq_len):
             # sequence parallelism. Static batching does not support sequence parallelism
             # except for the MoE layers which is handled separately.
             seq_len = seq_len // self.tp_size
-        recv_size = (seq_len, batch_size, self.inference_wrapper_config.hidden_size)
+        recv_size = (seq_len, batch_size, self.config.hidden_size)
         return torch.empty(
             recv_size, dtype=self.pipeline_communication_dtype, device=torch.cuda.current_device()
         )
@@ -214,10 +179,12 @@ def forward_pass_without_pipeline_parallel(
     ) -> torch.Tensor:
         """Utility to carry out simple forward pass for TP or no model parallel models
 
-        Runs a very simple forward pass for model. Used  in the case of models without any parallelism or only tensor parallelism.
+        Runs a very simple forward pass for model. Used  in the case of models without any
+        parallelism or only tensor parallelism.
 
         Args:
-            inference_input (Dict[str, Any]): A dict containg the inputs for the gpt model [tokens, position ids, attention mask]
+            inference_input (Dict[str, Any]): A dict containg the inputs for the gpt model
+                [tokens, position ids, attention mask]
 
         Returns:
             torch.Tensor: The output logits of shape [batch_size, seq_len, padded_vocab_size]
@@ -228,16 +195,18 @@ def forward_pass_without_pipeline_parallel(
 
         return logits
 
-    def forward_pass_with_pipeline_parallel_small_input_batch(
+    def forward_pass_with_pipeline_parallel(
         self, inference_input: Dict[str, Any], recv_buffer_seq_len: Optional[int] = None
     ) -> torch.Tensor:
-        """Utility to carry out forward pass for PP models with very small inputs
+        """Utility to carry out forward pass for PP models
 
-        If a model is pipeline parallel, yet, the input global batch is very small, we compute a foward pass on the entire global batch, rather than splitting it up into micro batches and doing something more complex as in the forward_pass_with_pipeline_parallel_large_input_batch method
+        TODO: Add support for asynchronous microbatches
 
         Args:
-            inference_input (Dict[str, Any]): A dict containing the inputs for the gpt model [tokens, position ids, attention mask]
-            recv_buffer_seq_len (int): An optional sequence length for the pipeline parallel recv buffer.
+            inference_input (Dict[str, Any]): A dict containing the inputs for the gpt model
+                [tokens, position ids, attention mask]
+            recv_buffer_seq_len (int): An optional sequence length for the pipeline parallel
+                recv buffer.
 
         Returns:
             torch.Tensor: The output logits of shape [batch_size, seq_len, padded_vocab_size]
@@ -268,98 +237,8 @@ def forward_pass_with_pipeline_parallel_small_input_batch(
             logits = output_tensor
 
             # Explicitly cast logits to expected dtype
-            logits = logits.to(self.inference_wrapper_config.params_dtype)
-
-        return logits
-
-    def forward_pass_with_pipeline_parallel_large_input_batch(
-        self, inference_input: Dict[str, Any], recv_buffer_seq_len=None
-    ) -> torch.Tensor:
-        """Utility to carry out forward pass PP models.
-
-        Runs the forward pass for models which are pipeline parallel.
-        This is more complex than forward_pass_with_pipeline_parallel_small_input_batch because
-        this splits the global batch into small micro batches and runs them through the model.
-
-        Args:
-            inference_input (Dict[str, Any]): A dict containg the inputs for the gpt model [tokens, position ids, attention mask]
-            recv_buffer_seq_len (int): An optional sequence length for the pipeline parallel recv buffer.
-
-        Returns:
-            torch.Tensor: The output logits of shape [batch_size, seq_len, padded_vocab_size]
-        """
-        tokens = inference_input["tokens"]
-        position_ids = inference_input["position_ids"]
-        attention_mask = inference_input["attention_mask"]
-        materialize_only_last_token_logits = (
-            self.inference_context.materialize_only_last_token_logits
-        )
-
-        micro_batch_size = max(
-            1,
-            self.inference_wrapper_config.inference_batch_times_seqlen_threshold // tokens.size(1),
-        )
-        batch_size, seq_len = self._get_batch_size_and_seq_len(tokens, recv_buffer_seq_len)
-        # Round up to account for the last partial micro batch if present
-        num_micro_batches = math.ceil(batch_size / micro_batch_size)
-
-        logits = None
-        # Preallocate memory for output logits.
-        if is_pipeline_last_stage(self.pp_group):
-            logits_seq_len = 1 if materialize_only_last_token_logits else seq_len
-            logits = torch.empty(
-                (batch_size, logits_seq_len, self.inference_wrapper_config.padded_vocab_size),
-                dtype=self.pipeline_communication_dtype,
-                device=torch.cuda.current_device(),
-            )
-
-        recv_buffer = None
-        if not is_pipeline_first_stage(self.pp_group):
-            recv_buffer = self._allocate_recv_buffer(micro_batch_size, seq_len)
-        for micro_batch_index in range(num_micro_batches):
-            start = micro_batch_index * micro_batch_size
-            end = min(start + micro_batch_size, batch_size)
-            tokens2use = tokens[start:end, ...]
-            position_ids2use = position_ids[start:end, ...]
-            current_micro_batch_size = end - start
-
-            # Need to change recv buffer shape for the last partial microbatch (if exists)
-            if current_micro_batch_size != micro_batch_size:
-                recv_buffer = self._allocate_recv_buffer(current_micro_batch_size, seq_len)
-
-            if not is_pipeline_first_stage(self.pp_group):
-                recv_from_prev_pipeline_rank_(recv_buffer, self.pp_group)
-
-            self.model.set_input_tensor(recv_buffer)
-
-            output_tensor = self._forward(
-                {
-                    "tokens": tokens2use,
-                    "position_ids": position_ids2use,
-                    "attention_mask": attention_mask,
-                    "inference_context": self.inference_context,
-                }
-            )
-
-            if not is_pipeline_last_stage(self.pp_group):
-                send_to_next_pipeline_rank(output_tensor, self.pp_group)
-
-            self.inference_context.batch_size_offset += current_micro_batch_size
-
-            if is_pipeline_last_stage(self.pp_group):
-                assert logits is not None
-                logits[start:end, ...] = output_tensor
-
-        # Explicitly cast logits to expected dtype
-        if is_pipeline_last_stage(self.pp_group):
-            assert logits is not None
-            logits = logits.to(self.inference_wrapper_config.params_dtype)
-
-        # Once done with all micro batches, we reset batch size offset and seq len offset
-        self.inference_context.increment_sequence_len_offset(seq_len)
-        self.inference_context.reset_batch_size_offset()
+            logits = logits.to(self.config.params_dtype)
 
-        # NOTE: Only returns the logits on the last pipeline stage
         return logits
 
     @torch.inference_mode()
@@ -368,14 +247,18 @@ def run_one_forward_step(
     ) -> torch.Tensor:
         """The forward pass of the model for inference
 
-        Appropriate utility is called for the forward pass depending on the type of model parallelism used
+        Appropriate utility is called for the forward pass depending on the type of model
+        parallelism used
 
         Args:
-            inference_input (Dict[str, Any]): A dict containing the inputs for the gpt model [tokens, position ids, attention mask]
-            recv_buffer_seq_len (int): An optional sequence length for the pipeline parallel recv buffer.
+            inference_input (Dict[str, Any]): A dict containing the inputs for the gpt model
+                [tokens, position ids, attention mask]
+            recv_buffer_seq_len (int): An optional sequence length for the pipeline parallel
+                recv buffer.
 
         Returns:
-            torch.Tensor: The output logits of shape [batch_size, seq_len, padded_vocab_size]. The logits are returned only in the last pipeline stage for PP models.
+            torch.Tensor: The output logits of shape [batch_size, seq_len, padded_vocab_size].
+            The logits are returned only in the last pipeline stage for PP models.
         """
         # Check if we are in a PP model
         if not (is_pipeline_first_stage(self.pp_group) and is_pipeline_last_stage(self.pp_group)):
@@ -383,19 +266,6 @@ def run_one_forward_step(
             current_batch_size, seq_len = self._get_batch_size_and_seq_len(
                 tokens, recv_buffer_seq_len
             )
-            # If input batch is large, we need to split into micro batches and run the forward pass
-            if (
-                current_batch_size * seq_len
-                > self.inference_wrapper_config.inference_batch_times_seqlen_threshold
-                and self.inference_wrapper_config.inference_batch_times_seqlen_threshold != -1
-            ):
-                return self.forward_pass_with_pipeline_parallel_large_input_batch(
-                    inference_input, recv_buffer_seq_len
-                )
-            else:
-                # If input batch is very small we can do a simple forward pass on the entire global batch
-                return self.forward_pass_with_pipeline_parallel_small_input_batch(
-                    inference_input, recv_buffer_seq_len
-                )
+            return self.forward_pass_with_pipeline_parallel(inference_input, recv_buffer_seq_len)
         else:
             return self.forward_pass_without_pipeline_parallel(inference_input)
diff --git a/megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py b/megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py
index ba89fbc2f6c..67cfc1bae48 100644
--- a/megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py
+++ b/megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py
@@ -7,14 +7,12 @@
 from megatron.core.inference.model_inference_wrappers.abstract_model_inference_wrapper import (
     AbstractModelInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.utils import get_attention_mask
 from megatron.core.models.gpt import GPTModel
-from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer.enums import AttnBackend
-from megatron.core.utils import get_model_config
+from megatron.core.utils import deprecate_args, get_model_config
+
+DEPRECATED_ARGS = ["inference_wrapper_config", "pg_collection"]
 
 
 # pylint: disable=line-too-long
@@ -25,22 +23,13 @@ class GPTInferenceWrapper(AbstractModelInferenceWrapper):
 
     Args:
         model (GPTModel): The GPT model (MCore or legacy)
-        inference_wrapper_config (InferenceWrapperConfig): Has info like hidden size, vocab
-            size, etc.
         inference_context (BaseInferenceContext): Manages KV cache, and tracks
             sequence/token/batch offsets.
-        pg_collection (ProcessGroupCollection): Process groups for model communication.
-            If not provided, defaults to global parallel state groups.
     """
 
-    def __init__(
-        self,
-        model: GPTModel,
-        inference_wrapper_config: InferenceWrapperConfig,
-        inference_context: Optional[BaseInferenceContext] = None,
-        pg_collection: Optional[ProcessGroupCollection] = None,
-    ):
-        super().__init__(model, inference_wrapper_config, inference_context, pg_collection)
+    @deprecate_args(*DEPRECATED_ARGS)
+    def __init__(self, model: GPTModel, inference_context: Optional[BaseInferenceContext] = None):
+        super().__init__(model, inference_context)
 
     def prep_inference_input(self, prompts_tokens: torch.Tensor) -> Dict[str, Any]:
         """Prepares the inference input data.
diff --git a/megatron/core/inference/model_inference_wrappers/inference_wrapper_config.py b/megatron/core/inference/model_inference_wrappers/inference_wrapper_config.py
deleted file mode 100644
index 5d89085add2..00000000000
--- a/megatron/core/inference/model_inference_wrappers/inference_wrapper_config.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-
-
-@dataclass
-class InferenceWrapperConfig:
-    """Config for the model inference wrapper
-
-    NOTE : All the arguments here are obtained from arguments.py file
-    """
-
-    hidden_size: int
-    """Receive happens between the layers during PP with size [seq_len, batch_size, hidden_size]"""
-
-    params_dtype: torch.dtype
-    """Can be torch.float or torch.half if --fp16 is used, or torch.bfloat16 if --bf16 is used"""
-
-    inference_batch_times_seqlen_threshold: int
-    """if (batch-size * sequence-length) is smaller than this threshold then we will not pipeline 
-    the batch."""
-
-    padded_vocab_size: int
-    """The final padded vocab size (Padded to make it divisible by 
-    --make-vocab-size-divisible-by value)"""
-
-    inference_max_requests: int = 8
-    """ Maximum number of requests for inference (prefill & decode). Necessary for CUDA graphs. """
-
-    inference_max_seq_length: int = 2560
-    """ Maximum sequence length for inference (prefill & decode). Necessary for CUDA graphs. """
-
-    fp32_residual_connection: bool = False
-    """Move residual connections to fp32. Obtained from arguments.py"""
-
-    nccl_all_reduce_for_prefill: bool = False
-    """When using symmetric all reduce kernels we keep the default all reduces for nccl. 
-    This can be more effecient for large prefill sizes"""
-
-    fp8: Optional[str] = None
-    """If set, enables the use of FP8 precision through Transformer Engine. There are 2 predefined
-    choices (1) 'e4m3' uniformly uses e4m3 for all FP8 tensors, (2) 'hybrid' uses e4m3 for all FP8
-    activation and weight tensors and e5m2 for all FP8 output activation gradient tensors."""
-
-    moe_pad_experts_for_cuda_graph_inference: bool = False
-    """Some MoE routers have a D2H sync that will break cuda graphs.  If this flag is set the router
-    will switch to dropping and padding during decode time which does not have a D2H sync. The 
-    capacity factor is set to the max that an expert could see during inference so no tokens are
-    actually dropped. """
-
-    def add_attributes(self, attribute_value_pair: dict):
-        """Utility to add more attributes to inference params
-
-        Use this method to pass in a custom dictionary to add more configs to the instance created.
-        Use as follows:
-        c = InferenceWrapperConfig
-        c.add_attributes({'precision':'fp32'})
-
-        Args:
-            attribute_value_pair (dict): A dictionary containing attributes as the key names and
-            corresponding values.
-        """
-        for key, value in attribute_value_pair.items():
-            setattr(self, key, value)
diff --git a/megatron/core/inference/model_inference_wrappers/t5/t5_inference_wrapper.py b/megatron/core/inference/model_inference_wrappers/t5/t5_inference_wrapper.py
index 2ae1e2ade6f..c773ab507a3 100644
--- a/megatron/core/inference/model_inference_wrappers/t5/t5_inference_wrapper.py
+++ b/megatron/core/inference/model_inference_wrappers/t5/t5_inference_wrapper.py
@@ -11,9 +11,6 @@
 from megatron.core.inference.model_inference_wrappers.abstract_model_inference_wrapper import (
     AbstractModelInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.models.T5 import T5Model
 from megatron.core.utils import get_attr_wrapped_model
 
@@ -27,7 +24,6 @@ class T5InferenceWrapper(AbstractModelInferenceWrapper):
 
     Args:
         model (T5Model): The T5 model (MCore or legacy)
-        inference_wrapper_config (InferenceWrapperConfig): The command line arguments that were passed
         inference_context (BaseInferenceContext): Manages KV cache, and tracks
             sequence/token/batch offsets.
         use_local (bool): Whether  the T5 model's transformer impl
@@ -37,11 +33,10 @@ class T5InferenceWrapper(AbstractModelInferenceWrapper):
     def __init__(
         self,
         model: T5Model,
-        inference_wrapper_config: InferenceWrapperConfig,
         inference_context: Optional[BaseInferenceContext] = None,
         use_local: bool = False,
     ):
-        super().__init__(model, inference_wrapper_config, inference_context)
+        super().__init__(model, inference_context)
         self.use_local = use_local
 
     def prep_inference_input(
diff --git a/megatron/core/inference/quantization/__init__.py b/megatron/core/inference/quantization/__init__.py
new file mode 100644
index 00000000000..26496bfed70
--- /dev/null
+++ b/megatron/core/inference/quantization/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
diff --git a/megatron/core/inference/quantization/mxfp8_tensor.py b/megatron/core/inference/quantization/mxfp8_tensor.py
new file mode 100644
index 00000000000..cadbd86b0ef
--- /dev/null
+++ b/megatron/core/inference/quantization/mxfp8_tensor.py
@@ -0,0 +1,37 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+try:
+    from flashinfer import mxfp8_quantize
+
+    HAVE_FLASHINFER = True
+except ImportError:
+    HAVE_FLASHINFER = True
+
+
+@dataclass
+class MXFP8Tensor:
+    """MXFP8 tensor wrapper class."""
+
+    data: torch.Tensor
+    scale: torch.Tensor
+
+    def size(self, idx: Optional[int] = None):
+        """Wrapper for calling self.data.size()"""
+        return self.data.size(idx)
+
+    @classmethod
+    def from_bf16(cls, x: torch.Tensor, group_size: int = 32):
+        """Quantize BF16 tensor to MXFP8 format using FlashInfer."""
+
+        assert HAVE_FLASHINFER, "Need flashinfer for mxfp8 quantization"
+        assert x.is_cuda, "Input must be on CUDA"
+        assert x.dim() == 2, "Input must be 2D [M, K]"
+        M, K = x.shape
+        assert K % group_size == 0, f"K ({K}) must be divisible by group_size ({group_size})"
+
+        return cls(*mxfp8_quantize(x))
diff --git a/megatron/core/inference/quantization/utils.py b/megatron/core/inference/quantization/utils.py
new file mode 100644
index 00000000000..c02f40cc185
--- /dev/null
+++ b/megatron/core/inference/quantization/utils.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import torch
+
+from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+
+try:
+    from transformer_engine.pytorch.tensor.mxfp8_tensor import MXFP8Tensor as TEMXFP8Tensor
+
+    HAVE_TE = True
+except ImportError:
+    HAVE_TE = False
+
+try:
+    from flashinfer import mm_mxfp8 as flashinfer_mm_mxfp8
+
+    HAVE_FLASHINFER = True
+except ImportError:
+    HAVE_FLASHINFER = False
+
+
+def _verify_te_to_flashinfer_mxfp8_conversion(te_dequantized, fi_quantized: MXFP8Tensor) -> None:
+    # Sanity check: compare the first logical block (32 values)
+    # Slice logical dimensions first to naturally handle any data swizzling/strides
+    te_block = te_dequantized[0, :32].float()
+
+    # Safely extract bytes from the first logical block, then view as e4m3
+    fi_data_bytes = fi_quantized.data[0, :32].contiguous().view(torch.uint8)
+    fi_data_e4m3 = fi_data_bytes.view(torch.float8_e4m3fn).float()
+
+    # Extract the scale. Logical block (0, 0) is always at physical index 0,
+    # bypassing any scale swizzling layout complexity (like SWIZZLED_128x4)
+    fi_scale_byte = fi_quantized.scale.contiguous().flatten()[0:1].view(torch.uint8).to(torch.int32)
+    fi_scale_f32 = (fi_scale_byte << 23).view(torch.float32)
+
+    fi_block = fi_data_e4m3 * fi_scale_f32
+
+    if not torch.allclose(te_block, fi_block):
+        diff_norm = torch.norm(te_block - fi_block)
+        raise ValueError(f"MXFP8 sanity check failed. Diff norm: {diff_norm}")
+
+
+def quantize_model_to_mxfp8(model: torch.nn.Module) -> None:
+    """
+    Converts a TE MXFP8 model to a FlashInfer MXFP8 model by
+    recursively translating each layer's weights.
+    """
+    assert HAVE_TE
+    assert HAVE_FLASHINFER
+
+    # Recurse through child modules
+    for child in model.children():
+        quantize_model_to_mxfp8(child)
+
+    def replace_in_dict(attr_dict):
+        """Helper function to replace TE MXFP8 weights."""
+        keys = list(attr_dict.keys())
+        for key in keys:
+            val = attr_dict[key]
+            if isinstance(val, TEMXFP8Tensor):
+                # Undo the TE quantization and re-quantize with FlashInfer
+                # Note that this introduces a one-time overhead but avoids any
+                # numerical differences between TE and FlashInfer MXFP8 formats
+                te_dequantized = val.dequantize()
+                fi_quantized = MXFP8Tensor.from_bf16(te_dequantized)
+
+                # Sanity check the numerical correctness of the TE -> FlashInfer conversion
+                _verify_te_to_flashinfer_mxfp8_conversion(te_dequantized, fi_quantized)
+
+                # Remove the existing TE parameter and then replace the
+                # attribute with the re-quantized tensor
+                del model._parameters[key]
+                setattr(model, key, fi_quantized)
+
+    if hasattr(model, '_parameters') and model._parameters:
+        replace_in_dict(model._parameters)
+
+    return model
+
+
+def mm_mxfp8(x: torch.Tensor, weight: MXFP8Tensor, out: torch.Tensor = None):
+    """
+    Computes a matmul in MXFP8 using FlashInfer.
+
+    Quantizes the bf16 input activation tensor. Weight must be pre-quantized.
+    """
+    assert HAVE_FLASHINFER
+
+    x = MXFP8Tensor.from_bf16(x.squeeze(1))
+    return flashinfer_mm_mxfp8(
+        x.data, weight.data.T, x.scale, weight.scale, out_dtype=torch.bfloat16, out=out
+    ).unsqueeze(1)
diff --git a/megatron/core/inference/sampling_params.py b/megatron/core/inference/sampling_params.py
index ba1acae4c57..3338fbe2879 100644
--- a/megatron/core/inference/sampling_params.py
+++ b/megatron/core/inference/sampling_params.py
@@ -24,7 +24,7 @@ class SamplingParams:
     return_log_probs: bool = False
     skip_prompt_log_probs: bool = False
     return_segments: bool = False  # Whether to return individually detokenized tokens
-    num_tokens_to_generate: int = 30
+    num_tokens_to_generate: int = None
     num_tokens_total: Optional[int] = None  # Cannot set both this and num_tokens_to_generate
     termination_id: Optional[int] = None
     top_n_logprobs: int = 0
diff --git a/megatron/core/inference/text_generation_controllers/simple_text_generation_controller.py b/megatron/core/inference/text_generation_controllers/simple_text_generation_controller.py
deleted file mode 100644
index 340cadb48a9..00000000000
--- a/megatron/core/inference/text_generation_controllers/simple_text_generation_controller.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
-
-from megatron.core.inference.text_generation_controllers.text_generation_controller import (  # noqa: F401 # pylint: disable=unused-import
-    TextGenerationController as SimpleTextGenerationController,
-)
diff --git a/megatron/core/inference/text_generation_controllers/text_generation_controller.py b/megatron/core/inference/text_generation_controllers/text_generation_controller.py
index a5233983ed0..f56e5b1c761 100644
--- a/megatron/core/inference/text_generation_controllers/text_generation_controller.py
+++ b/megatron/core/inference/text_generation_controllers/text_generation_controller.py
@@ -11,25 +11,28 @@
 import torch
 import torch.nn.functional as F
 from torch import Tensor
-from torch.distributed import ProcessGroup
 
+from megatron.core import parallel_state
 from megatron.core.inference.async_stream import AsyncStream
 from megatron.core.inference.communication_utils import (
     broadcast_from_last_pipeline_stage,
-    is_pipeline_first_stage,
     is_pipeline_last_stage,
 )
 from megatron.core.inference.contexts.dynamic_context import MaxSequenceLengthOverflowError
+from megatron.core.inference.contexts.static_context import StaticInferenceContext
 from megatron.core.inference.inference_request import InferenceRequest, Status
 from megatron.core.inference.model_inference_wrappers.abstract_model_inference_wrapper import (
     AbstractModelInferenceWrapper,
 )
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.utils import get_attention_mask, set_decode_expert_padding
+from megatron.core.models.multimodal.llava_model import LLaVAModel
+from megatron.core.tensor_parallel.mappings import gather_from_sequence_parallel_region
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.moe.moe_layer import BaseMoELayer
+from megatron.core.transformer.moe.router_replay import RouterReplay, RouterReplayAction
 from megatron.core.transformer.utils import set_model_to_sequence_parallel
-from megatron.core.utils import get_asyncio_loop, get_model_config, unwrap_model
+from megatron.core.utils import get_asyncio_loop, get_model_config, get_pg_size, unwrap_model
 
 try:
     import transformer_engine as te  # pylint: disable=unused-import
@@ -52,28 +55,32 @@ class TextGenerationController:
         inference_wrapped_model (AbstractModelInferenceWrapper): A model that
             is wrapped using the specs given in the abstract_model_inference_wrapper.py
         tokenizer (_type_): Tokenizer used for tokenizing and detokenizing the prompts
-        pp_group (ProcessGroup): Process group for pipeline parallelism
     """
 
-    def __init__(
-        self,
-        inference_wrapped_model: AbstractModelInferenceWrapper,
-        tokenizer,
-        pp_group: ProcessGroup = None,
-    ):
+    def __init__(self, inference_wrapped_model: AbstractModelInferenceWrapper, tokenizer):
         self.inference_wrapped_model = inference_wrapped_model
+        self.model_config = self.inference_wrapped_model.model.config
+        inference_config = self.inference_wrapped_model.inference_context.config
         self.tokenizer = tokenizer
 
-        self.pp_group = pp_group
+        pg_collection = inference_config.pg_collection
+        if pg_collection is not None:
+            self.pp_group = pg_collection.pp
+        else:
+            self.pp_group = parallel_state.get_pipeline_model_parallel_group()
 
-        # For models without pipeline parallelism, is_first_stage and is_last_stage returns True
-        self.model_is_pipeline_parallel = not (
-            is_pipeline_first_stage(self.pp_group) and is_pipeline_last_stage(self.pp_group)
-        )
+        self.model_is_pipeline_parallel = self.model_config.pipeline_model_parallel_size > 1
+
+        # Use padded vocab size because tokenizer vocab size might pad to nearest power of 2.
+        # TODO(ksanthanam): Consider deprecating this check if LLaVAModel is no longer used
+        unwrapped_model = unwrap_model(self.inference_wrapped_model.model)
+        if isinstance(unwrapped_model, LLaVAModel):
+            self.vocab_size = unwrapped_model.language_model.vocab_size
+        else:
+            self.vocab_size = unwrapped_model.vocab_size
 
-        model_config = get_model_config(self.inference_wrapped_model.model)
         self.sampling_rng = torch.Generator(device=torch.cuda.current_device())
-        self.sampling_rng.manual_seed(model_config.inference_sampling_seed)
+        self.sampling_rng.manual_seed(self.model_config.inference_sampling_seed)
 
         if self.inference_wrapped_model.inference_context.is_dynamic_batching():
             self._init_dynamic_sampling_tensors()
@@ -98,9 +105,7 @@ def _init_dynamic_sampling_tensors(self):
         self._get_stop_word_finished_ids_callback = None
 
         device = torch.cuda.current_device()
-        logits_dtype = self.inference_wrapped_model.inference_wrapper_config.params_dtype
-        # Use padded vocab size because tokenizer vocab size might pad to nearest power of 2.
-        vocab_size = self.inference_wrapped_model.inference_wrapper_config.padded_vocab_size
+        logits_dtype = self.inference_wrapped_model.config.params_dtype
 
         self._sampling_backend = "torch"
         self._sampled_tokens_cuda = torch.empty(max_requests, dtype=torch.int64, device=device)
@@ -505,7 +510,6 @@ def _dynamic_step_context_init(
             position_ids (Tensor): The active position IDs.
         """
         context = self.inference_wrapped_model.inference_context
-        inference_wrapper_config = self.inference_wrapped_model.inference_wrapper_config
         active_request_slice = slice(context.paused_request_count, context.total_request_count)
 
         # Remove Float16Module wrapper if it exists
@@ -517,11 +521,11 @@ def _dynamic_step_context_init(
 
         # If using symmetric kernels and we are using using nccl
         # for prefill turn off symmetric kernels
-        symmetric_ar_type = model_config.symmetric_ar_type
-        nccl_all_reduce_for_prefill = inference_wrapper_config.nccl_all_reduce_for_prefill
+        symmetric_ar_type = self.model_config.symmetric_ar_type
+        nccl_all_reduce_for_prefill = self.model_config.nccl_all_reduce_for_prefill
         # Turning on/off MoE padding for cuda-graphs
         moe_pad_experts_for_cuda_graph_inference = (
-            inference_wrapper_config.moe_pad_experts_for_cuda_graph_inference
+            self.model_config.moe_pad_experts_for_cuda_graph_inference
         )
         if moe_pad_experts_for_cuda_graph_inference:
             if context.using_cuda_graph_this_step():
@@ -569,8 +573,6 @@ def _dynamic_step_forward_logits(self, input_ids: Tensor, position_ids: Tensor)
             input_ids (Tensor): The input token IDs.
             position_ids (Tensor): The position IDs.
         """
-        inference_wrapper_config = self.inference_wrapped_model.inference_wrapper_config
-
         context = self.inference_wrapped_model.inference_context
         active_request_count = context.total_request_count - context.paused_request_count
 
@@ -582,18 +584,17 @@ def _dynamic_step_forward_logits(self, input_ids: Tensor, position_ids: Tensor)
         if self.model_is_pipeline_parallel:
             logits_seq_len = (
                 active_request_count
-                if context.materialize_only_last_token_logits
+                if context.config.materialize_only_last_token_logits
                 else input_ids.shape[1]
             )
-            vocab_size = inference_wrapper_config.padded_vocab_size
-            logits_shape = [1, logits_seq_len, vocab_size]
+            logits_shape = [1, logits_seq_len, self.vocab_size]
 
             if is_pipeline_last_stage(self.pp_group):
                 assert logits is not None and torch.Size(logits_shape) == logits.shape
 
             logits = broadcast_from_last_pipeline_stage(
                 logits_shape,
-                dtype=inference_wrapper_config.params_dtype,
+                dtype=self.model_config.params_dtype,
                 tensor=logits,
                 pp_group=self.pp_group,
             )
@@ -639,7 +640,7 @@ def _dynamic_step_sample_logits(self, logits: Tensor):
 
         # Last token logits.
         context = self.inference_wrapped_model.inference_context
-        if context.materialize_only_last_token_logits:
+        if context.config.materialize_only_last_token_logits:
             # When materialize_only_last_token_logits is true, last_token_logits is
             # already called in the forward pass of GPT.
             last_token_logits = logits.squeeze(0)
@@ -676,6 +677,66 @@ def _dynamic_step_log_probs_bookkeeping(self) -> Tuple[bool, bool]:
 
         return return_log_probs.any(), top_n_log_probs.any()
 
+    def _router_record_bookkeeping(self) -> Optional[Dict[int, Tensor]]:
+        """Collect and map routing indices per request for MoE router recording.
+
+        This method retrieves recorded routing decisions and maps them to individual
+        requests using the context's request_ids and query_lengths. Uses the context's
+        routing_metadata when available (which handles CUDA graph static buffers automatically).
+        Must be called while context attributes are still valid (before request transitions).
+
+        Returns:
+            Optional[Dict[int, Tensor]]: A dictionary mapping request_id to a tensor of
+                shape [num_tokens, num_layers, topk]. Returns None if routing replay is
+                disabled or no routing data was recorded.
+        """
+        config = self.inference_wrapped_model.model.config
+        if not config.moe_enable_routing_replay:
+            return None
+
+        # Get routing indices - use routing_metadata if available (handles CUDA graph static buffers)
+        context = self.inference_wrapped_model.inference_context
+        if context.moe_routing_metadata is None:
+            return None
+
+        stacked_routing = context.moe_routing_metadata.get_routing_indices()
+
+        if stacked_routing is None:
+            return None
+
+        # Get active request info from context
+        active_request_slice = slice(context.paused_request_count, context.total_request_count)
+        active_request_ids = context.request_ids[active_request_slice].tolist()
+        active_query_lengths = context.request_query_lengths[active_request_slice].tolist()
+        active_token_count = context.active_token_count
+
+        # Get TP group for all-gather if using sequence parallelism
+        # With sequence parallelism, each TP rank only sees a portion of the tokens,
+        # so we need to gather routing indices across all TP ranks.
+        tp_group = self.inference_wrapped_model.tp_group
+        tp_size = get_pg_size(tp_group)
+
+        # All-gather across TP group if using sequence parallelism (tp_size > 1)
+        if tp_size > 1 and get_model_config(self.inference_wrapped_model.model).sequence_parallel:
+            # gather_from_sequence_parallel_region gathers along dim 0
+            # [local_token_count, num_layers, topk] -> [global_token_count, num_layers, topk]
+            stacked_routing = gather_from_sequence_parallel_region(stacked_routing, group=tp_group)
+
+        # Slice to real tokens (remove CUDA padding)
+        stacked_routing = stacked_routing[:active_token_count]
+
+        # Split by request along token dimension
+        # stacked_routing has shape [active_token_count, num_layers, topk]
+        routing_splits = stacked_routing.split(active_query_lengths, dim=0)
+
+        # Map to request IDs
+        routing_indices_per_request = {}
+        for req_id, routing_split in zip(active_request_ids, routing_splits):
+            # routing_split has shape [num_tokens_for_request, num_layers, topk]
+            routing_indices_per_request[req_id] = routing_split
+
+        return routing_indices_per_request
+
     def _dynamic_step_calculate_log_probs(self, logits: Tensor) -> Optional[Tensor]:
         """Calculate log probs from logits."""
         context = self.inference_wrapped_model.inference_context
@@ -684,7 +745,7 @@ def _dynamic_step_calculate_log_probs(self, logits: Tensor) -> Optional[Tensor]:
         return context.calculate_log_probs(
             logits,
             self._sampled_tokens_cuda[:active_request_count],
-            only_last_token_logits=context.materialize_only_last_token_logits,
+            only_last_token_logits=context.config.materialize_only_last_token_logits,
         )
 
     def _dynamic_step_calculate_top_n_logprobs(
@@ -712,7 +773,7 @@ def _dynamic_step_calculate_top_n_logprobs(
         active_request_slice = slice(context.paused_request_count, context.total_request_count)
 
         # Handle decode-only mode (only last token)
-        if context.materialize_only_last_token_logits or context.is_decode_only():
+        if context.config.materialize_only_last_token_logits or context.is_decode_only():
             # In decode mode or when only last token logits are materialized,
             # logits already represent only the last tokens
             log_probs = log_probs_tensor[:active_request_count]
@@ -890,8 +951,16 @@ async def async_generate_output_tokens_dynamic_batch(
             context.padded_active_request_count if context.is_decode_only() else None
         )
 
+        # Enable routing recording before forward pass if routing replay is enabled
+        config = self.inference_wrapped_model.model.config
+        if config.moe_enable_routing_replay:
+            RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)
+
         logits = self._dynamic_step_forward_logits(input_ids, position_ids)
 
+        # Collect routing indices per request (must be done before context transitions)
+        routing_indices_per_request = self._router_record_bookkeeping()
+
         # This is the best place to yield control back to event loop.
         # At this point we have enqueued FW pass GPU kernels asynchronously.
         # While they are running, we can do other useful CPU work.
@@ -923,6 +992,7 @@ async def async_generate_output_tokens_dynamic_batch(
             "sample": self._sampled_tokens_cuda[:active_request_count],
             "log_probs": log_probs,
             "top_n_logprobs": top_n_logprobs,
+            "routing_indices_per_request": routing_indices_per_request,
             "cuda_graph_request_count": cuda_graph_request_count,
         }
         ret.update(request_bookkeeping)
@@ -1024,9 +1094,10 @@ def generate_all_output_tokens_static_batch(
         # Pad batch tokens if necessary
         batch_size = len(active_requests)
         max_sequence_length = max_prompt_length_in_batch + sampling_params.num_tokens_to_generate
-        inference_wrapper_config = self.inference_wrapped_model.inference_wrapper_config
-        inference_max_batch_size = inference_wrapper_config.inference_max_requests
-        inference_max_sequence_length = inference_wrapper_config.inference_max_seq_length
+        context = self.inference_wrapped_model.inference_context
+        assert isinstance(context, StaticInferenceContext)
+        inference_max_batch_size = context.max_batch_size
+        inference_max_sequence_length = context.max_sequence_length
         padded_batch_size = inference_max_batch_size if enable_cuda_graph else batch_size
         if padded_batch_size > inference_max_batch_size:
             raise ValueError(
@@ -1066,10 +1137,6 @@ def generate_all_output_tokens_static_batch(
             batch_size, device=torch.cuda.current_device()
         ).cuda()
 
-        # Use padded vocab size because tokenizer vocab size might not include padding
-        # to nearest power of 2
-        vocab_size = inference_wrapper_config.padded_vocab_size
-
         # Check whether early termination is enabled
         no_early_termination = getattr(sampling_params, "no_early_termination", False)
         termination_id = -1 if no_early_termination else self.tokenizer.eod
@@ -1130,14 +1197,14 @@ def generate_all_output_tokens_static_batch(
 
             # If using symmetric kernels and we are using using nccl
             # for prefill turn off symmetric kernels
-            symmetric_ar_type = model_config.symmetric_ar_type
-            nccl_all_reduce_for_prefill = inference_wrapper_config.nccl_all_reduce_for_prefill
+            symmetric_ar_type = self.model_config.symmetric_ar_type
+            nccl_all_reduce_for_prefill = self.model_config.nccl_all_reduce_for_prefill
             if symmetric_ar_type is not None and nccl_all_reduce_for_prefill:
                 unwrapped_model.set_symmetric_ar(None)
 
             # Turning off MoE padding for prefill
             moe_pad_experts_for_cuda_graph_inference = (
-                inference_wrapper_config.moe_pad_experts_for_cuda_graph_inference
+                self.model_config.moe_pad_experts_for_cuda_graph_inference
             )
             if moe_pad_experts_for_cuda_graph_inference:
                 set_decode_expert_padding(unwrapped_model, False)
@@ -1191,7 +1258,7 @@ def generate_all_output_tokens_static_batch(
                     or not (sampling_params.return_log_probs or sampling_params.top_n_logprobs > 0)
                 )
                 inference_context = self.inference_wrapped_model.inference_context
-                inference_context.materialize_only_last_token_logits = (
+                inference_context.config.materialize_only_last_token_logits = (
                     materialize_only_last_token_logits
                 )
 
@@ -1212,14 +1279,14 @@ def generate_all_output_tokens_static_batch(
                 if self.model_is_pipeline_parallel:
                     context_length = context_end_position - context_start_position
                     logits_seq_len = 1 if materialize_only_last_token_logits else context_length
-                    logits_shape = [batch_size, logits_seq_len, vocab_size]
+                    logits_shape = [batch_size, logits_seq_len, self.vocab_size]
                     if is_pipeline_last_stage(self.pp_group):
                         assert logits is not None and torch.Size(logits_shape) == logits.shape
                     # TODO(ksanthanam): Evaluate whether it makes more sense to sample on 1 rank
                     # and then broadcast the sampled tokens rather than broadcasting the raw logits.
                     logits = broadcast_from_last_pipeline_stage(
-                        [batch_size, logits_seq_len, vocab_size],
-                        dtype=inference_wrapper_config.params_dtype,
+                        [batch_size, logits_seq_len, self.vocab_size],
+                        dtype=self.model_config.params_dtype,
                         tensor=logits,
                         pp_group=self.pp_group,
                     )
@@ -1248,7 +1315,7 @@ def generate_all_output_tokens_static_batch(
                 sampled_logits = self.sample_from_logits(
                     last_token_logits,
                     sampling_params,
-                    vocab_size,
+                    self.vocab_size,
                     generation_started=generation_started,
                     top_n_logprobs_dict=top_n_logprobs_dict,
                     logits=logits_for_top_n_prompt_logprobs,
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
index 0c3379bc53f..a4cb61fb962 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/chat_completions.py
@@ -3,8 +3,12 @@
 import asyncio
 import logging
 import time
+import traceback
+import uuid
+import warnings
 
 from megatron.core.inference.sampling_params import SamplingParams
+from megatron.core.tokenizers.text.parsers import PARSER_MAPPING
 
 logger = logging.getLogger(__name__)
 
@@ -19,6 +23,7 @@ async def chat_completions():
         """Handles async POST requests for chat completions."""
         client = current_app.config['client']
         tokenizer = current_app.config['tokenizer']
+        parsers = current_app.config['parsers']
 
         req = request.get_json()
 
@@ -31,14 +36,17 @@ async def chat_completions():
 
         try:
             prompt_tokens = tokenizer.apply_chat_template(
-                messages, tokenize=True, add_generation_prompt=True
+                messages, tokenize=True, add_generation_prompt=True, tools=req.get("tools", None)
+            )
+        except (AttributeError, AssertionError):
+            warnings.warn(
+                "Tokenizer does not support 'apply_chat_template'. Using tokenize instead."
+            )
+            prompt_tokens = tokenizer.tokenize(
+                "\n".join([message["content"] for message in messages])
             )
-        except AttributeError:
-            return (
-                "Tokenizer does not support 'apply_chat_template'. "
-                "Chat completions requires a tokenizer with a configured chat template."
-            ), 500
         except Exception as e:
+            logger.error(f"{traceback.format_exc()}")
             return f"Error processing 'messages': {e}", 500
 
         # --- 2. Parse Sampling Params ---
@@ -55,6 +63,17 @@ async def chat_completions():
             # Check for 'logprobs' (bool) and 'top_logprobs' (int)
             return_log_probs = bool(req.get("logprobs", False))
             top_n_logprobs = int(req.get("top_logprobs", 0)) if return_log_probs else 0
+            skip_prompt_log_probs = bool(req.get("skip_prompt_log_probs", True))
+            add_BOS = bool(req.get("add_BOS", False))
+
+            # The engine only handles add_BOS for string prompts, not pre-tokenized
+            # input. Since we pre-tokenize via apply_chat_template, we must handle
+            # BOS ourselves, matching the logic in tokenize_prompt().
+            if hasattr(tokenizer, 'bos') and tokenizer.bos is not None:
+                while prompt_tokens and prompt_tokens[0] == tokenizer.bos:
+                    prompt_tokens.pop(0)
+                if add_BOS:
+                    prompt_tokens = [tokenizer.bos] + prompt_tokens
 
             sampling_params = SamplingParams(
                 temperature=temperature,
@@ -62,7 +81,13 @@ async def chat_completions():
                 top_p=top_p,
                 return_log_probs=return_log_probs,
                 top_n_logprobs=top_n_logprobs,
-                num_tokens_to_generate=int(req.get("max_tokens", 16)),
+                num_tokens_to_generate=(
+                    int(max_tokens)
+                    if ((max_tokens := req.get("max_tokens", None)) is not None)
+                    else None
+                ),
+                skip_prompt_log_probs=skip_prompt_log_probs,
+                add_BOS=add_BOS,
             )
         except ValueError as e:
             return f"Invalid sampling parameter: {e}", 400
@@ -71,80 +96,138 @@ async def chat_completions():
         # For chat, we run the *same* prompt 'n' times.
         tasks = []
         for _ in range(n):
-            per_req_params = SamplingParams(
-                temperature=sampling_params.temperature,
-                top_k=sampling_params.top_k,
-                top_p=sampling_params.top_p,
-                return_log_probs=sampling_params.return_log_probs,
-                top_n_logprobs=sampling_params.top_n_logprobs,
-                num_tokens_to_generate=sampling_params.num_tokens_to_generate,
-            )
-            tasks.append(client.add_request(prompt_tokens, per_req_params))
+            tasks.append(client.add_request(prompt_tokens, sampling_params))
+
+        if current_app.config['verbose']:
+            start_time = time.perf_counter()
 
-        start_time = time.perf_counter()
         try:
             batch_results = await asyncio.gather(*tasks)
         except Exception as e:
+            logger.error(f"Error during inference: {e}")
             return f"Error during inference: {e}", 500
 
-        logger.info(
-            f"Batch of {len(tasks)} requests (n={n}) processed in "
-            f"{time.perf_counter() - start_time:.2f}s"
-        )
+        if current_app.config['verbose']:
+            logging.info(
+                f"Batch of {len(tasks)} requests (n={n}) processed in "
+                f"{time.perf_counter() - start_time:.2f}s"
+            )
 
         # --- 4. Format OpenAI Response ---
         choices = []
         total_completion_tokens = 0
-        prompt_token_count = len(prompt_tokens)  # Calculated once
+        prompt_tokens_counts = []
 
         request_idx = 0
         for record in batch_results:
-            for result in record.requests:
-                text_output = result.generated_text
-
-                logprobs_content = None
-                if sampling_params.return_log_probs:
-                    token_logprobs = getattr(result, 'log_probs', [])
-                    tokens = [tokenizer.detokenize([tok]) for tok in result.generated_tokens]
-
-                    # Get top_n_logprobs if available
-                    generated_top_n_logprobs = getattr(result, 'generated_top_n_logprobs', None)
-
-                    logprobs_content = []
-                    for i, (tok, lp) in enumerate(zip(tokens, token_logprobs)):
-                        # Build top_logprobs list for this token position
-                        top_logprobs_list = []
-                        if generated_top_n_logprobs and i < len(generated_top_n_logprobs):
-                            top_n_dict = generated_top_n_logprobs[i]
-                            for token_str, logprob in top_n_dict.items():
-                                top_logprobs_list.append(
-                                    {
-                                        "token": token_str,
-                                        "logprob": logprob,
-                                        "bytes": list(token_str.encode("utf-8")),
-                                    }
-                                )
-
-                        entry = {
-                            "token": tok,
-                            "logprob": lp,
-                            "bytes": list(tok.encode("utf-8")),
-                            "top_logprobs": top_logprobs_list,
-                        }
-                        logprobs_content.append(entry)
-
-                choice_data = {
-                    "index": 0,
-                    "message": {"role": "assistant", "content": text_output},
-                    # 'logprobs' in chat API is an object containing 'content'
-                    "logprobs": {"content": logprobs_content} if logprobs_content else None,
-                    "finish_reason": "length",  # Original code hardcoded this.
-                }
-                choices.append(choice_data)
-                total_completion_tokens += len(result.generated_tokens)
-                request_idx += 0
-
+            result = record.merge().serialize()
+            # Unwrap ("tensor", [...]) tuples from serialize() into plain lists.
+            result = {
+                k: v[1] if isinstance(v, (list, tuple)) and len(v) == 2 and v[0] == "tensor" else v
+                for k, v in result.items()
+            }
+            prompt_tokens = result["prompt_tokens"]  # The engine can modify prompt_tokens.
+            text_output = result["generated_text"]
+            prompt_tokens_count = len(prompt_tokens) if prompt_tokens is not None else 0
+            prompt_tokens_counts.append(prompt_tokens_count)
+
+            logprobs_content = None
+            if sampling_params.return_log_probs:
+                token_logprobs = result.get('log_probs', [])
+                tokens = [tokenizer.detokenize([tok]) for tok in result["generated_tokens"]]
+
+                # Get top_n_logprobs if available
+                generated_top_n_logprobs = result.get('generated_top_n_logprobs')
+
+                logprobs_content = []
+                for i, (tok, lp) in enumerate(zip(tokens, token_logprobs)):
+                    # Build top_logprobs list for this token position
+                    top_logprobs_list = []
+                    if generated_top_n_logprobs and i < len(generated_top_n_logprobs):
+                        top_n_dict = generated_top_n_logprobs[i]
+                        for token_str, logprob in top_n_dict.items():
+                            top_logprobs_list.append(
+                                {
+                                    "token": token_str,
+                                    "logprob": logprob,
+                                    "bytes": list(token_str.encode("utf-8")),
+                                }
+                            )
+
+                    entry = {
+                        "token": tok,
+                        "logprob": lp,
+                        "bytes": list(tok.encode("utf-8")),
+                        "top_logprobs": top_logprobs_list,
+                    }
+                    logprobs_content.append(entry)
+
+            metadata = {}
+            message_text = text_output
+            if parsers:
+                for parser in parsers:
+                    if parser not in PARSER_MAPPING:
+                        raise ValueError(f"Parser {parser} not found in PARSER_MAPPING")
+                    message_text, new_info = PARSER_MAPPING[parser].parse(
+                        message_text, tools=req.get("tools", None)
+                    )
+                    assert not (
+                        metadata.keys() & new_info.keys()
+                    ), "Multiple parsers found the same information."
+                    metadata.update(new_info)
+            message = {"role": "assistant", "content": message_text}
+            if "tool_calls" in metadata:
+                message["tool_calls"] = metadata["tool_calls"]
+            if "reasoning" in metadata:
+                message["reasoning"] = metadata["reasoning"]
+
+            # Replicate data in the message field for compatibility.
+            message["prompt_token_ids"] = result["prompt_tokens"]
+            message["generation_token_ids"] = result["generated_tokens"]
+            message["generation_log_probs"] = result.get("generated_log_probs", None)
+            return_log_probs = sampling_params.return_log_probs
+
+            choice_data = {
+                "index": request_idx,
+                "message": message,
+                "prompt_token_ids": result["prompt_tokens"],
+                "generation_token_ids": result["generated_tokens"],
+                "generation_log_probs": result["generated_log_probs"],
+                "raw_text": result["prompt"] + result["generated_text"],
+                # 'logprobs' in chat API is an object containing 'content'
+                # "logprobs": {"content": logprobs_content} if logprobs_content else None,
+                "logprobs": {"content": logprobs_content} if return_log_probs else None,
+                "finish_reason": (
+                    "tool_calls" if metadata.get("tool_calls", []) else "stop"
+                ),  # Original code hardcoded this.
+            }
+            if result.get("policy_staleness") is not None:
+                choice_data["policy_staleness"] = result["policy_staleness"]
+            if result.get("kv_cache_staleness") is not None:
+                choice_data["kv_cache_staleness"] = result["kv_cache_staleness"]
+            events = result.get("events")
+            if events is not None:
+                num_evictions = sum(1 for e in events if e.get("type") == "EVICT")
+                if num_evictions > 0:
+                    choice_data["num_evictions"] = num_evictions
+            if current_app.config['verbose']:
+                logging.info(result)
+            if result["routing_indices"] is not None:
+                choice_data["moe_topk_indices"] = result["routing_indices"]
+                if prompt_tokens_count:
+                    choice_data["prompt_moe_topk_indices"] = result["routing_indices"][
+                        :prompt_tokens_count
+                    ]
+            choices.append(choice_data)
+            total_completion_tokens += len(result["generated_tokens"])
+            request_idx += 1
+
+        prompt_token_count = max(prompt_tokens_counts)
         response = {
+            "id": str(uuid.uuid4()),
+            "created": int(time.time()),
+            "model": "EMPTY",
+            "object": "chat.completion",
             "choices": choices,
             "usage": {
                 "prompt_tokens": prompt_token_count,
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
index b749205cdfd..fdab97eb7b5 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/endpoints/completions.py
@@ -110,103 +110,109 @@ async def completions():
             )
             tasks.append(client.add_request(prompt_tokens, per_req_params))
 
-        start_time = time.perf_counter()
+        if current_app.config['verbose']:
+            start_time = time.perf_counter()
+
         try:
             batch_results = await asyncio.gather(*tasks)
         except Exception as e:
             return f"Error during inference: {e}", 500
 
-        logger.info(
-            f"Batch of {len(tasks)} requests processed in {time.perf_counter() - start_time:.2f}s"
-        )
+        if current_app.config['verbose']:
+            logging.info(
+                f"Batch of {len(tasks)} requests processed in "
+                f"{time.perf_counter() - start_time:.2f}s"
+            )
 
         # --- 4. Format Response (matching old_completions.py) ---
         choices = []
 
         request_idx = 0
         for record in batch_results:
-            for result in record.requests:
-                full_text = result.generated_text or ""
-                text_output = (prompts_as_strings[request_idx] + full_text) if echo else full_text
-
-                logprobs_data = None
-                if sampling_params.return_log_probs:
-                    # Get prompt tokens and logprobs
-                    prompt_tokens_list = []
-                    if result.prompt_tokens is not None:
-                        if hasattr(result.prompt_tokens, 'tolist'):
-                            prompt_tokens_list = result.prompt_tokens.tolist()
-                        else:
-                            prompt_tokens_list = list(result.prompt_tokens)
-
-                    prompt_log_probs = getattr(result, 'prompt_log_probs', None) or []
-                    prompt_top_n_logprobs = getattr(result, 'prompt_top_n_logprobs', None) or []
-
-                    # Get generated tokens and logprobs
-                    generated_tokens_list = (
-                        list(result.generated_tokens) if result.generated_tokens else []
-                    )
-                    generated_log_probs = getattr(result, 'generated_log_probs', None) or []
-                    generated_top_n_logprobs = (
-                        getattr(result, 'generated_top_n_logprobs', None) or []
-                    )
-
-                    if echo:
-                        # When echo=True, include prompt tokens and their logprobs
-                        # Prompt logprobs are for tokens [1:] (first token has no logprob)
-                        all_token_ids = prompt_tokens_list + generated_tokens_list
-                        tokens = [tokenizer.detokenize([tok]) for tok in all_token_ids]
-
-                        # Build token_logprobs: [None] for first token, then prompt logprobs,
-                        # then generated logprobs
-                        token_logprobs = [None] + list(prompt_log_probs) + list(generated_log_probs)
-
-                        # Build top_logprobs: [None] for first token, then prompt top_n,
-                        # then generated top_n
-                        top_logprobs = None
-                        if prompt_top_n_logprobs or generated_top_n_logprobs:
-                            top_logprobs = (
-                                [None]
-                                + list(prompt_top_n_logprobs)
-                                + list(generated_top_n_logprobs)
-                            )
-
-                        # Calculate text_offset: cumulative character positions starting from 0
-                        text_offset = []
-                        current_offset = 0
-                        for tok_str in tokens:
-                            text_offset.append(current_offset)
-                            current_offset += len(tok_str)
+            result = record.merge()
+            full_text = result.generated_text or ""
+            text_output = (prompts_as_strings[request_idx] + full_text) if echo else full_text
+
+            logprobs_data = None
+            if sampling_params.return_log_probs:
+                # Get prompt tokens and logprobs
+                prompt_tokens_list = []
+                if result.prompt_tokens is not None:
+                    if hasattr(result.prompt_tokens, 'tolist'):
+                        prompt_tokens_list = result.prompt_tokens.tolist()
                     else:
-                        # When echo=False, only return generated tokens and their logprobs
-                        tokens = [tokenizer.detokenize([tok]) for tok in generated_tokens_list]
-
-                        # Prepend [None] to match OpenAI format
-                        token_logprobs = [None] + list(generated_log_probs)
-
-                        # Build top_logprobs
-                        top_logprobs = None
-                        if generated_top_n_logprobs:
-                            top_logprobs = [None] + list(generated_top_n_logprobs)
-
-                        # Calculate text_offset for generated tokens only
-                        text_offset = []
-                        current_offset = 0
-                        for tok_str in tokens:
-                            text_offset.append(current_offset)
-                            current_offset += len(tok_str)
-
-                    logprobs_data = {
-                        "token_logprobs": token_logprobs,
-                        "tokens": tokens,
-                        "text_offset": text_offset,
-                        "top_logprobs": top_logprobs,
-                    }
-
-                choices.append(
-                    {"index": request_idx, "text": text_output, "logprobs": logprobs_data}
+                        prompt_tokens_list = list(result.prompt_tokens)
+
+                prompt_log_probs = getattr(result, 'prompt_log_probs', None) or []
+                prompt_top_n_logprobs = getattr(result, 'prompt_top_n_logprobs', None) or []
+
+                # Get generated tokens and logprobs
+                generated_tokens_list = (
+                    list(result.generated_tokens) if result.generated_tokens else []
                 )
-                request_idx += 1
+                generated_log_probs = getattr(result, 'generated_log_probs', None) or []
+                generated_top_n_logprobs = getattr(result, 'generated_top_n_logprobs', None) or []
+
+                if echo:
+                    # When echo=True, include prompt tokens and their logprobs
+                    # Prompt logprobs are for tokens [1:] (first token has no logprob)
+                    all_token_ids = prompt_tokens_list + generated_tokens_list
+                    tokens = [tokenizer.detokenize([tok]) for tok in all_token_ids]
+
+                    # Build token_logprobs: [None] for first token, then prompt logprobs,
+                    # then generated logprobs
+                    token_logprobs = [None] + list(prompt_log_probs) + list(generated_log_probs)
+
+                    # Build top_logprobs: [None] for first token, then prompt top_n,
+                    # then generated top_n
+                    top_logprobs = None
+                    if prompt_top_n_logprobs or generated_top_n_logprobs:
+                        top_logprobs = (
+                            [None] + list(prompt_top_n_logprobs) + list(generated_top_n_logprobs)
+                        )
+
+                    # Calculate text_offset: cumulative character positions starting from 0
+                    text_offset = []
+                    current_offset = 0
+                    for tok_str in tokens:
+                        text_offset.append(current_offset)
+                        current_offset += len(tok_str)
+                else:
+                    # When echo=False, only return generated tokens and their logprobs
+                    tokens = [tokenizer.detokenize([tok]) for tok in generated_tokens_list]
+
+                    # Prepend [None] to match OpenAI format
+                    token_logprobs = [None] + list(generated_log_probs)
+
+                    # Build top_logprobs
+                    top_logprobs = None
+                    if generated_top_n_logprobs:
+                        top_logprobs = [None] + list(generated_top_n_logprobs)
+
+                    # Calculate text_offset for generated tokens only
+                    text_offset = []
+                    current_offset = 0
+                    for tok_str in tokens:
+                        text_offset.append(current_offset)
+                        current_offset += len(tok_str)
+
+                logprobs_data = {
+                    "token_logprobs": token_logprobs,
+                    "tokens": tokens,
+                    "text_offset": text_offset,
+                    "top_logprobs": top_logprobs,
+                }
+
+            choices.append({"index": request_idx, "text": text_output, "logprobs": logprobs_data})
+            if result.routing_indices is not None:
+                choices[-1]["moe_topk_indices"] = result.routing_indices.tolist()
+                prompt_length = len(result.prompt_tokens) if result.prompt_tokens is not None else 0
+                if prompt_length:
+                    choices[-1]["prompt_moe_topk_indices"] = result.routing_indices[
+                        :prompt_length
+                    ].tolist()
+
+            request_idx += 1
 
         return jsonify({"choices": choices})
 
diff --git a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py
index 2b0469b340a..73b9684ad48 100644
--- a/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py
+++ b/megatron/core/inference/text_generation_server/dynamic_text_gen_server/flask_server.py
@@ -1,13 +1,16 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
+import asyncio
 import logging
 import socket
+from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
 
 try:
     from flask import Flask
     from hypercorn.asyncio import serve
     from hypercorn.config import Config
+    from hypercorn.middleware import AsyncioWSGIMiddleware
 
     HAS_FLASK = True
 except ImportError as e:
@@ -33,8 +36,14 @@ def temp_log_level(level, logger=None):
 
 
 @trace_async_exceptions
-async def run_flask_server(coordinator_port: int, tokenizer, rank: int, flask_port: int):
-    """Initializes and runs the async Flask server."""
+async def run_flask_server_on_client(
+    client: InferenceClient,
+    tokenizer,
+    flask_port: int,
+    parsers: list[str] = None,
+    verbose: bool = False,
+):
+    """Initializes and runs the async Flask server using the provided InferenceClient."""
     if not HAS_FLASK:
         raise RuntimeError(f"Flask not available")
 
@@ -44,15 +53,13 @@ async def run_flask_server(coordinator_port: int, tokenizer, rank: int, flask_po
         logger.warning(f"Could not get hostname: {e}")
         hostname = "0.0.0.0"
 
-    inference_client = InferenceClient(coordinator_port)
-    await inference_client.start()
-    logger.info(f"Rank {rank}: InferenceClient connected.")
-
     app = Flask(__name__)
 
     # Store client and tokenizer in app config for Blueprints to use
-    app.config['client'] = inference_client
+    app.config['client'] = client
     app.config['tokenizer'] = tokenizer
+    app.config['parsers'] = parsers
+    app.config['verbose'] = verbose
 
     # Register all blueprints from the 'endpoints' package
     for endpoint in endpoints.__all__:
@@ -62,15 +69,41 @@ async def run_flask_server(coordinator_port: int, tokenizer, rank: int, flask_po
     def health_check():
         return "Megatron Dynamic Inference Server is running."
 
+    loop = asyncio.get_event_loop()
+
     config = Config()
+    config.wsgi_max_body_size = 2**30  # 1 GB; needed for large prompts.
+    config.keep_alive_timeout = 30.0  # Keep connection alive between long-running requests.
+    config.backlog = 2**14  # Expect high load; ensure we do not drop connections.
+    config.h2_max_concurrent_streams = 2**14  # Allow many concurrent streams for HTTP/2 clients.
     config.bind = [f"0.0.0.0:{flask_port}"]
 
     # Force logging level to INFO to ensure that hostname is printed
     with temp_log_level(logging.INFO, logger):
         logger.info(f"Starting Flask server on http://{hostname}:{flask_port}")
+        logger.info(f"Using tokenizer: {type(tokenizer)}")
+        logger.info(f"Using parsers: {parsers}")
+
+    loop.set_default_executor(ThreadPoolExecutor(max_workers=8192))
+    await serve(AsyncioWSGIMiddleware(app, max_body_size=config.wsgi_max_body_size), config)
 
+
+@trace_async_exceptions
+async def run_flask_server(
+    coordinator_addr: str,
+    tokenizer,
+    rank: int,
+    flask_port: int,
+    parsers: list[str] = None,
+    verbose: bool = False,
+):
+    """Initializes and runs the async Flask server
+    starting an InferenceClient with the provided coordinator address."""
+    inference_client = InferenceClient(coordinator_addr)
+    await inference_client.start()
+    logger.info(f"Rank {rank}: InferenceClient connected.")
     try:
-        await serve(app, config)
+        await run_flask_server_on_client(inference_client, tokenizer, flask_port, parsers, verbose)
     finally:
         await inference_client.stop()
         logger.info(f"Rank {rank}: Flask server and client shut down.")
diff --git a/megatron/core/inference/text_generation_server/text_generation_server.py b/megatron/core/inference/text_generation_server/text_generation_server.py
index ec60a52d9a4..4d11a27c405 100644
--- a/megatron/core/inference/text_generation_server/text_generation_server.py
+++ b/megatron/core/inference/text_generation_server/text_generation_server.py
@@ -30,6 +30,7 @@ class MegatronGenerate(Resource):
     def __init__(self, engine, args):
         self.engine = engine
         self.args = args
+        self.verbose = getattr(args, 'inference_flask_server_logging', False)
 
     def put(self):
         """Handle generation request."""
@@ -156,12 +157,6 @@ def put(self):
             if random_seed < 0:
                 return "random_seed must be a positive integer"
 
-        no_log = False
-        if "no_log" in request.get_json():
-            no_log = request.get_json()["no_log"]
-            if not isinstance(no_log, bool):
-                return "no_log must be a boolean value"
-
         stop_token = 50256
         if "stop_token" in request.get_json():
             stop_token = request.get_json()["stop_token"]
@@ -176,7 +171,7 @@ def put(self):
 
         with LOCK:  # Need to get lock to keep multiple threads from hitting code
 
-            if not no_log:
+            if self.verbose:
                 logging.info(f"request IP: {str(request.remote_addr)}")
                 logging.info(json.dumps(request.get_json()))
                 logging.info(f"start time: {datetime.datetime.now()}")
diff --git a/megatron/core/inference/unified_memory.py b/megatron/core/inference/unified_memory.py
index 6b58e845812..53d8d862b92 100644
--- a/megatron/core/inference/unified_memory.py
+++ b/megatron/core/inference/unified_memory.py
@@ -69,6 +69,12 @@ def _handler(signum, frame):
             "Please clean up your stale cache and try again."
         )
 
+    # Signal-based timeout only works in the main thread.
+    # In non-main threads (e.g., Ray actors), skip the timeout mechanism.
+    if threading.current_thread() is not threading.main_thread():
+        yield
+        return
+
     curr_handler = signal.signal(signal.SIGALRM, _handler)
     try:
         signal.alarm(timeout_s)
diff --git a/megatron/core/model_parallel_config.py b/megatron/core/model_parallel_config.py
index e30cb0e1d1a..5bbeef9b022 100644
--- a/megatron/core/model_parallel_config.py
+++ b/megatron/core/model_parallel_config.py
@@ -80,11 +80,6 @@ class ModelParallelConfig:
        Default is None, which will be set to the value of tensor_model_parallel_size.
     """
 
-    moe_extended_tp: bool = False
-    """NOTE: Deprecated from MCore v0.10. This flag is ignored.
-      Its functionality is replaced by expert_tensor_parallel_size.
-    """
-
     ###################
     # Initialization
     ###################
@@ -176,9 +171,6 @@ class ModelParallelConfig:
        must turn off gradient accumulation fusion.
     """
 
-    async_tensor_model_parallel_allreduce: bool = True
-    """NOTE: Deprecated. This flag is ignored."""
-
     use_te_rng_tracker: bool = field(
         default=False, metadata={"argparse_meta": {"arg_names": ["--te-rng-tracker"]}}
     )
diff --git a/megatron/core/models/T5/t5_spec.py b/megatron/core/models/T5/t5_spec.py
index 50aecf0a950..4d437eb065d 100644
--- a/megatron/core/models/T5/t5_spec.py
+++ b/megatron/core/models/T5/t5_spec.py
@@ -76,7 +76,8 @@ def encoder_model_with_transformer_engine_default_spec() -> ModuleSpec:
             mlp=ModuleSpec(
                 module=MLP,
                 submodules=MLPSubmodules(
-                    linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
+                    linear_fc1=not_none(TELayerNormColumnParallelLinear),
+                    linear_fc2=not_none(TERowParallelLinear),
                 ),
             ),
             mlp_bda=get_bias_dropout_add,
@@ -102,7 +103,7 @@ def decoder_model_with_transformer_engine_default_spec() -> ModuleSpec:
                 ),
             ),
             self_attn_bda=get_bias_dropout_add,
-            pre_cross_attn_layernorm=TENorm,
+            pre_cross_attn_layernorm=not_none(TENorm),
             cross_attention=ModuleSpec(
                 module=CrossAttention,
                 params={"attn_mask_type": AttnMaskType.padding},
@@ -117,7 +118,8 @@ def decoder_model_with_transformer_engine_default_spec() -> ModuleSpec:
             mlp=ModuleSpec(
                 module=MLP,
                 submodules=MLPSubmodules(
-                    linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
+                    linear_fc1=not_none(TELayerNormColumnParallelLinear),
+                    linear_fc2=not_none(TERowParallelLinear),
                 ),
             ),
             mlp_bda=get_bias_dropout_add,
diff --git a/megatron/core/models/backends.py b/megatron/core/models/backends.py
index 7f84599a04c..a4fc3165ba5 100644
--- a/megatron/core/models/backends.py
+++ b/megatron/core/models/backends.py
@@ -1,14 +1,16 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+from __future__ import annotations
 
 import warnings
 from abc import abstractmethod
-from typing import Optional, Protocol, Tuple
+from typing import Optional, Protocol, cast
 
 from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from megatron.core.transformer.dot_product_attention import DotProductAttention
-from megatron.core.transformer.mlp import MLPSubmodules
-from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP
-from megatron.core.transformer.torch_norm import WrappedTorchNorm
+from megatron.core.transformer.mlp import MLPSubmodules, TEActivationFunctionBuilder
+from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLPSubmodules
+from megatron.core.transformer.torch_norm import LayerNormBuilder, WrappedTorchNorm
+from megatron.core.typed_torch import not_none
 
 try:
     import apex  # pylint: disable=unused-import
@@ -19,8 +21,9 @@
     LNImpl = FusedLayerNorm
 except ImportError:
     warnings.warn("Apex is not installed. Falling back to Torch Norm")
-    LNImpl = WrappedTorchNorm
+    FusedLayerNorm = None
     HAVE_APEX = False
+    LNImpl = WrappedTorchNorm
 
 from megatron.core.extensions.transformer_engine import (
     TEActivationOp,
@@ -60,7 +63,7 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         ...
 
     @abstractmethod
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> type:
+    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
         """Which module for layernorm"""
         ...
 
@@ -72,12 +75,12 @@ def core_attention(self) -> type:
     @abstractmethod
     def grouped_mlp_modules(
         self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> Tuple[type, Optional[MLPSubmodules]]:
+    ) -> tuple[type, MLPSubmodules | TEGroupedMLPSubmodules | None]:
         """Which module and submodules to use for grouped mlp"""
         ...
 
     @abstractmethod
-    def activation_func(self) -> type:
+    def activation_func(self) -> TEActivationFunctionBuilder | None:
         """Which module to use for activation function"""
         ...
 
@@ -101,7 +104,7 @@ def column_parallel_layer_norm_linear(self) -> Optional[type]:
         """Which module for sequential layernorm and linear"""
         return None
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> type:
+    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if rms_norm:
             # Matching get_gpt_layer_local_spec.
@@ -116,7 +119,7 @@ def core_attention(self) -> type:
 
     def grouped_mlp_modules(
         self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> Tuple[type, Optional[MLPSubmodules]]:
+    ) -> tuple[type[GroupedMLP], None] | tuple[type[SequentialMLP], MLPSubmodules]:
         """Which module and submodules to use for grouped mlp"""
         if moe_use_grouped_gemm:
             warnings.warn(
@@ -129,7 +132,7 @@ def grouped_mlp_modules(
                 linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear
             )
 
-    def activation_func(self) -> type:
+    def activation_func(self) -> TEActivationFunctionBuilder | None:
         """Which module to use for activation function"""
         return None
 
@@ -157,26 +160,28 @@ def column_parallel_layer_norm_linear(self) -> type[InferenceLayerNormColumnPara
         """Which module for sequential layernorm and linear"""
         return InferenceLayerNormColumnParallelLinear
 
-    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> type:
+    def layer_norm(self, rms_norm: bool = False, for_qk: bool = False) -> LayerNormBuilder:
         """Which module to use for layer norm"""
         if for_qk and not is_te_min_version("1.9.0"):
             # TENorm significantly harms convergence when used
             # for QKLayerNorm if TE Version < 1.9;
             # we instead use the Apex implementation.
-            return FusedLayerNorm
+            return not_none(FusedLayerNorm)
         return TENorm
 
     def core_attention(self) -> type[TEDotProductAttention]:
         """Which module to use for attention"""
         return TEDotProductAttention
 
-    def activation_func(self) -> type:
+    def activation_func(self) -> TEActivationFunctionBuilder | None:
         """Which module to use for activation function"""
-        return TEActivationOp
+        # transformer_engine.BasicOperation.forward has an overly permissive return type, but by
+        # design these classes always meet the interface.
+        return cast(TEActivationFunctionBuilder, TEActivationOp)
 
     def grouped_mlp_modules(
         self, moe_use_grouped_gemm: bool, moe_use_legacy_grouped_gemm: bool
-    ) -> Tuple[type, Optional[MLPSubmodules]]:
+    ) -> tuple[type, MLPSubmodules | TEGroupedMLPSubmodules | None]:
         raise NotImplementedError(
             "MOE is not supported with inference optimized transformer implementation."
         )
diff --git a/megatron/core/models/bert/bert_layer_specs.py b/megatron/core/models/bert/bert_layer_specs.py
index 8415ef02cc5..de47f24d046 100644
--- a/megatron/core/models/bert/bert_layer_specs.py
+++ b/megatron/core/models/bert/bert_layer_specs.py
@@ -45,40 +45,50 @@
     HAVE_APEX = False
 
 
-def get_bert_layer_with_transformer_engine_spec():
-    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).
+def get_bert_layer_with_transformer_engine_submodules() -> TransformerLayerSubmodules:
+    """Use these submodules to use lower-level Transformer Engine modules (required for fp8
+    training).
 
     Returns:
-        ModuleSpec: Module specification with TE modules
+        TransformerLayerSubmodules: Submodules with TE modules.
     """
     if not HAVE_TE:
         raise ImportError(
             "Transformer Engine is not installed. Please use local Bert layer spec instead."
         )
 
-    return ModuleSpec(
-        module=TransformerLayer,
-        submodules=TransformerLayerSubmodules(
-            self_attention=ModuleSpec(
-                module=SelfAttention,
-                params={"attn_mask_type": AttnMaskType.padding},
-                submodules=SelfAttentionSubmodules(
-                    linear_qkv=not_none(TELayerNormColumnParallelLinear),
-                    core_attention=not_none(TEDotProductAttention),
-                    linear_proj=TERowParallelLinear,
-                    q_layernorm=IdentityOp,
-                    k_layernorm=IdentityOp,
-                ),
+    return TransformerLayerSubmodules(
+        self_attention=ModuleSpec(
+            module=SelfAttention,
+            params={"attn_mask_type": AttnMaskType.padding},
+            submodules=SelfAttentionSubmodules(
+                linear_qkv=not_none(TELayerNormColumnParallelLinear),
+                core_attention=not_none(TEDotProductAttention),
+                linear_proj=not_none(TERowParallelLinear),
+                q_layernorm=IdentityOp,
+                k_layernorm=IdentityOp,
             ),
-            self_attn_bda=get_bias_dropout_add,
-            mlp=ModuleSpec(
-                module=MLP,
-                submodules=MLPSubmodules(
-                    linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear
-                ),
+        ),
+        self_attn_bda=get_bias_dropout_add,
+        mlp=ModuleSpec(
+            module=MLP,
+            submodules=MLPSubmodules(
+                linear_fc1=not_none(TELayerNormColumnParallelLinear),
+                linear_fc2=not_none(TERowParallelLinear),
             ),
-            mlp_bda=get_bias_dropout_add,
         ),
+        mlp_bda=get_bias_dropout_add,
+    )
+
+
+def get_bert_layer_with_transformer_engine_spec():
+    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).
+
+    Returns:
+        ModuleSpec: Module specification with TE modules
+    """
+    return ModuleSpec(
+        module=TransformerLayer, submodules=get_bert_layer_with_transformer_engine_submodules()
     )
 
 
diff --git a/megatron/core/models/bert/bert_model.py b/megatron/core/models/bert/bert_model.py
index abe9bc1c9b7..70760e7f87f 100644
--- a/megatron/core/models/bert/bert_model.py
+++ b/megatron/core/models/bert/bert_model.py
@@ -15,6 +15,7 @@
 from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding
 from megatron.core.models.common.language_module.language_module import LanguageModule
 from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.transformer.attention import SelfAttentionSubmodules
 from megatron.core.transformer.dot_product_attention import (
     DotProductAttention as MCoreDotProductAttention,
 )
@@ -22,16 +23,9 @@
 from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_block import TransformerBlock
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
 from megatron.core.transformer.utils import get_linear_layer
-from megatron.core.utils import deprecate_inference_params
-from megatron.core.utils import get_te_version as _get_te_version
-from megatron.core.utils import is_te_min_version
-
-
-def get_te_version():
-    """Included for backwards compatibility."""
-    warnings.warn("`get_te_version` will be deprecated in a future release")
-    return _get_te_version()
+from megatron.core.utils import deprecate_inference_params, is_te_min_version
 
 
 class BertModel(LanguageModule):
@@ -187,6 +181,11 @@ def _sanity_check_attention_and_get_attn_mask_dimension(self) -> str:
         """
         attention_backend = self.config.attention_backend
         attn_mask_dimensions = None
+        assert isinstance(self.transformer_layer_spec.submodules, TransformerLayerSubmodules)
+        assert isinstance(
+            self.transformer_layer_spec.submodules.self_attention.submodules,
+            SelfAttentionSubmodules,
+        )
         # For local layer spec we just use b1ss
         if (
             self.transformer_layer_spec.submodules.self_attention.submodules.core_attention
diff --git a/megatron/core/models/common/language_module/language_module.py b/megatron/core/models/common/language_module/language_module.py
index 259bb716a93..a290a61aa4c 100644
--- a/megatron/core/models/common/language_module/language_module.py
+++ b/megatron/core/models/common/language_module/language_module.py
@@ -23,6 +23,7 @@
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer.enums import AttnBackend, CudaGraphScope
 from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.multi_token_prediction import tie_word_embeddings_state_dict
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.utils import ensure_metadata_has_dp_cp_group
 from megatron.core.utils import (
@@ -239,7 +240,7 @@ def setup_embeddings_and_output_layer(self) -> None:
         # Ensure that first and last stages have the same initial parameter
         # values.
         if torch.distributed.is_initialized():
-            if self._is_in_embd_group():
+            if self._is_in_embd_group() and not self.config.init_model_with_meta_device:
                 weight = self.shared_embedding_or_output_weight()
                 weight.data = weight.data.cuda()
                 torch.distributed.all_reduce(weight.data, group=self.embd_group)
@@ -255,12 +256,20 @@ def setup_embeddings_and_output_layer(self) -> None:
             LanguageModule.embedding_warning_printed = True
 
     def shared_embedding_or_output_weight(self) -> Tensor:
-        """Gets the emedding weight or output logit weights when share embedding and output weights set to True.
+        """Gets the embedding weight or output logit weights when share embedding and output weights set to True
+          or when use Multi-Token Prediction (MTP).
 
         Returns:
-            Tensor: During pre processing it returns the input embeddings weight while during post processing it returns the final output layers weight
+            Tensor: During pre processing or MTP process it returns the input embeddings weight while during post processing it returns the final output layers weight
         """
-        if self.pre_process:
+        if self.pre_process or getattr(self, 'mtp_process', False):
+            # Multi-Token Prediction (MTP) need both embedding layer and output layer.
+            # So there will be both embedding layer and output layer in the mtp process stage.
+            # When share_embeddings_and_output_weights is True, the embedding weight is the
+            # canonical shared weight and is passed to the output layer during forward.
+            assert hasattr(
+                self, 'embedding'
+            ), f"embedding is needed in this pipeline stage, but it is not initialized."
             return self.embedding.word_embeddings.weight
         elif self.post_process:
             return self.output_layer.weight
@@ -293,6 +302,21 @@ def sharded_state_dict(
         output_layer_weight_key = f'{prefix}output_layer.weight'
         output_layer_bias_key = f'{prefix}output_layer.bias'
 
+        # Multi-Token Prediction (MTP) needs embedding layer in mtp process stage.
+        # If MTP is not placed in the pre processing stage, we need to maintain a copy of
+        # embedding layer in the mtp process stage and tie it to the embedding in the pre
+        # processing stage.
+        # Note: MTP loss is computed at post_process stage, so the output_layer on mtp_process
+        # rank doesn't need special tying - it's not used for loss computation.
+        if getattr(self, 'mtp_process', False) and not self.pre_process:
+            emb_weight = self.embedding.word_embeddings.weight
+            tie_word_embeddings_state_dict(
+                sharded_state_dict,
+                emb_weight,
+                first_stage_word_emb_key,
+                tp_group=self.tp_group,
+                dp_cp_group=metadata['dp_cp_group'],
+            )
         if self.share_embeddings_and_output_weights:
             self.tie_embeddings_and_output_weights_state_dict(
                 sharded_state_dict, output_layer_weight_key, first_stage_word_emb_key, metadata
diff --git a/megatron/core/models/common/model_chunk_schedule_plan.py b/megatron/core/models/common/model_chunk_schedule_plan.py
index f451942ffc2..2e26e5fd1d3 100644
--- a/megatron/core/models/common/model_chunk_schedule_plan.py
+++ b/megatron/core/models/common/model_chunk_schedule_plan.py
@@ -123,7 +123,7 @@ def _build_callable_nodes(self, event, comp_stream, comm_stream, extra_args):
 
         # get flags for latter use
         is_mtp = isinstance(self.layer, MultiTokenPredictionLayer)
-        transformer_layer = self.layer.transformer_layer if is_mtp else self.layer
+        transformer_layer = self.layer.mtp_model_layer if is_mtp else self.layer
         is_moe = isinstance(transformer_layer.mlp, MoELayer)
         num_local_experts = transformer_layer.mlp.num_local_experts if is_moe else None
 
diff --git a/megatron/core/models/gpt/fine_grained_callables.py b/megatron/core/models/gpt/fine_grained_callables.py
index e77cfb71871..6658b6363ea 100644
--- a/megatron/core/models/gpt/fine_grained_callables.py
+++ b/megatron/core/models/gpt/fine_grained_callables.py
@@ -22,6 +22,7 @@
     get_mtp_layer_offset,
 )
 from megatron.core.transformer.transformer_layer import TransformerLayer, make_viewless_tensor
+from megatron.core.typed_torch import apply_module, copy_signature
 from megatron.core.utils import internal_api
 
 
@@ -481,13 +482,15 @@ def forward_func(
                         layer.offload_mlp_norm, hidden_states, "mlp_norm"
                     ) as hidden_states:
                         pre_mlp_layernorm_output = layer.pre_mlp_norm_checkpoint.checkpoint(
-                            layer.pre_mlp_layernorm, hidden_states
+                            apply_module(layer.pre_mlp_layernorm), hidden_states
                         )
                 else:
                     with off_interface(
                         layer.offload_mlp_norm, hidden_states, "mlp_norm"
                     ) as hidden_states:
-                        pre_mlp_layernorm_output = layer.pre_mlp_layernorm(hidden_states)
+                        pre_mlp_layernorm_output = apply_module(layer.pre_mlp_layernorm)(
+                            hidden_states
+                        )
 
                 shared_expert_output = layer.mlp.shared_experts_compute(pre_mlp_layernorm_output)
                 probs, routing_map = layer.mlp.route(pre_mlp_layernorm_output)
@@ -610,6 +613,7 @@ def submodule_combine_forward(node: ScheduleNode, output: torch.Tensor):
             output = make_viewless_tensor(inp=output, requires_grad=True, keep_graph=True)
         return output
 
+    @copy_signature(layer._forward_mlp, handle_first_dst_param='preserve')
     def mlp_wrapper(node: ScheduleNode, *args, **kwargs):
         """Wrapper for Dense forward."""
         return layer._forward_mlp(*args, **kwargs)
@@ -638,9 +642,9 @@ def build_mtp_layer_callables(layer):
     multi-token prediction layer nodes (attention, MLP, etc.)
     """
 
-    forward_funcs, backward_dw = build_transformer_layer_callables(layer.transformer_layer)
+    forward_funcs, backward_dw = build_transformer_layer_callables(layer.mtp_model_layer)
     attn_forward, dispatch_forward, mlp_forward, combine_forward, _ = forward_funcs
-    is_moe = isinstance(layer.transformer_layer.mlp, MoELayer)
+    is_moe = isinstance(layer.mtp_model_layer.mlp, MoELayer)
     assert is_moe, "MTP layer in a2a overlap only supports MoE layer for now."
 
     def submodule_mtp_attn_forward(node, hidden_states):
diff --git a/megatron/core/models/gpt/gpt_layer_specs.py b/megatron/core/models/gpt/gpt_layer_specs.py
index 5bb479ad3ea..c1723623691 100755
--- a/megatron/core/models/gpt/gpt_layer_specs.py
+++ b/megatron/core/models/gpt/gpt_layer_specs.py
@@ -1,5 +1,4 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-
 import warnings
 from typing import Optional, Union
 
@@ -37,6 +36,7 @@
     TransformerLayerSubmodules,
     get_transformer_layer_offset,
 )
+from megatron.core.typed_torch import copy_signature
 from megatron.core.utils import is_te_min_version
 
 try:
@@ -72,12 +72,12 @@
     HAVE_APEX = False
 
 
-def get_gpt_layer_with_inference_spec(
+def get_gpt_layer_with_inference_submodules(
     qk_layernorm: Optional[bool] = False,
     multi_latent_attention: Optional[bool] = False,
     qk_l2_norm: Optional[bool] = False,
-) -> ModuleSpec:
-    """Use this spec to use inference optimized linear layers.
+) -> TransformerLayerSubmodules:
+    """Use these submodules for inference optimized linear layers.
     Args:
         qk_layernorm (bool, optional): To use layernorm for queries/keys. Defaults to False.
         multi_latent_attention (bool, optional): To use MLA. Defaults to False.
@@ -107,68 +107,70 @@ def get_gpt_layer_with_inference_spec(
             if qk_layernorm
             else backend.column_parallel_linear()
         )
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                input_layernorm=backend.layer_norm(),
-                self_attention=ModuleSpec(
-                    module=MLASelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=MLASelfAttentionSubmodules(
-                        linear_q_proj=backend.column_parallel_linear(),
-                        linear_q_down_proj=backend.linear(),
-                        linear_q_up_proj=linear_q_up_proj,
-                        linear_kv_down_proj=backend.linear(),
-                        linear_kv_up_proj=linear_kv_up_proj,
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=IdentityOp,
-                        kv_layernorm=IdentityOp,
-                    ),
+        return TransformerLayerSubmodules(
+            input_layernorm=backend.layer_norm(),
+            self_attention=ModuleSpec(
+                module=MLASelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=MLASelfAttentionSubmodules(
+                    linear_q_proj=backend.column_parallel_linear(),
+                    linear_q_down_proj=backend.linear(),
+                    linear_q_up_proj=linear_q_up_proj,
+                    linear_kv_down_proj=backend.linear(),
+                    linear_kv_up_proj=linear_kv_up_proj,
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=IdentityOp,
+                    kv_layernorm=IdentityOp,
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=IdentityOp,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
             ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=IdentityOp,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
         )
     else:
         qk_norm = backend.layer_norm(for_qk=True)
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                self_attention=ModuleSpec(
-                    module=SelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=SelfAttentionSubmodules(
-                        linear_qkv=backend.column_parallel_layer_norm_linear(),
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
-                        k_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
+        return TransformerLayerSubmodules(
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=backend.column_parallel_layer_norm_linear(),
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
+                    ),
+                    k_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
                     ),
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=IdentityOp,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
-                sharded_state_dict_keys_map={
-                    "mlp.0.weight": "mlp.linear_fc1.layer_norm_weight",
-                    "mlp.0.bias": "mlp.linear_fc1.layer_norm_bias",
-                    "mlp.1.basic_ops.0.weight": "mlp.linear_fc1.weight",
-                    "mlp.1.basic_ops.1.bias": "mlp.linear_fc1.bias",
-                    "mlp.3.basic_ops.0.weight": "mlp.linear_fc2.weight",
-                    "mlp.3.basic_ops.1.bias": "mlp.linear_fc2.bias",
-                },
             ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=IdentityOp,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+            sharded_state_dict_keys_map={
+                "mlp.0.weight": "mlp.linear_fc1.layer_norm_weight",
+                "mlp.0.bias": "mlp.linear_fc1.layer_norm_bias",
+                "mlp.1.basic_ops.0.weight": "mlp.linear_fc1.weight",
+                "mlp.1.basic_ops.1.bias": "mlp.linear_fc1.bias",
+                "mlp.3.basic_ops.0.weight": "mlp.linear_fc2.weight",
+                "mlp.3.basic_ops.1.bias": "mlp.linear_fc2.bias",
+            },
         )
 
 
-def get_gpt_layer_with_transformer_engine_spec(
+@copy_signature(get_gpt_layer_with_inference_submodules)
+def get_gpt_layer_with_inference_spec(*args, **kwargs) -> ModuleSpec:
+    """Use this spec to use inference optimized linear layers."""
+    return ModuleSpec(
+        module=TransformerLayer, submodules=get_gpt_layer_with_inference_submodules(*args, **kwargs)
+    )
+
+
+def get_gpt_layer_with_transformer_engine_submodules(
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
     qk_layernorm: Optional[bool] = False,
@@ -182,8 +184,11 @@ def get_gpt_layer_with_transformer_engine_spec(
     fallback_to_eager_attn: bool = False,
     use_kitchen_attention: bool = False,
     kitchen_attention_backend: str = "sdpa",
-) -> ModuleSpec:
-    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training).
+    post_self_attn_layernorm: bool = False,
+    post_mlp_layernorm: bool = False,
+) -> TransformerLayerSubmodules:
+    """Use these submodules to use lower-level Transformer Engine modules (required for fp8
+    training).
 
 
     Args:
@@ -199,7 +204,7 @@ def get_gpt_layer_with_transformer_engine_spec(
                                           enable certain operation fusions. Defaults to False.
 
     Returns:
-        ModuleSpec: Module specification with TE modules
+        TransformerLayerSubmodules: TE modules to construct a TransformerLayer
 
     """
     if fp8 is not None:
@@ -243,68 +248,75 @@ def get_gpt_layer_with_transformer_engine_spec(
             if qk_layernorm
             else backend.column_parallel_linear()
         )
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                input_layernorm=backend.layer_norm(),
-                self_attention=ModuleSpec(
-                    module=MLASelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=MLASelfAttentionSubmodules(
-                        linear_q_proj=backend.column_parallel_linear(),
-                        linear_q_down_proj=backend.linear(),
-                        linear_q_up_proj=linear_q_up_proj,
-                        linear_kv_down_proj=backend.linear(),
-                        linear_kv_up_proj=linear_kv_up_proj,
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=IdentityOp,
-                        kv_layernorm=IdentityOp,
-                    ),
+        return TransformerLayerSubmodules(
+            input_layernorm=backend.layer_norm(),
+            self_attention=ModuleSpec(
+                module=MLASelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=MLASelfAttentionSubmodules(
+                    linear_q_proj=backend.column_parallel_linear(),
+                    linear_q_down_proj=backend.linear(),
+                    linear_q_up_proj=linear_q_up_proj,
+                    linear_kv_down_proj=backend.linear(),
+                    linear_kv_up_proj=linear_kv_up_proj,
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=IdentityOp,
+                    kv_layernorm=IdentityOp,
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
             ),
+            self_attn_bda=get_bias_dropout_add,
+            post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp,
+            pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+            post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp,
         )
     else:
         qk_norm = backend.layer_norm(for_qk=True)
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                self_attention=ModuleSpec(
-                    module=SelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=SelfAttentionSubmodules(
-                        linear_qkv=backend.column_parallel_layer_norm_linear(),
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
-                        k_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
+        return TransformerLayerSubmodules(
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=backend.column_parallel_layer_norm_linear(),
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
+                    ),
+                    k_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
                     ),
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
-                sharded_state_dict_keys_map={
-                    "mlp.0.weight": "mlp.linear_fc1.layer_norm_weight",
-                    "mlp.0.bias": "mlp.linear_fc1.layer_norm_bias",
-                    "mlp.1.basic_ops.0.weight": "mlp.linear_fc1.weight",
-                    "mlp.1.basic_ops.1.bias": "mlp.linear_fc1.bias",
-                    "mlp.3.basic_ops.0.weight": "mlp.linear_fc2.weight",
-                    "mlp.3.basic_ops.1.bias": "mlp.linear_fc2.bias",
-                },
             ),
+            self_attn_bda=get_bias_dropout_add,
+            post_self_attn_layernorm=TENorm if post_self_attn_layernorm else IdentityOp,
+            pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+            post_mlp_layernorm=TENorm if post_mlp_layernorm else IdentityOp,
+            sharded_state_dict_keys_map={
+                "mlp.0.weight": "mlp.linear_fc1.layer_norm_weight",
+                "mlp.0.bias": "mlp.linear_fc1.layer_norm_bias",
+                "mlp.1.basic_ops.0.weight": "mlp.linear_fc1.weight",
+                "mlp.1.basic_ops.1.bias": "mlp.linear_fc1.bias",
+                "mlp.3.basic_ops.0.weight": "mlp.linear_fc2.weight",
+                "mlp.3.basic_ops.1.bias": "mlp.linear_fc2.bias",
+            },
         )
 
 
-def get_gpt_layer_local_spec(
+@copy_signature(get_gpt_layer_with_transformer_engine_submodules)
+def get_gpt_layer_with_transformer_engine_spec(*args, **kwargs) -> ModuleSpec:
+    """Use this spec to use lower-level Transformer Engine modules (required for fp8 training)."""
+    return ModuleSpec(
+        module=TransformerLayer,
+        submodules=get_gpt_layer_with_transformer_engine_submodules(*args, **kwargs),
+    )
+
+
+def get_gpt_layer_local_submodules(
     num_experts: Optional[int] = None,
     moe_grouped_gemm: Optional[bool] = False,
     qk_layernorm: Optional[bool] = False,
@@ -316,8 +328,8 @@ def get_gpt_layer_local_spec(
     use_kitchen: bool = False,
     use_kitchen_attention: bool = False,
     kitchen_attention_backend: str = "sdpa",
-) -> ModuleSpec:
-    """Use this spec for an implementation using only modules in Megatron-Core.
+) -> TransformerLayerSubmodules:
+    """Use these submodules for an implementation using only modules in Megatron-Core.
 
 
     Args:
@@ -331,7 +343,7 @@ def get_gpt_layer_local_spec(
         qk_l2_norm (bool, optional): To use l2 norm for queries/keys. Defaults to False.
 
     Returns:
-        ModuleSpec: Module specification with Megatron-Core modules
+        TransformerLayerSubmodules: Megatron-Core modules to construct a TransformerLayer
     """
 
     if use_kitchen:
@@ -366,63 +378,65 @@ def get_gpt_layer_local_spec(
 
     if multi_latent_attention:
         assert qk_l2_norm is False, "qk_l2_norm is not supported with MLA."
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                input_layernorm=layer_norm,
-                self_attention=ModuleSpec(
-                    module=MLASelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=MLASelfAttentionSubmodules(
-                        linear_q_proj=backend.column_parallel_linear(),
-                        linear_q_down_proj=backend.column_parallel_linear(),
-                        linear_q_up_proj=backend.column_parallel_linear(),
-                        linear_kv_down_proj=backend.column_parallel_linear(),
-                        linear_kv_up_proj=backend.column_parallel_linear(),
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=qk_norm if qk_layernorm else IdentityOp,
-                        kv_layernorm=qk_norm if qk_layernorm else IdentityOp,
-                    ),
+        return TransformerLayerSubmodules(
+            input_layernorm=layer_norm,
+            self_attention=ModuleSpec(
+                module=MLASelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=MLASelfAttentionSubmodules(
+                    linear_q_proj=backend.column_parallel_linear(),
+                    linear_q_down_proj=backend.column_parallel_linear(),
+                    linear_q_up_proj=backend.column_parallel_linear(),
+                    linear_kv_down_proj=backend.column_parallel_linear(),
+                    linear_kv_up_proj=backend.column_parallel_linear(),
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=qk_norm if qk_layernorm else IdentityOp,
+                    kv_layernorm=qk_norm if qk_layernorm else IdentityOp,
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=layer_norm,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
             ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=layer_norm,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
         )
     else:
-        return ModuleSpec(
-            module=TransformerLayer,
-            submodules=TransformerLayerSubmodules(
-                input_layernorm=layer_norm,
-                self_attention=ModuleSpec(
-                    module=SelfAttention,
-                    params={"attn_mask_type": AttnMaskType.causal},
-                    submodules=SelfAttentionSubmodules(
-                        linear_qkv=backend.column_parallel_linear(),
-                        core_attention=backend.core_attention(),
-                        linear_proj=backend.row_parallel_linear(),
-                        q_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
-                        k_layernorm=(
-                            L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
-                        ),
+        return TransformerLayerSubmodules(
+            input_layernorm=layer_norm,
+            self_attention=ModuleSpec(
+                module=SelfAttention,
+                params={"attn_mask_type": AttnMaskType.causal},
+                submodules=SelfAttentionSubmodules(
+                    linear_qkv=backend.column_parallel_linear(),
+                    core_attention=backend.core_attention(),
+                    linear_proj=backend.row_parallel_linear(),
+                    q_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
+                    ),
+                    k_layernorm=(
+                        L2Norm if qk_l2_norm else (qk_norm if qk_layernorm else IdentityOp)
                     ),
                 ),
-                self_attn_bda=get_bias_dropout_add,
-                pre_mlp_layernorm=layer_norm,
-                mlp=mlp,
-                mlp_bda=get_bias_dropout_add,
-                sharded_state_dict_keys_map={
-                    "input_layernorm.": "self_attention.linear_qkv.layer_norm_",
-                    "pre_mlp_layernorm.": "mlp.linear_fc1.layer_norm_",
-                },
             ),
+            self_attn_bda=get_bias_dropout_add,
+            pre_mlp_layernorm=layer_norm,
+            mlp=mlp,
+            mlp_bda=get_bias_dropout_add,
+            sharded_state_dict_keys_map={
+                "input_layernorm.": "self_attention.linear_qkv.layer_norm_",
+                "pre_mlp_layernorm.": "mlp.linear_fc1.layer_norm_",
+            },
         )
 
 
+@copy_signature(get_gpt_layer_local_submodules)
+def get_gpt_layer_local_spec(*args, **kwargs) -> ModuleSpec:
+    """Use this spec for an implementation using only modules in Megatron-Core."""
+    return ModuleSpec(
+        module=TransformerLayer, submodules=get_gpt_layer_local_submodules(*args, **kwargs)
+    )
+
+
 def _get_mlp_module_spec(
     use_te: Optional[bool] = True,
     num_experts: Optional[int] = None,
@@ -696,7 +710,7 @@ def get_gpt_mtp_block_spec_for_backend(
         raise ValueError(f"Invalid spec: {spec}")
 
     mtp_layer_spec = get_mtp_layer_spec_for_backend(
-        transformer_layer_spec=transformer_layer_spec, backend=backend
+        mtp_model_layer_spec=transformer_layer_spec, backend=backend
     )
     mtp_num_layers = config.mtp_num_layers if config.mtp_num_layers else 0
     mtp_layer_specs = [mtp_layer_spec] * mtp_num_layers
diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py
index 5b31ddedf13..66f4dc129cb 100644
--- a/megatron/core/models/gpt/gpt_model.py
+++ b/megatron/core/models/gpt/gpt_model.py
@@ -6,7 +6,7 @@
 import torch
 from torch import Tensor
 
-from megatron.core import parallel_state, tensor_parallel
+from megatron.core import tensor_parallel
 from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
 from megatron.core.inference.contexts import BaseInferenceContext
@@ -27,11 +27,9 @@
 from megatron.core.transformer.enums import CudaGraphScope, ModelType
 from megatron.core.transformer.linear_cross_entropy import LinearCrossEntropyModule
 from megatron.core.transformer.multi_token_prediction import (
-    MTPLossAutoScaler,
-    MTPLossLoggingHelper,
     MultiTokenPredictionBlock,
-    roll_tensor,
-    tie_word_embeddings_state_dict,
+    mtp_on_this_rank,
+    process_mtp_loss,
 )
 from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_block import TransformerBlock
@@ -145,7 +143,9 @@ def __init__(
             self.rotary_base = rotary_base
         self.rotary_scaling = rope_scaling
         self.mtp_block_spec = mtp_block_spec
-        self.mtp_process = mtp_block_spec is not None
+        self.mtp_process = mtp_block_spec is not None and mtp_on_this_rank(
+            self.config, ignore_virtual=False, vp_stage=vp_stage
+        )
 
         self.fuse_linear_cross_entropy = (
             self.config.cross_entropy_loss_fusion
@@ -488,6 +488,7 @@ def forward(
         inference_params: Optional[BaseInferenceContext] = None,
         loss_mask: Optional[Tensor] = None,
         padding_mask: Optional[Tensor] = None,
+        mtp_kwargs: Optional[dict] = {},
     ) -> Tensor:
         """Forward function of the GPT Model This function passes the input tensors
         through the embedding layer, and then the decoder and finally into the post
@@ -560,6 +561,7 @@ def forward(
             runtime_gather_output=runtime_gather_output,
             extra_block_kwargs=extra_block_kwargs,
             inference_context=inference_context,
+            mtp_kwargs=mtp_kwargs,
         )
 
     def _postprocess(
@@ -581,6 +583,7 @@ def _postprocess(
         runtime_gather_output=None,
         extra_block_kwargs=None,
         inference_context=None,
+        mtp_kwargs={},
     ):
         """Postprocesses decoder hidden states to generate logits or compute loss.
 
@@ -595,7 +598,7 @@ def _postprocess(
         output_weight = None
         if self.share_embeddings_and_output_weights:
             output_weight = self.shared_embedding_or_output_weight()
-        if mtp_in_postprocess:
+        if mtp_in_postprocess and mtp_kwargs.get('mtp_labels', None) is not None:
             hidden_states = self.mtp(
                 input_ids=input_ids,
                 position_ids=position_ids,
@@ -614,70 +617,25 @@ def _postprocess(
         if not self.post_process:
             return hidden_states
 
-        if self.config.mtp_num_layers is not None:
-            mtp_labels = labels.clone()
-            hidden_states_list = torch.chunk(hidden_states, 1 + self.config.mtp_num_layers, dim=0)
-            hidden_states = hidden_states_list[0]
-            if loss_mask is None:
-                # if loss_mask is not provided, use all ones as loss_mask
-                loss_mask = torch.ones_like(mtp_labels)
-            for mtp_layer_number in range(self.config.mtp_num_layers):
-                # Calc loss for the current Multi-Token Prediction (MTP) layers.
-                mtp_labels, _ = roll_tensor(
-                    mtp_labels,
-                    shifts=-1,
-                    dims=-1,
-                    cp_group=self.cp_group,
-                    packed_seq_params=packed_seq_params,
-                )
-                loss_mask, num_tokens = roll_tensor(
-                    loss_mask,
-                    shifts=-1,
-                    dims=-1,
-                    cp_group=self.cp_group,
-                    packed_seq_params=packed_seq_params,
-                )
-
-                # Compute mtp loss without storing logits to save memory.
-                output_layer_kwargs = dict(
-                    input_=hidden_states_list[mtp_layer_number + 1],
-                    weight=output_weight,
-                    runtime_gather_output=runtime_gather_output,
-                )
-                if self.fuse_linear_cross_entropy:
-                    mtp_loss = self.output_layer(
-                        output_cross_entropy_loss=self.fuse_linear_cross_entropy,
-                        labels=mtp_labels,
-                        **output_layer_kwargs,
-                    )
-                else:
-                    mtp_logits, _ = self.output_layer(**output_layer_kwargs)
-                    mtp_loss = self.compute_language_model_loss(mtp_labels, mtp_logits)
-
-                mtp_loss = loss_mask * mtp_loss
-                if self.training:
-                    # TODO(shifangx): remove the use of parallel_state here
-                    # after moving loss logging to loss_func in pretrain_gpt.py
-                    MTPLossLoggingHelper.save_loss_to_tracker(
-                        torch.sum(mtp_loss) / num_tokens,
-                        mtp_layer_number,
-                        self.config.mtp_num_layers,
-                        avg_group=parallel_state.get_data_parallel_group(
-                            with_context_parallel=True
-                        ),
-                    )
-                mtp_loss_scale = self.config.mtp_loss_scaling_factor / self.config.mtp_num_layers
-                if self.config.calculate_per_token_loss:
-                    hidden_states = MTPLossAutoScaler.apply(
-                        hidden_states, mtp_loss_scale * mtp_loss
-                    )
-                else:
-                    hidden_states = MTPLossAutoScaler.apply(
-                        hidden_states, mtp_loss_scale * mtp_loss / num_tokens
-                    )
+        if self.config.mtp_num_layers:
+            mtp_labels = mtp_kwargs.get('mtp_labels', None) if mtp_kwargs else None
+            hidden_states = process_mtp_loss(
+                hidden_states=hidden_states,
+                labels=mtp_labels if mtp_labels is not None else labels,
+                loss_mask=loss_mask,
+                output_layer=self.output_layer,
+                output_weight=output_weight,
+                runtime_gather_output=runtime_gather_output,
+                is_training=self.training,
+                compute_language_model_loss=self.compute_language_model_loss,
+                config=self.config,
+                cp_group=self.pg_collection.cp,
+                packed_seq_params=packed_seq_params,
+                detach_output_weight=mtp_labels is not None,
+            )
         sequence_parallel_override = False
 
-        if in_inference_mode and inference_context.materialize_only_last_token_logits:
+        if in_inference_mode and inference_context.config.materialize_only_last_token_logits:
             if inference_context.is_static_batching():
                 hidden_states = hidden_states[-1:, :, :]
             else:
@@ -710,7 +668,7 @@ def _postprocess(
             assert (
                 in_inference_mode
                 and inference_context.is_dynamic_batching()
-                and inference_context.materialize_only_last_token_logits
+                and inference_context.config.materialize_only_last_token_logits
             )
             self.output_layer.sequence_parallel = True
 
@@ -745,27 +703,6 @@ def _postprocess(
 
         return loss
 
-    def shared_embedding_or_output_weight(self) -> Tensor:
-        """Gets the embedding weight or output logit weights when share input embedding and
-        output weights set to True or when use Multi-Token Prediction (MTP) feature.
-
-        Returns:
-            Tensor: During pre processing or MTP process it returns the input embeddings weight.
-            Otherwise, during post processing it returns the final output layers weight.
-        """
-        if self.pre_process or self.mtp_process:
-            # Multi-Token Prediction (MTP) need both embedding layer and output layer.
-            # So there will be both embedding layer and output layer in the mtp process stage.
-            # In this case, if share_embeddings_and_output_weights is True, the shared weights
-            # will be stored in embedding layer, and output layer will not have any weight.
-            assert hasattr(
-                self, 'embedding'
-            ), f"embedding is needed in this pipeline stage, but it is not initialized."
-            return self.embedding.word_embeddings.weight
-        elif self.post_process:
-            return self.output_layer.weight
-        return None
-
     def build_schedule_plan(
         self,
         input_ids: Tensor,
@@ -856,20 +793,4 @@ def sharded_state_dict(
             output_extra_state and output_extra_state.data
         ), f'Expected output layer extra state to be empty, got: {output_extra_state}'
 
-        # Multi-Token Prediction (MTP) need embedding layer in mtp process stage.
-        # If MTP is not placed in the pre processing stage, we need to maintain a copy of
-        # embedding layer in the mtp process stage and tie it to the embedding in the pre
-        # processing stage.
-        # Now MTP loss is computed in post processing stage, so the output_layer is not needed.
-        if self.mtp_process and not self.pre_process:
-            emb_weight_key = f'{prefix}embedding.word_embeddings.weight'
-            emb_weight = self.embedding.word_embeddings.weight
-            tie_word_embeddings_state_dict(
-                sharded_state_dict,
-                emb_weight,
-                emb_weight_key,
-                tp_group=self.tp_group,
-                dp_cp_group=metadata['dp_cp_group'],
-            )
-
         return sharded_state_dict
diff --git a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py
index 5e9687b09a3..98a3eebb32b 100644
--- a/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py
+++ b/megatron/core/models/gpt/heterogeneous/heterogeneous_layer_specs.py
@@ -144,8 +144,10 @@ def _get_heterogenous_mlp_spec(mlp_config: MLPConfig, use_te: bool):
         mlp = ModuleSpec(
             module=MLP,
             submodules=MLPSubmodules(
-                linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear,
-                linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
+                linear_fc1=(
+                    not_none(TELayerNormColumnParallelLinear) if use_te else ColumnParallelLinear
+                ),
+                linear_fc2=not_none(TERowParallelLinear) if use_te else RowParallelLinear,
             ),
         )
     return mlp
diff --git a/megatron/core/models/mamba/mamba_layer_specs.py b/megatron/core/models/mamba/mamba_layer_specs.py
index b87124bab1d..6ca628475be 100755
--- a/megatron/core/models/mamba/mamba_layer_specs.py
+++ b/megatron/core/models/mamba/mamba_layer_specs.py
@@ -1,6 +1,7 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 from megatron.core.extensions.transformer_engine import (
+    TEColumnParallelLinear,
     TEDotProductAttention,
     TELayerNormColumnParallelLinear,
     TENorm,
@@ -19,6 +20,12 @@
 from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
+from megatron.core.transformer.multi_token_prediction import (
+    MultiTokenPredictionBlock,
+    MultiTokenPredictionBlockSubmodules,
+    MultiTokenPredictionLayer,
+    MultiTokenPredictionLayerSubmodules,
+)
 from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_layer import (
     MoETransformerLayer,
@@ -26,6 +33,7 @@
     TransformerLayerSubmodules,
 )
 
+# This should be private and should not be used outside of this file.
 moe = get_moe_module_spec(
     use_te=True,
     num_experts=8,  # Can be any positive integer (must not be None).
@@ -33,6 +41,28 @@
     moe_use_legacy_grouped_gemm=False,
 )
 
+
+# MTP block spec for Mamba - provides norms and projection only.
+# Inner layers are built by MultiTokenPredictionLayer using nested MambaStack
+_mamba_mtp_block_spec = ModuleSpec(
+    module=MultiTokenPredictionBlock,
+    submodules=MultiTokenPredictionBlockSubmodules(
+        layer_specs=[
+            ModuleSpec(
+                module=MultiTokenPredictionLayer,
+                submodules=MultiTokenPredictionLayerSubmodules(
+                    enorm=TENorm,
+                    hnorm=TENorm,
+                    eh_proj=TEColumnParallelLinear,
+                    mtp_model_layer=None,  # Built via pattern + mamba_submodules
+                    layer_norm=TENorm,
+                ),
+            )
+        ]
+    ),
+)
+
+
 mamba_stack_spec = ModuleSpec(
     module=MambaStack,
     submodules=MambaStackSubmodules(
@@ -87,9 +117,11 @@
                 pre_mlp_layernorm=TENorm, mlp=moe, mlp_bda=get_bias_dropout_add
             ),
         ),
+        mtp_block_spec=_mamba_mtp_block_spec,
     ),
 )
 
+
 mamba_inference_stack_spec = ModuleSpec(
     module=MambaStack,
     submodules=MambaStackSubmodules(
@@ -147,5 +179,6 @@
                 pre_mlp_layernorm=TENorm, mlp=moe, mlp_bda=get_bias_dropout_add
             ),
         ),
+        mtp_block_spec=_mamba_mtp_block_spec,
     ),
 )
diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py
index cf1002a5426..0aada61d5d4 100644
--- a/megatron/core/models/mamba/mamba_model.py
+++ b/megatron/core/models/mamba/mamba_model.py
@@ -4,6 +4,7 @@
 
 from torch import Tensor
 
+from megatron.core import tensor_parallel
 from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk
 from megatron.core.inference.contexts import BaseInferenceContext
 from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
@@ -15,7 +16,11 @@
 from megatron.core.tensor_parallel import gather_from_sequence_parallel_region
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import ModelType
-from megatron.core.transformer.linear_cross_entropy import LinearCrossEntropyModule
+from megatron.core.transformer.multi_token_prediction import (
+    MultiTokenPredictionBlock,
+    mtp_on_this_rank,
+    process_mtp_loss,
+)
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
 from megatron.core.utils import (
     WrappedTensor,
@@ -38,7 +43,11 @@ class MambaModel(LanguageModule):
         hybrid_attention_ratio (float, optional): The target ratio of attention
             layers to total layers
         hybrid_mlp_ratio (float, optional): The target ratio of mlp layers to total layers
-        hybrid_override_pattern (str, optional): The hybrid layer pattern to override with
+        hybrid_override_pattern (str, optional): Unified hybrid layer pattern with optional MTP.
+            Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
+            Examples:
+                - "M*M*" -> main decoder only, no MTP
+                - "M*M*/MM/MM" -> main="M*M*", mtp="MM", 2 depths
         post_process (bool, optional): Include an output layer (used with pipeline parallelism).
             Defaults to True.
         fp16_lm_cross_entropy (bool, optional): Defaults to False.
@@ -79,6 +88,7 @@ def __init__(
         scatter_embedding_sequence_parallel: bool = True,
         seq_len_interpolation_factor: Optional[float] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        vp_stage: Optional[int] = None,
     ) -> None:
         super().__init__(config=config, pg_collection=pg_collection)
 
@@ -97,6 +107,21 @@ def __init__(
         self.parallel_output = parallel_output
         self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
         self.position_embedding_type = position_embedding_type
+        self.vp_stage = vp_stage
+
+        # Parse unified pattern to extract main and MTP components
+        from megatron.core.ssm.mamba_hybrid_layer_allocation import parse_hybrid_pattern
+
+        parsed = parse_hybrid_pattern(hybrid_override_pattern)
+        self.mtp_pattern = parsed.mtp_pattern
+        self.mtp_num_depths = parsed.mtp_num_depths
+
+        # Determine if MTP is needed (based on pattern parsing)
+        self.mtp_process = (
+            self.mtp_pattern is not None
+            and self.mtp_num_depths > 0
+            and mtp_on_this_rank(self.config, vp_stage=self.vp_stage)
+        )
 
         # megatron core pipelining currently depends on model type
         # TODO: remove this dependency ?
@@ -107,7 +132,7 @@ def __init__(
             and self.config.cross_entropy_fusion_impl == "linear"
         )
 
-        if self.pre_process:
+        if self.pre_process or self.mtp_process:
             self.embedding = LanguageModelEmbedding(
                 config=self.config,
                 vocab_size=self.vocab_size,
@@ -133,15 +158,34 @@ def __init__(
             pre_process=self.pre_process,
             hybrid_attention_ratio=self.hybrid_attention_ratio,
             hybrid_mlp_ratio=self.hybrid_mlp_ratio,
-            hybrid_override_pattern=self.hybrid_override_pattern,
+            hybrid_override_pattern=parsed.main_pattern,
             post_process=self.post_process,
             dtype=config.params_dtype,
             pg_collection=self.pg_collection,
         )
 
+        # MTP block - uses mtp_block_spec from mamba_stack_spec.submodules
+        if self.mtp_process:
+            mamba_submodules = mamba_stack_spec.submodules
+            mtp_block_spec = mamba_submodules.mtp_block_spec
+            assert mtp_block_spec is not None, (
+                "MTP pattern specified but mtp_block_spec is None in mamba_stack_spec.submodules. "
+                "Ensure mamba_stack_spec includes mtp_block_spec for MTP support."
+            )
+
+            self.mtp = MultiTokenPredictionBlock(
+                config=self.config,
+                spec=mtp_block_spec,
+                pg_collection=self.pg_collection,
+                vp_stage=self.vp_stage,
+                mtp_layer_pattern=self.mtp_pattern,
+                mtp_num_depths=self.mtp_num_depths,
+                mamba_submodules=mamba_submodules,
+            )
+
         # Output
-        if post_process:
-            self.output_layer = LinearCrossEntropyModule(
+        if post_process or self.mtp_process:
+            self.output_layer = tensor_parallel.ColumnParallelLinear(
                 config.hidden_size,
                 self.vocab_size,
                 config=config,
@@ -154,7 +198,7 @@ def __init__(
                 tp_group=self.pg_collection.tp,
             )
 
-        if self.pre_process or self.post_process:
+        if self.pre_process or self.post_process or self.mtp_process:
             self.setup_embeddings_and_output_layer()
 
         for name, module in self.named_modules():
@@ -189,6 +233,7 @@ def forward(
         runtime_gather_output: Optional[bool] = None,
         *,
         inference_params: Optional[BaseInferenceContext] = None,
+        loss_mask: Optional[Tensor] = None,
         packed_seq_params: Optional[PackedSeqParams] = None,
         padding_mask: Optional[Tensor] = None,
     ) -> Tensor:
@@ -263,16 +308,42 @@ def forward(
             padding_mask=padding_mask,
         )
 
-        if not self.post_process:
-            return hidden_states
-
-        # logits and loss
         output_weight = None
         if self.share_embeddings_and_output_weights:
             output_weight = self.shared_embedding_or_output_weight()
 
+        if self.mtp_process:
+            hidden_states = self.mtp(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                inference_params=inference_params,
+                rotary_pos_emb=rotary_pos_emb,
+                packed_seq_params=packed_seq_params,
+                embedding=self.embedding,
+            )
+
+        if not self.post_process:
+            return hidden_states
+
+        if self.config.mtp_num_layers is not None:
+            hidden_states = process_mtp_loss(
+                hidden_states=hidden_states,
+                labels=labels,
+                loss_mask=loss_mask,
+                output_layer=self.output_layer,
+                output_weight=output_weight,
+                runtime_gather_output=runtime_gather_output,
+                is_training=self.training,
+                compute_language_model_loss=self.compute_language_model_loss,
+                config=self.config,
+                cp_group=self.pg_collection.cp,
+                packed_seq_params=packed_seq_params,
+            )
+
         sequence_parallel_override = False
-        if in_inference_mode and inference_context.materialize_only_last_token_logits:
+        if in_inference_mode and inference_context.config.materialize_only_last_token_logits:
             if inference_context.is_static_batching():
                 hidden_states = hidden_states[-1:, :, :]
             else:
@@ -286,7 +357,7 @@ def forward(
                     self.output_layer.sequence_parallel = False
                     sequence_parallel_override = True
 
-                # Reshape [B, 1, H] to [1, B, H] → extract each sample’s true last‐token hidden
+                # Reshape [B, 1, H] to [1, B, H] → extract each sample's true last‐token hidden
                 # state ([B, H]) → unsqueeze back to [B, 1, H]
                 # (so that the output layer, which expects S×B×H, receives only the final token)
                 hidden_states = inference_context.last_token_logits(
@@ -303,7 +374,7 @@ def forward(
             assert (
                 in_inference_mode
                 and inference_context.is_dynamic_batching()
-                and inference_context.materialize_only_last_token_logits
+                and inference_context.config.materialize_only_last_token_logits
             )
             self.output_layer.sequence_parallel = True
 
diff --git a/megatron/core/models/multimodal/llava_model.py b/megatron/core/models/multimodal/llava_model.py
index af0bcf6e9fd..63f62e9eeea 100644
--- a/megatron/core/models/multimodal/llava_model.py
+++ b/megatron/core/models/multimodal/llava_model.py
@@ -17,8 +17,10 @@
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.transformer import MegatronModule
+from megatron.core.transformer.attention import SelfAttentionSubmodules
 from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
 from megatron.core.utils import deprecate_inference_params, log_single_rank
 
 try:
@@ -158,9 +160,18 @@ def __init__(
         self.context_parallel_lm = language_transformer_config.context_parallel_size
         if self.sequence_parallel_lm or self.context_parallel_lm > 1:
             if not language_model_type.startswith('nemotron5-hybrid'):
-                attn_module = language_transformer_layer_spec.submodules.self_attention
+                assert isinstance(
+                    language_transformer_layer_spec.submodules, TransformerLayerSubmodules
+                )
+                assert isinstance(
+                    language_transformer_layer_spec.submodules.self_attention.submodules,
+                    SelfAttentionSubmodules,
+                )
+                attn_submodules = (
+                    language_transformer_layer_spec.submodules.self_attention.submodules
+                )
                 assert (
-                    attn_module.submodules.core_attention == TEDotProductAttention and HAVE_TE
+                    attn_submodules.core_attention == TEDotProductAttention and HAVE_TE
                 ), "Sequence/Context Parallelism is supported only with TE DotProductAttention."
             if self.context_parallel_lm > 1:
                 self.cp_group = self.pg_collection.cp
@@ -727,12 +738,13 @@ def _process_embedding_token_parallel(
                     "1.10.0"
                 ), "Please update Transformer Engine to >= 1.10 to use \
                     Context Parallel with THD format data"
-                cp_size = self.cp_group.size()
-                cp_rank = self.cp_group.rank()
+                index = tex.thd_get_partitioned_indices(
+                    packed_seq_params.cu_seqlens_q_padded,
+                    batch[next(iter(batch))].size(1),
+                    self.cp_group.size(),
+                    self.cp_group.rank(),
+                )
                 for key, data in batch.items():
-                    index = tex.thd_get_partitioned_indices(
-                        packed_seq_params.cu_seqlens_q_padded, data.size(1), cp_size, cp_rank
-                    )
                     batch[key] = data.index_select(1, index)
 
             if self.pre_process:
diff --git a/megatron/core/models/vision/multimodal_projector.py b/megatron/core/models/vision/multimodal_projector.py
index 31661934bb1..ab4d2f8cd41 100644
--- a/megatron/core/models/vision/multimodal_projector.py
+++ b/megatron/core/models/vision/multimodal_projector.py
@@ -6,8 +6,8 @@
 from megatron.core.fp8_utils import get_fp8_context
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
 from megatron.core.transformer.module import MegatronModule
-from megatron.core.transformer.spec_utils import build_module
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.typed_torch import apply_module, not_none
 from megatron.core.utils import make_viewless_tensor
 
 
@@ -45,12 +45,11 @@ def __init__(
                     config=config, submodules=submodules, input_size=input_size, tp_group=tp_group
                 )
             elif self.projector_type == "affine":
-                self.encoder = build_module(
-                    submodules.linear_fc1,
+                self.encoder = submodules.linear_fc1(
                     input_size,
                     config.hidden_size,
                     config=config,
-                    init_method=config.init_method,
+                    init_method=not_none(config.init_method),
                     gather_output=True,
                     bias=config.add_bias_linear,
                     skip_bias_add=True,
@@ -73,7 +72,7 @@ def forward(self, hidden_states):
         fp8_context = get_fp8_context(self.config)
         with fp8_context:
             # Run encoder.
-            encoder_output, encoder_output_bias = self.encoder(hidden_states)
+            encoder_output, encoder_output_bias = apply_module(self.encoder)(hidden_states)
 
             if encoder_output_bias is not None:
                 encoder_output = encoder_output + encoder_output_bias
diff --git a/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py b/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py
index 28487c3b367..c87ccd5ff31 100644
--- a/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py
+++ b/megatron/core/optimizer/cpu_offloading/hybrid_optimizer.py
@@ -122,7 +122,7 @@ def param_copy_back_gpu_hook(optimizer, args, kwargs):
                     for param in _param_generator(optimizer):
                         gpu_param = self.cpu_copys_map_gpu_param[param]
                         gpu_param.data.copy_(param.data, non_blocking=True)
-                self._d2h_stream.record_event().wait(torch.cuda.current_stream())
+                self._h2d_stream.record_event().wait(torch.cuda.current_stream())
 
             return param_copy_back_gpu_hook
 
diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py
index a4364f5e92d..833931bd12b 100644
--- a/megatron/core/optimizer/distrib_optimizer.py
+++ b/megatron/core/optimizer/distrib_optimizer.py
@@ -686,6 +686,8 @@ def state_dict(self):
                 # TE FusedAdam will not accumulate step for empty param groups, so we need to
                 # align the step across param groups.
                 param_group["step"] = int(step)
+            if "step" in param_group and param_group["step"] is None:
+                del param_group["step"]
 
         # Grad scaler state.
         if self.grad_scaler:
@@ -902,6 +904,8 @@ def _get_main_param_and_optimizer_states(self, model_param):
             sharded_model_param = self.optimizer.param_groups[group_index]["params"][group_order]
             tensors = {}
             for k in self.optimizer.state[sharded_model_param]:
+                if not isinstance(self.optimizer.state[sharded_model_param][k], torch.Tensor):
+                    continue
                 if isinstance(self.optimizer, HybridDeviceOptimizer):
                     tensors[k] = self.optimizer.state[sharded_model_param][k]
                     continue
@@ -911,7 +915,10 @@ def _get_main_param_and_optimizer_states(self, model_param):
         else:
             main_param = self.optimizer.param_groups[group_index]["params"][group_order]
             optim_state = self.optimizer.state[main_param]
-            tensors = {"param": main_param, **optim_state}
+            tensors = {"param": main_param}
+            for k, v in optim_state.items():
+                if isinstance(v, torch.Tensor):
+                    tensors[k] = v
         return tensors
 
     def _set_main_param_and_optimizer_states(self, model_param, tensors):
@@ -928,6 +935,8 @@ def _set_main_param_and_optimizer_states(self, model_param, tensors):
         if self.config.use_precision_aware_optimizer_no_fp8_or_ds_fp8:
             sharded_model_param = self.optimizer.param_groups[group_index]["params"][group_order]
             for k, v in tensors.items():
+                if not isinstance(v, torch.Tensor):
+                    continue
                 if isinstance(self.optimizer, HybridDeviceOptimizer):
                     if k == "param":
                         k = "master_param"
@@ -941,8 +950,13 @@ def _set_main_param_and_optimizer_states(self, model_param, tensors):
         else:
             main_param = self.optimizer.param_groups[group_index]["params"][group_order]
             optim_state = self.optimizer.state[main_param]
-            dst_tensors = {"param": main_param, **optim_state}
+            dst_tensors = {"param": main_param}
+            for k, v in optim_state.items():
+                if isinstance(v, torch.Tensor):
+                    dst_tensors[k] = v
             for key in dst_tensors:
+                if not isinstance(tensors[key], torch.Tensor):
+                    continue
                 dst_tensors[key].copy_(tensors[key])
 
     def get_parameter_state_dp_reshardable(self):
@@ -1667,6 +1681,11 @@ def sharded_param_state_dp_reshardable(
                             if key == 'padding':
                                 tensors[key] = LocalNonpersistentObject(tensors[key])
                                 continue
+                            if key == 'step':
+                                # The optimizer state of STEP is a 0-dim tensor and is handled
+                                # separately via param_groups, not as part of the gradient buffer.
+                                tensors[key] = LocalNonpersistentObject(tensors[key])
+                                continue
                             assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), (
                                 tensors[key].shape,
                                 gbuf_local_start,
@@ -1791,7 +1810,7 @@ def load_parameter_state_from_dp_reshardable(self, state_dict):
                     bucket_state = [
                         bucket_state_elem
                         for bucket_state_elem in bucket_state
-                        if not bucket_state_elem['padding']
+                        if not bucket_state_elem.get('padding', False)
                     ]
 
                     assert len(bucket_state) == len(gbuf_range_map["param_map"]), (
diff --git a/megatron/core/optimizer/optimizer.py b/megatron/core/optimizer/optimizer.py
index 2c33d7e701d..df8ec8ef613 100644
--- a/megatron/core/optimizer/optimizer.py
+++ b/megatron/core/optimizer/optimizer.py
@@ -12,6 +12,7 @@
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
+from typing_extensions import override
 
 try:
     from transformer_engine.pytorch.optimizers import multi_tensor_applier, multi_tensor_scale
@@ -1122,6 +1123,14 @@ def param_groups(self) -> List[dict]:
             param_groups += optimizer.param_groups
         return param_groups
 
+    @override
+    def get_parameters(self) -> List[torch.nn.Parameter]:
+        """Get list of parameters wrapped in all chained optimizers."""
+        params = []
+        for optimizer in self.chained_optimizers:
+            params.extend(optimizer.get_parameters())
+        return params
+
     @property
     def state(self) -> ProxyDict:
         """
diff --git a/megatron/core/optimizer_param_scheduler.py b/megatron/core/optimizer_param_scheduler.py
index 7ff6fee35a7..e01a708ce79 100644
--- a/megatron/core/optimizer_param_scheduler.py
+++ b/megatron/core/optimizer_param_scheduler.py
@@ -34,6 +34,26 @@ class ParamGroupOverride(TypedDict):
     wd_mult: float
 
 
+def get_canonical_lr_for_logging(param_groups: list[dict]) -> float | None:
+    """Return the lr of the first ``default_config=True`` param group.
+
+    All ``default_config`` groups share the same LR schedule, so the first one
+    is representative.  This includes empty rank-alignment stub groups, which
+    the scheduler still writes a valid lr onto.
+
+    Args:
+        param_groups (list[dict]): parameter groups from the optimizer.
+
+    Returns:
+        float | None: the canonical learning rate, or None if no
+            ``default_config=True`` group is found.
+    """
+    for param_group in param_groups:
+        if param_group.get('default_config', False):
+            return param_group.get('lr')
+    return None
+
+
 def param_group_override_to_tuple(
     param_group_override: ParamGroupOverride | None,
 ) -> tuple[tuple[str, Any], ...] | None:
@@ -265,6 +285,10 @@ def step(self, increment: int) -> None:
             increment (int): number of steps to increment
         """
         self.num_steps += increment
+        # Do not skip empty param groups: get_canonical_lr_for_logging reads lr
+        # from default_config groups regardless of whether they hold parameters.
+        # This is important for logging under model parallelism that may leave
+        # some ranks with empty default_config parameter groups.
         for param_group in self.optimizer.param_groups:
             param_group['lr'] = self.get_lr(param_group)
             param_group['weight_decay'] = self.get_wd(param_group) * param_group.get('wd_mult', 1.0)
diff --git a/megatron/core/parallel_state.py b/megatron/core/parallel_state.py
index 7bb96407838..2aaee40dc24 100644
--- a/megatron/core/parallel_state.py
+++ b/megatron/core/parallel_state.py
@@ -11,6 +11,7 @@
 
 import numpy as np
 import torch
+import torch.distributed as dist
 
 from .utils import GlobalMemoryBuffer, GlobalSymmetricMemoryBuffer, is_torch_min_version
 
@@ -1361,16 +1362,6 @@ def is_initialized():
     return _DATA_PARALLEL_GROUP is not None
 
 
-def is_unitialized() -> bool:
-    """Check if parallel state has been initialized
-
-    Deprecated. Use is_initialized instead.
-
-    """
-    warnings.warn("is_unitialized is deprecated, use is_initialized instead", DeprecationWarning)
-    return not is_initialized()
-
-
 def model_parallel_is_initialized():
     """Check if model- and data-parallel groups are initialized."""
     if (
@@ -1958,16 +1949,6 @@ def get_expert_data_parallel_group(check_initialized=True, partial_expert_data_p
         return _EXPERT_DATA_PARALLEL_GROUP
 
 
-def get_data_modulo_expert_parallel_group(partial_expert_data_parallel=False):
-    """[Deprecated] Get expert data parallel group."""
-    warnings.warn(
-        "get_data_modulo_expert_parallel_group is deprecated, please use "
-        "get_expert_data_parallel_group instead.",
-        DeprecationWarning,
-    )
-    return get_expert_data_parallel_group(partial_expert_data_parallel=partial_expert_data_parallel)
-
-
 def get_expert_data_parallel_group_gloo(partial_expert_data_parallel=False):
     """Get expert data parallel group-gloo."""
     if partial_expert_data_parallel:
diff --git a/megatron/core/pipeline_parallel/p2p_communication.py b/megatron/core/pipeline_parallel/p2p_communication.py
index ac839c21f18..f18309217c3 100644
--- a/megatron/core/pipeline_parallel/p2p_communication.py
+++ b/megatron/core/pipeline_parallel/p2p_communication.py
@@ -26,22 +26,22 @@ def _batched_p2p_ops(
     ops = []
     if tensor_send_prev is not None:
         send_prev_op = torch.distributed.P2POp(
-            torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group
+            torch.distributed.isend, tensor_send_prev, prev_pipeline_rank,
         )
         ops.append(send_prev_op)
     if tensor_recv_prev is not None:
         recv_prev_op = torch.distributed.P2POp(
-            torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group
+            torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank,
         )
         ops.append(recv_prev_op)
     if tensor_send_next is not None:
         send_next_op = torch.distributed.P2POp(
-            torch.distributed.isend, tensor_send_next, next_pipeline_rank, group
+            torch.distributed.isend, tensor_send_next, next_pipeline_rank,
         )
         ops.append(send_next_op)
     if tensor_recv_next is not None:
         recv_next_op = torch.distributed.P2POp(
-            torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group
+            torch.distributed.irecv, tensor_recv_next, next_pipeline_rank,
         )
         ops.append(recv_next_op)
     if len(ops) > 0:
diff --git a/megatron/core/pipeline_parallel/schedules.py b/megatron/core/pipeline_parallel/schedules.py
index f15dcd1400b..e903f392bf0 100644
--- a/megatron/core/pipeline_parallel/schedules.py
+++ b/megatron/core/pipeline_parallel/schedules.py
@@ -9,7 +9,6 @@
 from torch.autograd.variable import Variable
 
 from megatron.core import parallel_state
-from megatron.core.enums import ModelType
 from megatron.core.pipeline_parallel.fine_grained_activation_offload import (
     FineGrainedActivationOffloadingInterface as off_interface,
 )
@@ -213,7 +212,10 @@ def set_current_microbatch(model, microbatch_id):
             layer.current_microbatch = microbatch_id
         if hasattr(model_with_decoder, 'mtp'):
             for layer in model_with_decoder.mtp.layers:
-                layer.transformer_layer.current_microbatch = microbatch_id
+                assert hasattr(
+                    layer, 'mtp_model_layer'
+                ), f"MTP layer {layer} must have 'mtp_model_layer' attribute"
+                layer.mtp_model_layer.current_microbatch = microbatch_id
 
 
 def forward_step_calc_loss(
@@ -1012,10 +1014,6 @@ def forward_backward_pipelining_with_interleaving(
 
     elif p2p_communicator is not None and pg_collection is not None:
         model_type = get_model_type(model[0])
-        assert model_type != ModelType.encoder_and_decoder, (
-            "encoder PP stages not yet supported when passing custom process groups. "
-            "support coming soon!"
-        )
         assert hasattr(p2p_communicator, 'config'), "p2p_communicator must have a config"
         assert hasattr(pg_collection, 'tp'), "pg_collection must have a tp_group"
         assert hasattr(pg_collection, 'cp'), "pg_collection must have a cp_group"
@@ -2160,10 +2158,6 @@ def forward_backward_pipelining_without_interleaving(
         )
     elif p2p_communicator is not None and pg_collection is not None:
         model_type = get_model_type(model)
-        assert model_type != ModelType.encoder_and_decoder, (
-            "encoder PP stages not yet supported when passing custom process groups. "
-            "support coming soon!"
-        )
         assert hasattr(p2p_communicator, 'config'), "p2p_communicator must have a config"
         assert hasattr(pg_collection, 'tp'), "pg_collection must have tp_group"
         assert hasattr(pg_collection, 'cp'), "pg_collection must have cp_group"
diff --git a/megatron/core/post_training/modelopt/layers.py b/megatron/core/post_training/modelopt/layers.py
index 0045f6ea9f4..4a82048d255 100644
--- a/megatron/core/post_training/modelopt/layers.py
+++ b/megatron/core/post_training/modelopt/layers.py
@@ -1,15 +1,18 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+from __future__ import annotations
 
 import logging
-from typing import Callable, List, Optional
+from typing import TYPE_CHECKING, Callable, List, Optional, cast
 
 import torch
 
 from megatron.core.extensions.transformer_engine import _get_extra_te_kwargs
 from megatron.core.model_parallel_config import ModelParallelConfig
+from megatron.core.transformer.torch_norm import LayerNormInterface
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.transformer_layer import TransformerLayer
 from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint
+from megatron.core.typed_torch import copy_signature
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +21,13 @@
 
     HAVE_TE = True
 except ImportError:
-    HAVE_TE = False
+    if TYPE_CHECKING:
+        # Unambiguously treat transformer_engine as available during type checking.
+        import transformer_engine as te  # type: ignore[import]
+
+        HAVE_TE = True
+    else:
+        HAVE_TE = False
 
 logger = logging.getLogger(__name__)
 
@@ -53,7 +62,9 @@ class Norm:
     mismatch issue.
     """
 
-    def __new__(cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5):
+    def __new__(
+        cls, config: TransformerConfig, hidden_size: int, eps: float = 1e-5
+    ) -> LayerNormInterface:
         if not HAVE_TE:
             raise ImportError(
                 "Transformer-Engine is not installed, please install it with "
@@ -95,7 +106,7 @@ def _load_state_dict_pre_hook(
             instance._register_state_dict_hook(_state_dict_hook)
             instance._register_load_state_dict_pre_hook(_load_state_dict_pre_hook)
 
-        return instance
+        return cast(LayerNormInterface, instance)
 
 
 class Linear(torch.nn.Linear):
@@ -192,6 +203,7 @@ class RealQuantTransformerLayer(TransformerLayer):
     verbose: bool = False
     real_quant_cfg: str = "None"
 
+    @copy_signature(TransformerLayer.__init__)
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
diff --git a/megatron/core/post_training/modelopt/mamba/model_specs.py b/megatron/core/post_training/modelopt/mamba/model_specs.py
index 0a38d05b980..e9f83b49f71 100755
--- a/megatron/core/post_training/modelopt/mamba/model_specs.py
+++ b/megatron/core/post_training/modelopt/mamba/model_specs.py
@@ -18,9 +18,44 @@
 
 # Use this spec for ModelOpt PTQ and TensorRT-LLM export
 def get_mamba_stack_modelopt_spec(
+    local_core_attention: bool = False,
+    remap_te_layernorm: bool = False,
+    use_default_te_spec: bool = False,
+) -> ModuleSpec:
+    """Get the Mamba stack spec for ModelOpt PTQ and TensorRT-LLM export.
+
+    When use_default_te_spec=False (default), this is the native local spec with TENorm
+    from Transformer-Engine for the layernorm implementation (since FusedLayerNorm from
+    apex has stopped supporting RMSNorm needed by llama). The remap_te_layernorm flag
+    can be used to add sharded state_dict key remapping for TE-compatible checkpoint
+    saving/loading.
+
+    When use_default_te_spec=True, this returns the standard mamba_stack_spec from
+    mamba_layer_specs.py which uses full TE modules (TELayerNormColumnParallelLinear,
+    TERowParallelLinear, TEDotProductAttention, TENorm, moe_grouped_gemm=True).
+
+
+    Args:
+        local_core_attention: whether to use local DotProductAttention
+            (only for use_default_te_spec=False)
+        remap_te_layernorm: whether to perform sharded state_dict prefix mapping
+            on layernorm (only for use_default_te_spec=False)
+        use_default_te_spec: whether to use the default Transformer-Engine spec
+    """
+    if use_default_te_spec:
+        from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
+
+        return mamba_stack_spec
+
+    return _get_mamba_stack_local_spec(
+        local_core_attention=local_core_attention, remap_te_layernorm=remap_te_layernorm
+    )
+
+
+def _get_mamba_stack_local_spec(
     local_core_attention: bool = False, remap_te_layernorm: bool = False
 ) -> ModuleSpec:
-    """Mix the native spec with TENorm.
+    """Get the Mamba stack spec with local (non-TE) modules.
 
     This is essentially the native local spec except for the layernorm implementation
     is using TENorm from Transformer-Engine.
diff --git a/megatron/core/rerun_state_machine.py b/megatron/core/rerun_state_machine.py
index 8fce2beaa85..1136a493e72 100644
--- a/megatron/core/rerun_state_machine.py
+++ b/megatron/core/rerun_state_machine.py
@@ -16,6 +16,7 @@
 
 from megatron.core._rank_utils import log_single_rank, safe_get_rank
 from megatron.core.dist_checkpointing.mapping import ShardedObject
+from megatron.core.typed_torch import copy_signature
 
 """DISCLAIMER: THIS IS AN EXPERIMENTAL FEATURE.
 
@@ -1338,13 +1339,14 @@ def load_state_dict(self, state_dict: SerializableStateType) -> None:
         self.injected_error_type = state_dict["injected_error_type"]
 
 
-def initialize_rerun_state_machine(**kwargs) -> None:
+@copy_signature(RerunStateMachine.__init__, handle_first_src_param='skip')
+def initialize_rerun_state_machine(*args, **kwargs) -> None:
     """Helper function to initialize the rerun machine instance.
 
     Check the RerunStateMachine class for the details.
     """
 
-    rerun_state_machine: RerunStateMachine = RerunStateMachine(**kwargs)
+    rerun_state_machine: RerunStateMachine = RerunStateMachine(*args, **kwargs)
     _set_rerun_state_machine(rerun_state_machine)
 
 
diff --git a/megatron/core/resharding/__init__.py b/megatron/core/resharding/__init__.py
index d06484eef37..083c4518c0e 100644
--- a/megatron/core/resharding/__init__.py
+++ b/megatron/core/resharding/__init__.py
@@ -1,7 +1,12 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
 from .execution import execute_reshard_plan
 from .planner import build_centralized_reshard_plan
-from .refit import reshard_model_weights, swap_model_weights
+from .refit import (
+    clear_service_cache,
+    get_or_create_service,
+    reshard_model_weights,
+    swap_model_weights,
+)
 from .utils import ParameterMetadata, ReshardPlan, ShardingDescriptor, TransferOp
 
 __all__ = [
@@ -9,6 +14,8 @@
     "execute_reshard_plan",
     "swap_model_weights",
     "reshard_model_weights",
+    "get_or_create_service",
+    "clear_service_cache",
     "ParameterMetadata",
     "ShardingDescriptor",
     "TransferOp",
diff --git a/megatron/core/resharding/copy_services/__init__.py b/megatron/core/resharding/copy_services/__init__.py
index 15986e4d28e..447588f7b3a 100644
--- a/megatron/core/resharding/copy_services/__init__.py
+++ b/megatron/core/resharding/copy_services/__init__.py
@@ -3,5 +3,6 @@
 
 from .base import CopyService
 from .nccl_copy_service import NCCLCopyService
+from .nvshmem_copy_service import NVSHMEMCopyService
 
-__all__ = ["CopyService", "NCCLCopyService"]
+__all__ = ["CopyService", "NCCLCopyService", "NVSHMEMCopyService"]
diff --git a/megatron/core/resharding/copy_services/nvshmem_copy_service.py b/megatron/core/resharding/copy_services/nvshmem_copy_service.py
new file mode 100644
index 00000000000..14d83a1864e
--- /dev/null
+++ b/megatron/core/resharding/copy_services/nvshmem_copy_service.py
@@ -0,0 +1,174 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from __future__ import annotations
+
+import logging
+from typing import Dict
+
+import torch
+import torch.distributed as dist
+
+from ..nvshmem_copy_service import RemoteCopyService
+from .base import CopyService
+
+logger = logging.getLogger(__name__)
+
+
+class NVSHMEMCopyService(CopyService):
+    """CopyService implementation backed by NVSHMEM RemoteCopyService."""
+
+    def __init__(self):
+        if not dist.is_initialized():
+            raise RuntimeError("torch.distributed must be initialized before NVSHMEMCopyService()")
+
+        self.rank = dist.get_rank()
+        self._remote = RemoteCopyService()
+        # Lazily initialized on first use to avoid side effects at import time
+        self._initialized = False
+
+        # NOTE: keep the original typed tensors here (not uint8 views) so local copies
+        # preserve shape/strides semantics and avoid byte-offset pitfalls.
+        self._local_send_ops: Dict[int, torch.Tensor] = {}
+        self._local_recv_ops: Dict[int, torch.Tensor] = {}
+        self._local_copy_stream = torch.cuda.Stream()
+
+        logger.info("NVSHMEMCopyService constructed")
+
+    def _ensure_initialized(self):
+        if not self._initialized:
+            self._remote.init(log_level="INFO")
+            self._initialized = True
+            logger.info(
+                "NVSHMEMCopyService initialized: PE %d / %d", self._remote.my_pe, self._remote.n_pes
+            )
+
+    def submit_send(self, src_tensor: torch.Tensor, dest_rank: int):
+        """
+        Basic CopyService API is not rich enough to drive the NVSHMEM planner
+        (it lacks a globally shared task identifier), so this method is kept
+        only for interface compatibility and should not be used directly.
+
+        The resharding path calls into NVSHMEMCopyService via the
+        submit_send_with_id/submit_recv_with_id helpers instead.
+        """
+        raise RuntimeError(
+            "NVSHMEMCopyService.submit_send() is not supported; "
+            "use submit_send_with_id(...) from execute_reshard_plan."
+        )
+
+    def submit_recv(self, dest_tensor: torch.Tensor, src_rank: int):
+        raise RuntimeError(
+            "NVSHMEMCopyService.submit_recv() is not supported; "
+            "use submit_recv_with_id(...) from execute_reshard_plan."
+        )
+
+    #
+    # New helper API used from execute_reshard_plan via monkey-patching:
+    # we avoid changing the existing execute_reshard_plan signature by adding
+    # a small adapter layer that batches up matched send/recv slices.
+    #
+
+    def submit_send_with_id(self, task_id: int, src_tensor: torch.Tensor, dest_rank: int):
+        """Register a send with an explicit, globally shared task_id."""
+        self._ensure_initialized()
+
+        if not src_tensor.is_contiguous():
+            src_tensor = src_tensor.contiguous()
+
+        # Local transfers: keep them out of RemoteCopyService entirely.
+        if dest_rank == self.rank:
+            self._local_send_ops[task_id] = src_tensor
+            return
+
+        num_bytes = src_tensor.numel() * src_tensor.element_size()
+        src_bytes = src_tensor.view(torch.uint8)
+
+        logger.debug(
+            "NVSHMEMCopyService: register_send task_id=%d, %d bytes (%d → %d)",
+            task_id,
+            num_bytes,
+            self.rank,
+            dest_rank,
+        )
+
+        # Use public API on RemoteCopyService
+        self._remote.register_send(
+            task_id=task_id, src_tensor=src_bytes, src_pos=0, size=num_bytes, dest_pe=dest_rank
+        )
+
+    def submit_recv_with_id(self, task_id: int, dest_tensor: torch.Tensor, src_rank: int):
+        """Register a recv with an explicit, globally shared task_id."""
+        self._ensure_initialized()
+
+        if not dest_tensor.is_contiguous():
+            dest_tensor = dest_tensor.contiguous()
+
+        # Local transfers: keep them out of RemoteCopyService entirely.
+        if src_rank == self.rank:
+            self._local_recv_ops[task_id] = dest_tensor
+            return
+
+        num_bytes = dest_tensor.numel() * dest_tensor.element_size()
+        dst_bytes = dest_tensor.view(torch.uint8)
+
+        logger.debug(
+            "NVSHMEMCopyService: register_recv task_id=%d, %d bytes (%d ← %d)",
+            task_id,
+            num_bytes,
+            self.rank,
+            src_rank,
+        )
+
+        self._remote.register_receive(
+            task_id=task_id, dest_tensor=dst_bytes, dest_pos=0, size=num_bytes, src_pe=src_rank
+        )
+
+    def run(self):
+        """
+        Execute all registered transfer pairs via NVSHMEM.
+
+        This converts the registered pairs into RemoteCopyService send/receive
+        requests, builds a schedule, runs the pipelined NVSHMEM transfer, and
+        then clears internal state.
+        """
+        self._ensure_initialized()
+
+        # 1) Run same-rank copies (match by task_id), like NCCL backend.
+        if self._local_send_ops or self._local_recv_ops:
+            missing_sends = set(self._local_recv_ops.keys()) - set(self._local_send_ops.keys())
+            missing_recvs = set(self._local_send_ops.keys()) - set(self._local_recv_ops.keys())
+            if missing_sends or missing_recvs:
+                raise RuntimeError(
+                    "NVSHMEMCopyService: unmatched local ops on rank "
+                    f"{self.rank}: missing_sends={sorted(list(missing_sends))[:10]} "
+                    f"missing_recvs={sorted(list(missing_recvs))[:10]}"
+                )
+
+            with torch.no_grad():
+                with torch.cuda.stream(self._local_copy_stream):
+                    for task_id, dst in self._local_recv_ops.items():
+                        src = self._local_send_ops[task_id]
+                        if src.numel() != dst.numel() or src.element_size() != dst.element_size():
+                            raise RuntimeError(
+                                "NVSHMEMCopyService: local copy size mismatch on rank "
+                                f"{self.rank} task_id={task_id}: "
+                                f"src=({tuple(src.shape)}, {src.dtype}) "
+                                f"dst=({tuple(dst.shape)}, {dst.dtype})"
+                            )
+                        dst.copy_(src, non_blocking=True)
+
+            torch.cuda.current_stream().wait_stream(self._local_copy_stream)
+            self._local_send_ops.clear()
+            self._local_recv_ops.clear()
+
+        # 2) Execute remote schedule (if any remote sends/recvs were registered).
+        # NOTE: ALL ranks must call schedule() and run() because they contain collective
+        # operations that require all ranks to participate:
+        #  - schedule() has dist.all_gather_object() (torch distributed collective)
+        #  - run() has nvshmem.core.barrier_all() (nvshmem collective)
+        # This is critical for non-collocated refit where some ranks may have no work.
+        logger.info("NVSHMEMCopyService: building NVSHMEM schedule and executing")
+        self._remote.schedule()
+        self._remote.run()
+        self._remote.clear_requests()
+        logger.info("NVSHMEMCopyService: NVSHMEM transfers complete")
diff --git a/megatron/core/resharding/execution.py b/megatron/core/resharding/execution.py
index 6a7779406d0..7c7c4700754 100644
--- a/megatron/core/resharding/execution.py
+++ b/megatron/core/resharding/execution.py
@@ -23,14 +23,25 @@ def execute_reshard_plan(
     Execute a reshard plan (from centralized controller).
     A communication service must be provided to abstract transport.
     Expected service API: submit_send(tensor, dest_rank), submit_recv(tensor, src_rank), run().
+
+    Supports None for src_module and/or dst_module to allow ranks in non-collocated mode:
+    - src_module=None: Rank only receives data (destination-only)
+    - dst_module=None: Rank only sends data (source-only)
+    - Both provided: Rank participates in both send and recv (collocated mode)
     """
 
-    src_params = {name: p for name, p in src_module.named_parameters(recurse=True)}
-    dst_params = {name: p for name, p in dst_module.named_parameters(recurse=True)}
+    # Extract parameters from models if present
+    src_params = {}
+    dst_params = {}
+    if src_module is not None:
+        src_params = {name: p for name, p in src_module.named_parameters(recurse=True)}
+    if dst_module is not None:
+        dst_params = {name: p for name, p in dst_module.named_parameters(recurse=True)}
+
     submit_send_with_id = getattr(service, "submit_send_with_id", None)
     submit_recv_with_id = getattr(service, "submit_recv_with_id", None)
 
-    # Submit sends
+    # Submit sends (only if we have source model)
     for op in plan.send_ops:
         src_param = src_params.get(op.param_name)
         if src_param is not None:
@@ -40,7 +51,7 @@ def execute_reshard_plan(
             else:
                 service.submit_send(src_view, op.peer_rank)
 
-    # Submit recvs
+    # Submit recvs (only if we have destination model)
     recv_writebacks: List[Tuple[torch.Tensor, torch.nn.Parameter, tuple[slice, ...]]] = []
     for op in plan.recv_ops:
         dst_param = dst_params.get(op.param_name)
diff --git a/megatron/core/resharding/nvshmem_copy_service/__init__.py b/megatron/core/resharding/nvshmem_copy_service/__init__.py
new file mode 100644
index 00000000000..2ab8cde81fe
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/__init__.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+NVSHMEM-based remote copy service and supporting components.
+
+This package is an in-tree integration of the standalone
+`nvshmem_copy_service/python` implementation so that Megatron
+can use it without relying on an external library.
+"""
+
+from . import nvshmem_types
+from .core import GPUResourceManager, KernelLauncher, PipelineExecutor
+from .memory import DoubleBufferManager, TensorPointerExtractor
+from .planning import CommunicationScheduler, GPUExecutionPlanner, TaskSegmenter, WorkloadPacker
+from .service import RemoteCopyService
+
+__all__ = [
+    "RemoteCopyService",
+    "nvshmem_types",
+    "GPUResourceManager",
+    "KernelLauncher",
+    "PipelineExecutor",
+    "DoubleBufferManager",
+    "TensorPointerExtractor",
+    "CommunicationScheduler",
+    "GPUExecutionPlanner",
+    "TaskSegmenter",
+    "WorkloadPacker",
+]
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/__init__.py b/megatron/core/resharding/nvshmem_copy_service/core/__init__.py
new file mode 100644
index 00000000000..f466e925899
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/core/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""Core execution components for NVSHMEM operations."""
+
+from .gpu_resource_manager import GPUResourceManager
+from .kernel_launcher import KernelLauncher
+from .pipeline_executor import PipelineExecutor
+
+__all__ = ["GPUResourceManager", "KernelLauncher", "PipelineExecutor"]
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py b/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py
new file mode 100644
index 00000000000..6e03b914b26
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/core/gpu_resource_manager.py
@@ -0,0 +1,192 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+GPU resource management for NVSHMEM operations.
+
+Handles NVSHMEM initialization, CUDA device setup, stream management,
+and event lifecycle.
+"""
+
+import logging
+from typing import Dict, Optional
+
+try:
+    import nvshmem.core
+    from cuda.core.experimental import Device
+
+    HAVE_NVSHMEM = True
+except ImportError:
+    HAVE_NVSHMEM = False
+
+import torch
+import torch.distributed as dist
+
+logger = logging.getLogger(__name__)
+
+
+class GPUResourceManager:
+    """Manages GPU resources including NVSHMEM, streams, and events."""
+
+    def __init__(self):
+        self.device = None
+        self.my_pe: int = -1
+        self.n_pes: int = -1
+        self.initialized: bool = False
+
+        # CUDA streams (cuda.core.experimental)
+        self.pack_stream = None
+        self.unpack_stream = None
+        self.send_stream = None
+        self.copy_stream = None
+
+        # PyTorch stream wrappers
+        self.torch_pack_stream = None
+        self.torch_unpack_stream = None
+        self.torch_send_stream = None
+        self.torch_copy_stream = None
+
+        # Stream name to PyTorch stream mapping
+        self._torch_streams: Dict[str, torch.cuda.ExternalStream] = {}
+
+    def init(self) -> None:
+        """
+        Initialize NVSHMEM, CUDA device, and streams.
+
+        Expects torch.distributed to be already initialized.
+        """
+        if self.initialized:
+            return
+
+        if not HAVE_NVSHMEM:
+            raise RuntimeError(
+                "nvshmem.core is not available. Please install nvshmem to use GPUResourceManager."
+            )
+
+        # torch.distributed must be initialized before calling this
+        if not dist.is_initialized():
+            raise RuntimeError(
+                "torch.distributed must be initialized before " "GPUResourceManager.init()"
+            )
+
+        # Get current CUDA device (already set by caller based on LOCAL_RANK)
+        local_rank = torch.cuda.current_device()
+
+        # nvshmem4py requires a cuda.core Device at init time
+        self.device = Device(local_rank)
+        self.device.set_current()
+
+        # Extract rank, nranks from the default process group
+        num_ranks = dist.get_world_size()
+        rank_id = dist.get_rank()
+
+        # Create/Broadcast UniqueID using broadcast_object_list
+        uniqueid = nvshmem.core.get_unique_id(empty=True)
+        if rank_id == 0:
+            uniqueid = nvshmem.core.get_unique_id()
+            broadcast_objects = [uniqueid]
+        else:
+            broadcast_objects = [None]
+
+        # Broadcast ID to all ranks using the default group
+        dist.broadcast_object_list(broadcast_objects, src=0)
+
+        # Barrier to ensure everyone has the ID before NVSHMEM init
+        dist.barrier()
+
+        # Initialize NVSHMEM with the broadcasted UID
+        nvshmem.core.init(
+            device=self.device,
+            uid=broadcast_objects[0],
+            rank=rank_id,
+            nranks=num_ranks,
+            initializer_method="uid",
+        )
+
+        logger.info("NVSHMEM initialized")
+
+        self.my_pe = nvshmem.core.my_pe()
+        self.n_pes = nvshmem.core.n_pes()
+
+        # Create CUDA streams
+        self.pack_stream = self.device.create_stream()
+        self.unpack_stream = self.device.create_stream()
+        self.send_stream = self.device.create_stream()
+        self.copy_stream = self.device.create_stream()
+
+        # Get stream pointers and create PyTorch wrappers
+        _, pack_stream_ptr = self.pack_stream.__cuda_stream__()
+        _, unpack_stream_ptr = self.unpack_stream.__cuda_stream__()
+        _, send_stream_ptr = self.send_stream.__cuda_stream__()
+        _, copy_stream_ptr = self.copy_stream.__cuda_stream__()
+
+        self.torch_pack_stream = torch.cuda.ExternalStream(pack_stream_ptr)
+        self.torch_unpack_stream = torch.cuda.ExternalStream(unpack_stream_ptr)
+        self.torch_send_stream = torch.cuda.ExternalStream(send_stream_ptr)
+        self.torch_copy_stream = torch.cuda.ExternalStream(copy_stream_ptr)
+
+        # Build stream mapping
+        self._torch_streams = {
+            "pack": self.torch_pack_stream,
+            "unpack": self.torch_unpack_stream,
+            "send": self.torch_send_stream,
+            "copy": self.torch_copy_stream,
+        }
+
+        logger.info("Stream mapping built")
+
+        self.initialized = True
+
+        # Initial barrier to ensure all PEs are ready
+        nvshmem.core.barrier_all(stream=self.send_stream)
+
+    def get_stream(self, name: str):
+        """
+        Get CUDA stream by name.
+
+        Args:
+            name: Stream name ('pack', 'unpack', 'send', 'copy')
+
+        Returns:
+            CUDA stream object
+        """
+        streams = {
+            "pack": self.pack_stream,
+            "unpack": self.unpack_stream,
+            "send": self.send_stream,
+            "copy": self.copy_stream,
+        }
+        return streams.get(name)
+
+    def get_torch_stream(self, name: str) -> Optional[torch.cuda.ExternalStream]:
+        """
+        Get PyTorch ExternalStream by name.
+
+        Args:
+            name: Stream name ('pack', 'unpack', 'send', 'copy')
+
+        Returns:
+            PyTorch ExternalStream
+        """
+        return self._torch_streams.get(name)
+
+    def create_events(self, num_events: int = 2):
+        """
+        Create double-buffered CUDA events for pack and unpack operations.
+
+        Args:
+            num_events: Number of events to create for each type
+                (default: 2 for double buffering)
+
+        Returns:
+            tuple: (pack_events, unpack_events) lists of torch.cuda.Event
+        """
+        pack_events = [torch.cuda.Event(enable_timing=False) for _ in range(num_events)]
+        unpack_events = [torch.cuda.Event(enable_timing=False) for _ in range(num_events)]
+        return pack_events, unpack_events
+
+    def finalize(self) -> None:
+        """Cleanup resources (streams are automatically managed by CUDA)."""
+        self.initialized = False
+        self.my_pe = -1
+        self.n_pes = -1
+        # Streams are automatically cleaned up when objects are deleted
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py b/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py
new file mode 100644
index 00000000000..4e86d6a9505
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/core/kernel_launcher.py
@@ -0,0 +1,147 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+CUDA kernel management and launching for pack/unpack operations.
+
+Handles kernel compilation, launching, and stream coordination.
+"""
+
+import os
+from typing import Any, Tuple
+
+try:
+    import cupy as cp
+
+    HAVE_CUPY = True
+except ImportError:
+    HAVE_CUPY = False
+
+import torch
+import torch.cuda.nvtx as nvtx
+
+
+class KernelLauncher:
+    """Manages CUDA kernel loading and launching for data pack/unpack operations."""
+
+    def __init__(self):
+        self.chunked_copy_kernel = None
+        # Cached CuPy stream wrappers for efficient kernel launching
+        self.cp_pack_stream = None
+        self.cp_unpack_stream = None
+
+    def load_kernels(self) -> None:
+        """Load and compile CUDA kernels from source."""
+        if not HAVE_CUPY:
+            raise RuntimeError("cupy is not available. Please install cupy to use KernelLauncher.")
+
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        kernel_path = os.path.join(current_dir, "..", "kernels", "chunked_kernel.cu")
+
+        with open(kernel_path, "r") as f:
+            kernel_source = f.read()
+
+        self.chunked_copy_kernel = cp.RawKernel(
+            kernel_source, "chunked_batched_copy_kernel", options=("-std=c++11",)
+        )
+
+    def set_streams(self, pack_stream, unpack_stream) -> None:
+        """
+        Cache CuPy stream wrappers for kernel launching.
+
+        This eliminates per-launch overhead of stream pointer extraction
+        and CuPy ExternalStream creation.
+
+        Args:
+            pack_stream: CUDA stream for pack operations
+            unpack_stream: CUDA stream for unpack operations
+        """
+        _, pack_stream_ptr = pack_stream.__cuda_stream__()
+        _, unpack_stream_ptr = unpack_stream.__cuda_stream__()
+        self.cp_pack_stream = cp.cuda.ExternalStream(pack_stream_ptr)
+        self.cp_unpack_stream = cp.cuda.ExternalStream(unpack_stream_ptr)
+
+    def launch_pack(
+        self,
+        gpu_plan: Tuple[Any, Any, Any, int],
+        pack_stream,
+        torch_pack_stream: torch.cuda.ExternalStream,
+        pack_event: torch.cuda.Event,
+    ) -> None:
+        """
+        Launch pack kernel to copy data from user tensors to send buffer.
+
+        Args:
+            gpu_plan: Tuple of (cp_src_addrs, cp_dst_addrs, cp_sizes, num_chunks)
+                as CuPy arrays
+            pack_stream: CUDA stream (cuda.core.experimental.Stream) - unused,
+                kept for compatibility
+            torch_pack_stream: PyTorch external stream wrapper
+            pack_event: CUDA event to record after kernel launch
+        """
+        nvtx.range_push("Launch Pack Kernel")
+        if not gpu_plan:
+            nvtx.range_pop()
+            return
+
+        # Unpack cached CuPy arrays from gpu_plan
+        cp_src, cp_dst, cp_sizes, num_chunks = gpu_plan
+
+        # Grid/Block configuration
+        THREADS_PER_BLOCK = 1024
+        NUM_BLOCKS = 75
+
+        # Launch kernel using cached CuPy stream
+        assert self.chunked_copy_kernel is not None
+        assert self.cp_pack_stream is not None
+        self.chunked_copy_kernel(
+            (NUM_BLOCKS,),
+            (THREADS_PER_BLOCK,),
+            (cp_src, cp_dst, cp_sizes, num_chunks),
+            stream=self.cp_pack_stream,
+        )
+        nvtx.range_pop()
+        # Record event on PyTorch stream
+        pack_event.record(stream=torch_pack_stream)
+
+    def launch_unpack(
+        self,
+        gpu_plan: Tuple[Any, Any, Any, int],
+        unpack_stream,
+        torch_unpack_stream: torch.cuda.ExternalStream,
+        unpack_event: torch.cuda.Event,
+    ) -> None:
+        """
+        Launch unpack kernel to copy data from receive buffer to user tensors.
+
+        Args:
+            gpu_plan: Tuple of (cp_src_addrs, cp_dst_addrs, cp_sizes, num_chunks)
+                as CuPy arrays
+            unpack_stream: CUDA stream (cuda.core.experimental.Stream) - unused,
+            kept for compatibility
+            torch_unpack_stream: PyTorch external stream wrapper
+            unpack_event: CUDA event to record after kernel launch
+        """
+        nvtx.range_push("Launch Unpack Kernel")
+        if not gpu_plan:
+            nvtx.range_pop()
+            return
+
+        # Unpack cached CuPy arrays from gpu_plan
+        cp_src, cp_dst, cp_sizes, num_chunks = gpu_plan
+
+        # Grid/Block configuration
+        THREADS_PER_BLOCK = 1024
+        NUM_BLOCKS = 75
+
+        # Launch kernel using cached CuPy stream
+        assert self.chunked_copy_kernel is not None
+        assert self.cp_unpack_stream is not None
+        self.chunked_copy_kernel(
+            (NUM_BLOCKS,),
+            (THREADS_PER_BLOCK,),
+            (cp_src, cp_dst, cp_sizes, num_chunks),
+            stream=self.cp_unpack_stream,
+        )
+        nvtx.range_pop()
+        # Record event on PyTorch stream
+        unpack_event.record(stream=torch_unpack_stream)
diff --git a/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py b/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py
new file mode 100644
index 00000000000..5ba07f9956a
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/core/pipeline_executor.py
@@ -0,0 +1,275 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Pipelined communication execution engine.
+
+Orchestrates the pack/send/unpack pipeline with double-buffering
+and proper stream synchronization.
+"""
+
+from typing import Dict, List, Optional
+
+try:
+    import nvshmem.core
+
+    HAVE_NVSHMEM = True
+except ImportError:
+    HAVE_NVSHMEM = False
+
+import torch
+
+from ..logger import PELogger
+from ..memory.double_buffer_manager import DoubleBufferManager
+from ..nvshmem_types import ReceiveRequest, ScheduledBatch, SendRequest
+from .kernel_launcher import KernelLauncher
+
+
+class PipelineExecutor:
+    """Executes pipelined NVSHMEM communication with pack/send/unpack overlap."""
+
+    def __init__(
+        self, kernel_launcher: KernelLauncher, buffer_manager: DoubleBufferManager, my_pe: int
+    ):
+        """
+        Initialize pipeline executor.
+
+        Args:
+            kernel_launcher: KernelLauncher instance for pack/unpack kernels
+            buffer_manager: DoubleBufferManager for send/recv buffers
+            my_pe: This PE's rank
+        """
+        self.kernel_launcher = kernel_launcher
+        self.buffer_manager = buffer_manager
+        self.my_pe = my_pe
+
+        # Streams (will be set by service)
+        self.pack_stream = None
+        self.unpack_stream = None
+        self.send_stream = None
+        self.copy_stream = None
+
+        self.torch_pack_stream = None
+        self.torch_unpack_stream = None
+        self.torch_copy_stream = None
+
+        # Events for double-buffered synchronization
+        self.pack_events = []
+        self.unpack_events = []
+
+    def set_streams(
+        self,
+        pack_stream,
+        unpack_stream,
+        send_stream,
+        copy_stream,
+        torch_pack_stream,
+        torch_unpack_stream,
+        torch_copy_stream,
+    ):
+        """Set CUDA streams for execution."""
+        self.pack_stream = pack_stream
+        self.unpack_stream = unpack_stream
+        self.send_stream = send_stream
+        self.copy_stream = copy_stream
+
+        self.torch_pack_stream = torch_pack_stream
+        self.torch_unpack_stream = torch_unpack_stream
+        self.torch_copy_stream = torch_copy_stream
+
+    def set_events(self, pack_events: List, unpack_events: List):
+        """Set double-buffered CUDA events."""
+        self.pack_events = pack_events
+        self.unpack_events = unpack_events
+
+    def execute_pipeline(
+        self, iter_schedules: List[Dict[str, Optional[ScheduledBatch]]], num_iterations: int
+    ) -> None:
+        """
+        Execute pipelined communication.
+
+        Pipeline stages:
+        1. Pack NEXT iteration (async)
+        2. Unpack PRIOR iteration (async)
+        3. Send CURRENT iteration (sync)
+        4. Barrier
+        5. Wait for async pack/unpack to complete
+
+        Args:
+            iter_schedules: List of iteration schedules
+            num_iterations: Total number of iterations
+        """
+        PELogger.info(f"Executing pipeline: {num_iterations} iterations")
+
+        # Priming: Pack iteration 0 and WAIT for completion
+        if num_iterations > 0 and iter_schedules[0]["send"]:
+            torch.cuda.nvtx.range_push("Priming")
+            PELogger.debug("Priming: Packing iteration 0")
+            self._launch_pack(0, iter_schedules[0]["send"])
+            self.pack_events[0].synchronize()
+            torch.cuda.nvtx.range_pop()
+
+        for i in range(num_iterations):
+            torch.cuda.nvtx.range_push(f"Iteration {i}")
+            has_send = iter_schedules[i]["send"] is not None
+            has_recv = iter_schedules[i]["recv"] is not None
+            has_next_send = i + 1 < num_iterations and iter_schedules[i + 1]["send"] is not None
+            has_prior_recv = i > 0 and iter_schedules[i - 1]["recv"] is not None
+
+            slot = i % 2
+
+            # Log iteration start
+            send_info = (
+                f" → PE {iter_schedules[i]['send'].dest_pe} "
+                f"({iter_schedules[i]['send'].total_size} bytes)"
+                if has_send
+                else ""
+            )
+            recv_info = (
+                f" ← PE {iter_schedules[i]['recv'].src_pe} "
+                f"({iter_schedules[i]['recv'].total_size} bytes)"
+                if has_recv
+                else ""
+            )
+            PELogger.debug(f"Iteration {i}/{num_iterations}: slot={slot}{send_info}{recv_info}")
+
+            # Step 1: Pack NEXT iteration (async)
+            if has_next_send:
+                torch.cuda.nvtx.range_push("Step 1: Pack Next")
+                next_batch = iter_schedules[i + 1]["send"]
+                assert next_batch is not None
+                PELogger.debug(
+                    f"  Pack next (iter {i+1}): {len(next_batch.tasks)} tasks "
+                    f"→ PE {next_batch.dest_pe}"
+                )
+                self._launch_pack(i + 1, next_batch)
+                torch.cuda.nvtx.range_pop()
+
+            # Step 2: Unpack PRIOR iteration (async)
+            if has_prior_recv:
+                torch.cuda.nvtx.range_push("Step 2: Unpack Prior")
+                prior_batch = iter_schedules[i - 1]["recv"]
+                assert prior_batch is not None
+                PELogger.debug(
+                    f"  Unpack prior (iter {i-1}): {prior_batch.total_size} bytes "
+                    f"← PE {prior_batch.src_pe}"
+                )
+                self._launch_unpack(i - 1, prior_batch)
+                torch.cuda.nvtx.range_pop()
+
+            # Step 3: Send CURRENT iteration
+            if has_send:
+                torch.cuda.nvtx.range_push("Step 3: Send Current")
+                batch = iter_schedules[i]["send"]
+                assert batch is not None
+                transfer_size = batch.total_size
+                PELogger.debug(f"  Send current: {transfer_size} bytes → PE {batch.dest_pe}")
+
+                nvshmem.core.put(
+                    self.buffer_manager.recv_slots[slot][0:transfer_size],
+                    self.buffer_manager.send_slots[slot][0:transfer_size],
+                    batch.dest_pe,
+                    stream=self.send_stream,
+                )
+                torch.cuda.nvtx.range_pop()
+
+            # Ensure send completes
+            self.send_stream.sync()
+            nvshmem.core.quiet(stream=self.send_stream)
+
+            # Step 4: Global barrier
+            torch.cuda.nvtx.range_push("Step 4: Barrier")
+            nvshmem.core.barrier_all(stream=self.send_stream)
+            self.send_stream.sync()
+            torch.cuda.nvtx.range_pop()
+
+            # Step 5: Wait for async pack/unpack to complete
+            torch.cuda.nvtx.range_push("Step 5: Wait Async")
+            if has_prior_recv:
+                self.unpack_events[(i - 1) % 2].synchronize()
+            if has_next_send:
+                self.pack_events[(i + 1) % 2].synchronize()
+            torch.cuda.nvtx.range_pop()
+
+            torch.cuda.nvtx.range_pop()
+
+        # Final unpack for last iteration
+        if num_iterations > 0 and iter_schedules[num_iterations - 1]["recv"]:
+            torch.cuda.nvtx.range_push("Final Unpack")
+            PELogger.debug(f"Final unpack: iteration {num_iterations-1}")
+            last_recv = iter_schedules[num_iterations - 1]["recv"]
+            assert last_recv is not None
+            self._launch_unpack(num_iterations - 1, last_recv)
+            self.unpack_events[(num_iterations - 1) % 2].synchronize()
+            torch.cuda.nvtx.range_pop()
+
+        PELogger.info(f"Pipeline complete: {num_iterations} iterations")
+
+    def _launch_pack(self, iteration: int, batch: ScheduledBatch) -> None:
+        """Launch pack kernel for given iteration."""
+        if not batch.gpu_plan:
+            return
+
+        self.kernel_launcher.launch_pack(
+            batch.gpu_plan,
+            self.pack_stream,
+            self.torch_pack_stream,
+            self.pack_events[iteration % 2],
+        )
+
+    def _launch_unpack(self, iteration: int, batch: ScheduledBatch) -> None:
+        """Launch unpack kernel for given iteration."""
+        if not batch.gpu_plan:
+            return
+
+        self.kernel_launcher.launch_unpack(
+            batch.gpu_plan,
+            self.unpack_stream,
+            self.torch_unpack_stream,
+            self.unpack_events[iteration % 2],
+        )
+
+    def process_self_moves(
+        self, send_requests: List[SendRequest], receive_requests: List[ReceiveRequest]
+    ) -> None:
+        """
+        Handle same-PE transfers (where src_pe == dest_pe == my_pe).
+
+        Uses PyTorch copy on the copy stream for efficiency.
+
+        Args:
+            send_requests: List of send requests
+            receive_requests: List of receive requests
+        """
+        # Match send/recv requests where src_pe == dest_pe == my_pe
+        local_sends = {r.task_id: r for r in send_requests if r.dest_pe == self.my_pe}
+        local_recvs = [r for r in receive_requests if r.src_pe == self.my_pe]
+
+        if local_recvs:
+            PELogger.debug(f"Processing {len(local_recvs)} self-moves")
+
+        num_processed = 0
+        with torch.cuda.stream(self.torch_copy_stream):
+            for recv_req in local_recvs:
+                if recv_req.task_id in local_sends:
+                    send_req = local_sends[recv_req.task_id]
+                    PELogger.debug(
+                        "  Self-move: task_id=%d, size=%d bytes", recv_req.task_id, send_req.size
+                    )
+
+                    # Create views of the tensors with offsets
+                    src_view = send_req.src_tensor[
+                        send_req.src_pos : send_req.src_pos + send_req.size
+                    ]
+                    dest_view = recv_req.dest_tensor[
+                        recv_req.dest_pos : recv_req.dest_pos + send_req.size
+                    ]
+
+                    # Async copy on the copy stream
+                    dest_view.copy_(src_view, non_blocking=True)
+                    num_processed += 1
+
+        # Synchronize the PyTorch stream
+        self.torch_copy_stream.synchronize()
+
+        if num_processed > 0:
+            PELogger.info("Self-moves complete: %d transfers", num_processed)
diff --git a/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu b/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu
new file mode 100644
index 00000000000..e5b8fcc9a85
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/kernels/chunked_kernel.cu
@@ -0,0 +1,103 @@
+
+#include <cuda_runtime.h>
+
+// CUDA-compatible types (no C++ standard library headers for NVRTC)
+typedef unsigned char uint8_t;
+typedef unsigned long long uint64_t;
+typedef uint64_t uintptr_t;
+
+// ============================================================================
+// Kernel Configuration Constants (from ChunkedKernel.h)
+// ============================================================================
+
+constexpr int CHUNK_SIZE = 128 * 1024;       // 128KB per chunk
+constexpr int NUM_BLOCKS = 75;               // Fixed grid size
+constexpr int THREADS_PER_BLOCK = 1024;      // Fixed block size
+constexpr int FLOAT4_SIZE = 16;              // 16 bytes per float4
+constexpr int MAX_CHUNKS_PER_BLOCK = 512;    // Max chunks per block for shared memory
+
+extern "C" {
+
+/**
+ * Chunked batched copy kernel implementation
+ *
+ * This kernel performs efficient batched memory copies using:
+ * 1. Contiguous block assignment for better load balancing
+ * 2. Shared memory prefetching of chunk metadata
+ * 3. Vectorized float4 (16-byte) copies for aligned data
+ * 4. Byte-by-byte fallback for unaligned or small data
+ */
+__global__ void chunked_batched_copy_kernel(
+    uint8_t** src_addrs,
+    uint8_t** dst_addrs,
+    size_t* sizes,
+    int total_chunks
+) {
+    // Shared memory for metadata prefetching
+    __shared__ uint8_t* s_src_addrs[MAX_CHUNKS_PER_BLOCK];
+    __shared__ uint8_t* s_dst_addrs[MAX_CHUNKS_PER_BLOCK];
+    __shared__ size_t s_sizes[MAX_CHUNKS_PER_BLOCK];
+
+    // Contiguous block assignment: block i processes chunks [start_chunk, end_chunk)
+    int chunks_per_block = (total_chunks + gridDim.x - 1) / gridDim.x;  // Ceiling division
+    int start_chunk = blockIdx.x * chunks_per_block;
+    int end_chunk = start_chunk + chunks_per_block;
+    if (end_chunk > total_chunks) {
+        end_chunk = total_chunks;
+    }
+    int num_chunks_this_block = end_chunk - start_chunk;
+
+    // Phase 1: Cooperative loading of metadata to shared memory
+    // All 1024 threads cooperate to load metadata from global memory
+    for (int i = threadIdx.x; i < num_chunks_this_block; i += blockDim.x) {
+        int global_chunk_id = start_chunk + i;
+        s_src_addrs[i] = src_addrs[global_chunk_id];
+        s_dst_addrs[i] = dst_addrs[global_chunk_id];
+        s_sizes[i] = sizes[global_chunk_id];
+    }
+    __syncthreads();
+
+    // Phase 2: Process each chunk using metadata from shared memory
+    for (int chunk_id = 0; chunk_id < num_chunks_this_block; chunk_id++) {
+        uint8_t* src = s_src_addrs[chunk_id];
+        uint8_t* dst = s_dst_addrs[chunk_id];
+        size_t size = s_sizes[chunk_id];
+
+        // Check if both src and dst are aligned to 16 bytes for float4 access
+        uintptr_t src_addr = (uintptr_t)src;
+        uintptr_t dst_addr = (uintptr_t)dst;
+        bool is_aligned = ((src_addr % FLOAT4_SIZE) == 0) && ((dst_addr % FLOAT4_SIZE) == 0);
+
+        if (is_aligned && size >= FLOAT4_SIZE) {
+            // Fast path: vectorized float4 copies
+            size_t aligned_size = (size / FLOAT4_SIZE) * FLOAT4_SIZE;
+
+            // All 1024 threads cooperate on float4 copies
+            #pragma unroll 4
+            for (size_t offset = threadIdx.x * FLOAT4_SIZE;
+                 offset < aligned_size;
+                 offset += blockDim.x * FLOAT4_SIZE) {
+                // Vectorized 16-byte load and store
+                float4 data = *((float4*)(src + offset));
+                *((float4*)(dst + offset)) = data;
+            }
+
+            // Handle remaining bytes (< 16 bytes) with byte-by-byte copy
+            for (size_t offset = aligned_size + threadIdx.x;
+                 offset < size;
+                 offset += blockDim.x) {
+                dst[offset] = src[offset];
+            }
+        } else {
+            // Fallback path: byte-by-byte copy for unaligned addresses
+            // Still use all threads for parallelism
+            for (size_t offset = threadIdx.x; offset < size; offset += blockDim.x) {
+                dst[offset] = src[offset];
+            }
+        }
+    }
+}
+
+}
+
+
diff --git a/megatron/core/resharding/nvshmem_copy_service/logger.py b/megatron/core/resharding/nvshmem_copy_service/logger.py
new file mode 100644
index 00000000000..a3c7c1699ad
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/logger.py
@@ -0,0 +1,209 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+
+Per-PE Logger with colored console and file output.
+
+
+
+Similar to the C++ Logger implementation, provides:
+
+- Per-PE colored console output
+
+- Per-PE file logging
+
+- Support for TRACE, DEBUG, INFO, SUMMARY, WARN, ERROR levels
+
+"""
+
+import logging
+import os
+from datetime import datetime
+from typing import Optional
+
+
+class ColoredFormatter(logging.Formatter):
+    """Custom formatter that adds color codes for console output."""
+
+    def __init__(self, fmt: str, pe_id: int, use_color: bool = True):
+        super().__init__(fmt)
+        self.pe_id = pe_id
+        self.use_color = use_color
+
+        # ANSI color codes matching C++ implementation
+        self.colors = {
+            0: "\033[31m",  # Red
+            1: "\033[32m",  # Green
+            2: "\033[33m",  # Yellow
+            3: "\033[34m",  # Blue
+            4: "\033[35m",  # Magenta
+            5: "\033[36m",  # Cyan
+            6: "\033[91m",  # Bright Red
+            7: "\033[92m",  # Bright Green
+        }
+        self.reset = "\033[0m"
+
+    def formatTime(self, record, datefmt=None):
+        ct = self.converter(record.created)
+        if datefmt:
+            s = datetime.fromtimestamp(record.created).strftime(datefmt)
+            # For file logs, replace %f with milliseconds
+            if "%f" in datefmt:
+                s = s.replace("%f", f"{int(record.msecs):03d}")
+        else:
+            s = datetime.fromtimestamp(record.created).strftime("%H:%M:%S")
+            s = f"{s}.{int(record.msecs):03d}"
+        return s
+
+    def format(self, record):
+        # Save original message
+        original_msg = record.msg
+
+        if self.use_color and self.pe_id >= 0:
+            color = self.colors.get(self.pe_id, "\033[37m")  # White for others
+            record.msg = f"{color}{record.msg}{self.reset}"
+
+        result = super().format(record)
+
+        # Restore original message for other handlers
+        record.msg = original_msg
+
+        return result
+
+
+class PELogger:
+    """Per-PE logger with colored console and file output."""
+
+    _logger: Optional[logging.Logger] = None
+    _pe_id: int = -1
+    _level: int = logging.INFO
+
+    @classmethod
+    def init(cls, pe_id: int, level: str = "INFO", logs_dir: str = "logs"):
+        """
+        Initialize logger for this PE.
+
+        Args:
+            pe_id: Process element ID
+            level: Log level (TRACE, DEBUG, INFO, WARN, ERROR)
+            logs_dir: Directory for log files
+        """
+        cls._pe_id = pe_id
+
+        # Convert level string to logging level
+        level_map = {
+            "TRACE": logging.DEBUG - 5,  # Custom level below DEBUG
+            "DEBUG": logging.DEBUG,
+            "INFO": logging.INFO,
+            "SUMMARY": logging.INFO,
+            "WARN": logging.WARNING,
+            "WARNING": logging.WARNING,
+            "ERROR": logging.ERROR,
+            "CRITICAL": logging.CRITICAL,
+        }
+        cls._level = level_map.get(level.upper(), logging.INFO)
+
+        # Create logs directory if it doesn't exist
+        os.makedirs(logs_dir, exist_ok=True)
+
+        # Create logger
+        logger_name = f"PE_{pe_id}"
+        cls._logger = logging.getLogger(logger_name)
+        cls._logger.setLevel(cls._level)
+        cls._logger.propagate = False
+
+        # Remove existing handlers to avoid duplicates
+        cls._logger.handlers.clear()
+
+        # 1. Console handler with color
+        console_handler = logging.StreamHandler()
+        console_handler.setLevel(cls._level)
+        console_format = "[PE %d] [%%(asctime)s] [%%(levelname)s] %%(message)s" % pe_id
+        console_formatter = ColoredFormatter(console_format, pe_id, use_color=True)
+        console_handler.setFormatter(console_formatter)
+        cls._logger.addHandler(console_handler)
+
+        # 2. File handler without color
+        log_filename = os.path.join(logs_dir, f"pe_{pe_id}.log")
+        file_handler = logging.FileHandler(log_filename, mode="w")
+        file_handler.setLevel(cls._level)
+        file_format = "[PE %d] [%%(asctime)s] [%%(levelname)s] %%(message)s" % pe_id
+        file_formatter = ColoredFormatter(file_format, pe_id, use_color=False)
+        file_handler.setFormatter(file_formatter)
+        cls._logger.addHandler(file_handler)
+
+    @classmethod
+    def set_level(cls, level: str):
+        """Set the logging level."""
+        level_map = {
+            "TRACE": logging.DEBUG - 5,
+            "DEBUG": logging.DEBUG,
+            "INFO": logging.INFO,
+            "SUMMARY": logging.INFO,
+            "WARN": logging.WARNING,
+            "WARNING": logging.WARNING,
+            "ERROR": logging.ERROR,
+            "CRITICAL": logging.CRITICAL,
+        }
+        cls._level = level_map.get(level.upper(), logging.INFO)
+        if cls._logger:
+            cls._logger.setLevel(cls._level)
+            for handler in cls._logger.handlers:
+                handler.setLevel(cls._level)
+
+    @classmethod
+    def trace(cls, msg: str):
+        """Log at TRACE level (most detailed)."""
+        if cls._logger:
+            cls._logger.log(logging.DEBUG - 5, msg)
+
+    @classmethod
+    def debug(cls, msg: str):
+        """Log at DEBUG level."""
+        if cls._logger:
+            cls._logger.debug(msg)
+
+    @classmethod
+    def info(cls, msg: str):
+        """Log at INFO level."""
+        if cls._logger:
+            cls._logger.info(msg)
+
+    @classmethod
+    def summary(cls, msg: str):
+        """Log summary information (INFO level with [SUMMARY] prefix)."""
+        if cls._logger:
+            cls._logger.info(f"[SUMMARY] {msg}")
+
+    @classmethod
+    def warn(cls, msg: str):
+        """Log at WARNING level."""
+        if cls._logger:
+            cls._logger.warning(msg)
+
+    @classmethod
+    def warning(cls, msg: str):
+        """Log at WARNING level (alias for warn)."""
+        cls.warn(msg)
+
+    @classmethod
+    def error(cls, msg: str):
+        """Log at ERROR level."""
+        if cls._logger:
+            cls._logger.error(msg)
+
+    @classmethod
+    def critical(cls, msg: str):
+        """Log at CRITICAL level."""
+        if cls._logger:
+            cls._logger.critical(msg)
+
+    @classmethod
+    def shutdown(cls):
+        """Shutdown the logger and flush all handlers."""
+        if cls._logger:
+            for handler in cls._logger.handlers:
+                handler.flush()
+                handler.close()
+            cls._logger.handlers.clear()
+            cls._logger = None
diff --git a/megatron/core/resharding/nvshmem_copy_service/memory/__init__.py b/megatron/core/resharding/nvshmem_copy_service/memory/__init__.py
new file mode 100644
index 00000000000..5cd8aac704b
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/memory/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""Memory management utilities for NVSHMEM operations."""
+
+from .double_buffer_manager import DoubleBufferManager
+from .tensor_pointer_utils import TensorPointerExtractor
+
+__all__ = ["DoubleBufferManager", "TensorPointerExtractor"]
diff --git a/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py b/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py
new file mode 100644
index 00000000000..079b2c17610
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/memory/double_buffer_manager.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Double buffer management for NVSHMEM symmetric memory.
+
+Manages send and receive buffers with double-buffering for pipelined communication.
+"""
+
+try:
+    import nvshmem.core.interop.torch
+
+    HAVE_NVSHMEM = True
+except ImportError:
+    HAVE_NVSHMEM = False
+
+import torch
+
+from ..nvshmem_types import MAX_SEGMENT_SIZE
+
+
+class DoubleBufferManager:
+    """Manages double-buffered NVSHMEM symmetric buffers for send/receive operations."""
+
+    def __init__(self, slot_size: int = MAX_SEGMENT_SIZE):
+        """
+        Initialize buffer manager.
+
+        Args:
+            slot_size: Size of each buffer slot in bytes (default: 256MB)
+        """
+        self.slot_size = slot_size
+        self.send_slots = [None, None]
+        self.recv_slots = [None, None]
+
+    def allocate(self) -> None:
+        """Allocate NVSHMEM symmetric buffers for double-buffering."""
+        if not HAVE_NVSHMEM:
+            raise RuntimeError(
+                "nvshmem.core.interop.torch is not available. "
+                "Please install nvshmem to use DoubleBufferManager."
+            )
+
+        for i in range(2):
+            self.send_slots[i] = nvshmem.core.interop.torch.bytetensor(
+                (self.slot_size,), dtype=torch.uint8
+            )
+            self.recv_slots[i] = nvshmem.core.interop.torch.bytetensor(
+                (self.slot_size,), dtype=torch.uint8
+            )
+            # Zero out buffers
+            self.send_slots[i].zero_()
+            self.recv_slots[i].zero_()
+
+    def get_send_slot(self, iteration: int):
+        """
+        Get send buffer for given iteration.
+
+        Args:
+            iteration: Iteration number
+
+        Returns:
+            NVSHMEM tensor for sending
+        """
+        return self.send_slots[iteration % 2]
+
+    def get_recv_slot(self, iteration: int):
+        """
+        Get receive buffer for given iteration.
+
+        Args:
+            iteration: Iteration number
+
+        Returns:
+            NVSHMEM tensor for receiving
+        """
+        return self.recv_slots[iteration % 2]
+
+    def free(self) -> None:
+        """Free NVSHMEM symmetric buffers."""
+        for i in range(2):
+            if self.send_slots[i] is not None:
+                nvshmem.core.interop.torch.free_tensor(self.send_slots[i])
+                self.send_slots[i] = None
+            if self.recv_slots[i] is not None:
+                nvshmem.core.interop.torch.free_tensor(self.recv_slots[i])
+                self.recv_slots[i] = None
diff --git a/megatron/core/resharding/nvshmem_copy_service/memory/tensor_pointer_utils.py b/megatron/core/resharding/nvshmem_copy_service/memory/tensor_pointer_utils.py
new file mode 100644
index 00000000000..ee250618ee7
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/memory/tensor_pointer_utils.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Utilities for extracting data pointers from different tensor types.
+
+Supports PyTorch tensors, CuPy arrays, and raw integer pointers.
+"""
+
+from typing import Any
+
+import torch
+
+
+class TensorPointerExtractor:
+    """Extract memory pointers from various tensor types."""
+
+    @staticmethod
+    def get_pointer(tensor: Any) -> int:
+        """
+        Extract the data pointer from a tensor.
+
+        Args:
+            tensor: Can be torch.Tensor, CuPy array, or raw int pointer
+
+        Returns:
+            int: Memory address of the tensor data
+
+        Examples:
+
+            >>> import torch
+
+            >>> t = torch.zeros(100, device='cuda')
+
+            >>> ptr = TensorPointerExtractor.get_pointer(t)
+
+            >>> isinstance(ptr, int)
+
+            True
+        """
+        if isinstance(tensor, torch.Tensor):
+            return tensor.data_ptr()
+        elif hasattr(tensor, "data"):  # CuPy array
+            return tensor.data.ptr
+        else:  # Assume raw integer pointer
+            return tensor
diff --git a/megatron/core/resharding/nvshmem_copy_service/nvshmem_types.py b/megatron/core/resharding/nvshmem_copy_service/nvshmem_types.py
new file mode 100644
index 00000000000..731cace0502
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/nvshmem_types.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from dataclasses import dataclass, field
+from typing import Any, List
+
+# Constants
+MAX_SEGMENT_SIZE = 256 * 1024 * 1024  # 256MB
+MAX_TASKS_PER_BATCH = 10000
+
+
+@dataclass
+class SendRequest:
+    """Container for a send operation request."""
+
+    task_id: int
+    src_tensor: Any  # cupy.ndarray or pointer
+    src_pos: int
+    size: int
+    dest_pe: int
+
+
+@dataclass
+class ReceiveRequest:
+    """Container for a receive operation request."""
+
+    task_id: int
+    dest_tensor: Any  # cupy.ndarray or pointer
+    dest_pos: int
+    size: int
+    src_pe: int
+
+
+@dataclass
+class WorkloadGroup:
+    """Container for a group of send requests to a specific destination PE."""
+
+    dest_pe: int
+    tasks: List[SendRequest] = field(default_factory=list)
+    total_size: int = 0
+
+
+@dataclass
+class ScheduledBatch:
+    """Metadata for a scheduled communication batch."""
+
+    src_pe: int
+    dest_pe: int
+    batch_index: int
+    iteration: int
+    # Metadata for GPU execution
+    gpu_plan: Any = None  # Placeholder for GPU-resident plan
+    tasks: List[SendRequest] = field(default_factory=list)
+    total_size: int = 0
+    tasks_summary: Any = None  # WorkloadSummary
+
+
+@dataclass
+class WorkloadSummary:
+    """Summary of a workload group for communication with other PEs."""
+
+    total_size: int
+    task_ids: List[int]
+    task_sizes: List[int]
+
+
+@dataclass
+class TransferMetadata:
+    """GPU-resident metadata for communication tasks."""
+
+    ptrs: Any  # cupy array of uint64 (pointers)
+    sizes: Any  # cupy array of uint64 (sizes)
+    num_tasks: int
+    total_size: int
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/__init__.py b/megatron/core/resharding/nvshmem_copy_service/planning/__init__.py
new file mode 100644
index 00000000000..9df0b3ac318
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""Planning components for task segmentation, workload packing, and scheduling."""
+
+from .communication_scheduler import CommunicationScheduler
+from .gpu_execution_planner import GPUExecutionPlanner
+from .task_segmenter import TaskSegmenter
+from .workload_packer import WorkloadPacker
+
+__all__ = ["CommunicationScheduler", "GPUExecutionPlanner", "TaskSegmenter", "WorkloadPacker"]
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py b/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py
new file mode 100644
index 00000000000..842f2b9d300
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/communication_scheduler.py
@@ -0,0 +1,239 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Dict, List, Tuple
+
+from ..logger import PELogger
+from ..nvshmem_types import ScheduledBatch, WorkloadGroup, WorkloadSummary
+
+
+class CommunicationScheduler:
+    """
+    Builds a conflict-free, iteration-based schedule for communication.
+    Ensures that in any given iteration, a PE is not overloaded.
+    Uses greedy first-fit scheduling algorithm.
+    """
+
+    def __init__(self):
+        self.num_iterations = 0
+
+    def build_schedule(
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+    ) -> Tuple[Dict[int, List[ScheduledBatch]], Dict[Tuple[int, int, int], WorkloadSummary]]:
+        """
+        Main scheduling method.
+        1. Exchanges workload info with other PEs.
+        2. Assigns batches to iterations.
+        3. Returns:
+           - local schedule (iteration -> list of batches)
+           - global workload summaries (key: (src, dest, batch_idx) -> summary)
+        """
+        total_local_batches = sum(len(groups) for groups in workloads.values())
+        PELogger.info(f"Building schedule: {total_local_batches} local batches, {n_pes} PEs")
+
+        # Step 1: Collect all batches across all PE pairs
+        PELogger.debug("Collecting batches from all PEs...")
+        all_batches = self._collect_all_batches(workloads, my_pe, n_pes)
+        PELogger.debug(f"Collected {len(all_batches)} total batches globally")
+
+        # Step 2: Assign batches to iterations using greedy conflict-free algorithm
+        PELogger.debug("Assigning batches to iterations using greedy conflict-free algorithm...")
+        self._assign_iterations(all_batches)
+        PELogger.info(f"Schedule built: {self.num_iterations} iterations")
+
+        # Step 3: Exchange detailed workload summaries (Task IDs/Sizes)
+        # This is needed for receivers to know what tasks are in each batch
+        PELogger.debug("Exchanging workload summaries...")
+        global_summaries = self._exchange_workload_summaries(workloads, my_pe, n_pes)
+        PELogger.debug(f"Exchanged {len(global_summaries)} workload summaries")
+
+        # Step 4: Build schedule map for this PE
+        my_batches = [b for b in all_batches if b.src_pe == my_pe or b.dest_pe == my_pe]
+        my_batches.sort(key=lambda x: x.iteration)
+
+        final_schedule: Dict[int, List[ScheduledBatch]] = {}
+        for b in my_batches:
+            final_schedule.setdefault(b.iteration, []).append(b)
+
+        return final_schedule, global_summaries
+
+    def _collect_all_batches(
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+    ) -> List[ScheduledBatch]:
+        """
+        Exchanges batch counts and details with all PEs to build a global view.
+        Uses torch.distributed for reliable communication.
+        """
+        import torch.distributed as dist
+
+        # Build local batch list
+        local_batches: List[Tuple[int, int, int]] = []
+        for dest_pe, groups in workloads.items():
+            if dest_pe == my_pe:
+                continue
+            for i, _ in enumerate(groups):
+                local_batches.append((my_pe, dest_pe, i))  # (src, dest, batch_idx)
+
+        PELogger.debug(f"  Local batch count: {len(local_batches)}")
+        PELogger.debug(f"  Local batches: {local_batches}")
+
+        # Gather all batches from all PEs using torch.distributed
+        all_batches_list: List[List[Tuple[int, int, int]] | None] = [None] * n_pes
+        dist.all_gather_object(all_batches_list, local_batches)
+
+        # Flatten into global batch list
+        global_batches: List[ScheduledBatch] = []
+        for pe_batches in all_batches_list:
+            if pe_batches is None:
+                continue
+            for src, dest, idx in pe_batches:
+                global_batches.append(
+                    ScheduledBatch(src_pe=src, dest_pe=dest, batch_index=idx, iteration=-1)
+                )
+
+        PELogger.debug(f"  Global batches collected: {len(global_batches)} total")
+
+        # Group by source for readability
+        batches_by_src: Dict[int, List[Tuple[int, int]]] = {}
+        for b in global_batches:
+            batches_by_src.setdefault(b.src_pe, []).append((b.dest_pe, b.batch_index))
+        for src_pe in sorted(batches_by_src.keys()):
+            PELogger.debug(f"    PE {src_pe} sends to: {batches_by_src[src_pe]}")
+
+        return global_batches
+
+    def _assign_iterations(self, batches: List[ScheduledBatch]):
+        """
+        Greedy first-fit scheduling algorithm.
+
+        Assigns batches to iterations using simple greedy first-fit.
+        Processes batches in sorted order and assigns each to the first
+        available iteration with no conflicts.
+        """
+        self.num_iterations = 0
+
+        # Calculate degree (conflict count) for each batch
+        def calc_degree(batch: ScheduledBatch, all_batches: List[ScheduledBatch]) -> int:
+            """Count how many other batches conflict with this batch."""
+            conflicts = 0
+            batch_pes = {batch.src_pe, batch.dest_pe}
+            for other in all_batches:
+                if other is batch:
+                    continue
+                other_pes = {other.src_pe, other.dest_pe}
+                # Conflict if they share any PE
+                if batch_pes & other_pes:
+                    conflicts += 1
+            return conflicts
+
+        def has_conflict(batch: ScheduledBatch, iteration_state: Dict) -> bool:
+            """
+            Check if a batch conflicts with an iteration's current PE usage.
+
+            A batch conflicts if either its source or destination PE is already
+            being used (as sender or receiver) in the iteration.
+
+            Args:
+                batch: The batch to check
+                iteration_state: Dict with 'src_pes' and 'dst_pes' sets
+
+            Returns:
+                True if there's a conflict, False if the batch can be scheduled
+            """
+            return (
+                batch.src_pe in iteration_state['src_pes']
+                or batch.src_pe in iteration_state['dst_pes']
+                or batch.dest_pe in iteration_state['src_pes']
+                or batch.dest_pe in iteration_state['dst_pes']
+            )
+
+        # Sort batches: process batches with more potential conflicts first
+        # This heuristic (largest-degree-first) often produces better colorings
+        # Sort by degree (descending), then total_size (descending) for tie-breaking
+        batches.sort(key=lambda b: (-calc_degree(b, batches), -b.total_size))
+
+        # Track which PEs are busy (sending or receiving) in each iteration
+        # iteration -> {src_pes: set, dst_pes: set}
+        iteration_usage = []
+
+        for batch in batches:
+            # Find first iteration where this batch fits (no conflicts)
+            assigned = False
+            for iter_idx in range(len(iteration_usage)):
+                if not has_conflict(batch, iteration_usage[iter_idx]):
+                    # No conflict - assign to this iteration
+                    batch.iteration = iter_idx
+                    iteration_usage[iter_idx]['src_pes'].add(batch.src_pe)
+                    iteration_usage[iter_idx]['dst_pes'].add(batch.dest_pe)
+                    assigned = True
+                    PELogger.debug(
+                        f"  Assigned batch ({batch.src_pe} → {batch.dest_pe}, "
+                        f"idx={batch.batch_index}) to iteration {iter_idx}"
+                    )
+                    break
+
+            if not assigned:
+                # Need a new iteration
+                new_iter = len(iteration_usage)
+                batch.iteration = new_iter
+                iteration_usage.append({'src_pes': {batch.src_pe}, 'dst_pes': {batch.dest_pe}})
+                PELogger.debug(
+                    f"  Assigned batch ({batch.src_pe} → {batch.dest_pe}, "
+                    f"idx={batch.batch_index}) to NEW iteration {new_iter}"
+                )
+
+        self.num_iterations = len(iteration_usage)
+        PELogger.info(
+            f"Greedy scheduling: {len(batches)} batches → {self.num_iterations} iterations"
+        )
+
+    def _exchange_workload_summaries(
+        self, workloads: Dict[int, List[WorkloadGroup]], my_pe: int, n_pes: int
+    ) -> Dict[Tuple[int, int, int], WorkloadSummary]:
+        """
+        Exchange detailed workload content using torch.distributed.
+        Simple and reliable - no NVSHMEM symmetric memory issues.
+        """
+        import torch.distributed as dist
+
+        # Build local summaries as a simple dict:
+        # (src, dest, batch_idx) -> {total_size, task_ids, task_sizes}
+        local_summaries: Dict[Tuple[int, int, int], Dict[str, object]] = {}
+        batch_count = 0
+        total_tasks = 0
+
+        for dest_pe, groups in workloads.items():
+            if dest_pe == my_pe:
+                continue
+            for batch_idx, group in enumerate(groups):
+                key = (my_pe, dest_pe, batch_idx)
+                local_summaries[key] = {
+                    "total_size": group.total_size,
+                    "task_ids": [t.task_id for t in group.tasks],
+                    "task_sizes": [t.size for t in group.tasks],
+                }
+                batch_count += 1
+                total_tasks += len(group.tasks)
+
+        PELogger.debug(f"  Local summaries: {batch_count} batches, {total_tasks} tasks")
+
+        # Gather all summaries from all PEs using torch.distributed
+        all_summaries_list: List[Dict[Tuple[int, int, int], Dict[str, object]] | None] = [
+            None
+        ] * n_pes
+        dist.all_gather_object(all_summaries_list, local_summaries)
+
+        # Merge into global map
+        global_map: Dict[Tuple[int, int, int], WorkloadSummary] = {}
+        for pe_summaries in all_summaries_list:
+            if pe_summaries is None:
+                continue
+            for key, data in pe_summaries.items():
+                summary = WorkloadSummary(
+                    total_size=int(data["total_size"]),
+                    task_ids=list(data["task_ids"]),
+                    task_sizes=list(data["task_sizes"]),
+                )
+                global_map[key] = summary
+
+        PELogger.debug(f"  Exchanged {len(global_map)} workload summaries")
+        return global_map
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/gpu_execution_planner.py b/megatron/core/resharding/nvshmem_copy_service/planning/gpu_execution_planner.py
new file mode 100644
index 00000000000..68c4d11d7e5
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/gpu_execution_planner.py
@@ -0,0 +1,222 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+GPU execution planning for pack/unpack operations.
+
+Converts high-level task descriptions into GPU-ready metadata
+(pointer arrays, sizes, chunking) for kernel execution.
+"""
+
+from typing import Dict, List, Optional, Tuple
+
+try:
+    import cupy as cp
+
+    HAVE_CUPY = True
+except ImportError:
+    HAVE_CUPY = False
+
+import torch
+
+from ..logger import PELogger
+from ..memory.tensor_pointer_utils import TensorPointerExtractor
+from ..nvshmem_types import ReceiveRequest, ScheduledBatch
+
+
+class GPUExecutionPlanner:
+    """Plans GPU kernel execution by building pointer arrays and metadata."""
+
+    def __init__(self):
+        self.tensor_utils = TensorPointerExtractor()
+        self.CHUNK_SIZE = 128 * 1024  # 128KB chunks
+
+    def create_gpu_plans(
+        self,
+        iter_schedules: List[Dict[str, Optional[ScheduledBatch]]],
+        send_slots: List,
+        recv_slots: List,
+        receive_requests: List[ReceiveRequest],
+    ) -> None:
+        """
+        Build GPU execution plans for all iterations.
+
+        Modifies iter_schedules in-place by adding gpu_plan to each batch.
+
+        Args:
+            iter_schedules: List of iteration schedules (dicts with 'send' and 'recv')
+            send_slots: List of send buffer slots
+            recv_slots: List of receive buffer slots
+            receive_requests: List of all receive requests for matching
+        """
+        if not HAVE_CUPY:
+            raise RuntimeError(
+                "cupy is not available. Please install cupy to use GPUExecutionPlanner."
+            )
+
+        PELogger.debug(f"Creating GPU plans for {len(iter_schedules)} iterations")
+        for i, sched in enumerate(iter_schedules):
+            send_batch = sched["send"]
+            if send_batch:
+                # Build Pack Metadata
+                ptrs: List[int] = []
+                positions: List[int] = []
+                sizes: List[int] = []
+
+                for t in send_batch.tasks:
+                    # Extract pointer from tensor
+                    ptr = self.tensor_utils.get_pointer(t.src_tensor)
+                    ptrs.append(ptr)
+                    positions.append(t.src_pos)
+                    sizes.append(t.size)
+
+                # Plan kernel args for packing
+                send_batch.gpu_plan = self._plan_kernel_args(
+                    ptrs, positions, sizes, is_pack=True, buffer_base=send_slots[i % 2].data_ptr()
+                )
+                task_ids = [t.task_id for t in send_batch.tasks]
+                PELogger.debug(
+                    f"  Iter {i} send plan: {len(send_batch.tasks)} tasks → "
+                    f"PE {send_batch.dest_pe}, {send_batch.total_size} bytes"
+                )
+                displayed_ids = task_ids[:10] if len(task_ids) <= 10 else task_ids[:10] + ["..."]
+                PELogger.debug(f"    Send task IDs: {displayed_ids}")
+
+            recv_batch = sched["recv"]
+            if recv_batch:
+                # Build Unpack Metadata
+                summary = recv_batch.tasks_summary
+
+                # Skip if no summary available (shouldn't happen in normal operation)
+                if summary is None:
+                    PELogger.error(
+                        f"Iter {i}: recv batch from PE {recv_batch.src_pe} has no "
+                        "tasks_summary - UNPACK WILL BE SKIPPED!"
+                    )
+                    recv_batch.gpu_plan = None
+                    continue
+
+                PELogger.debug(
+                    f"  Iter {i} recv from PE {recv_batch.src_pe}: "
+                    f"{len(summary.task_ids)} tasks in summary"
+                )
+
+                ptrs = []
+                positions = []
+                sizes = []
+
+                # Create fast lookup map for receive requests
+                relevant_reqs: Dict[int, ReceiveRequest] = {
+                    r.task_id: r for r in receive_requests if r.src_pe == recv_batch.src_pe
+                }
+
+                # Match summary tasks with receive requests
+                matched_task_ids: List[int] = []
+                unmatched_task_ids: List[int] = []
+                for t_id, t_size in zip(summary.task_ids, summary.task_sizes):
+                    if t_id in relevant_reqs:
+                        req = relevant_reqs[t_id]
+                        ptr = self.tensor_utils.get_pointer(req.dest_tensor)
+                        ptrs.append(ptr)
+                        positions.append(req.dest_pos)
+                        sizes.append(t_size)  # Use sender's size
+                        matched_task_ids.append(t_id)
+                    else:
+                        unmatched_task_ids.append(t_id)
+                        PELogger.error(
+                            f"Iter {i}: Unexpected task {t_id} from PE "
+                            f"{recv_batch.src_pe} - no matching recv request!"
+                        )
+
+                if unmatched_task_ids:
+                    PELogger.error(
+                        f"  Iter {i}: {len(unmatched_task_ids)} unmatched tasks "
+                        f"from PE {recv_batch.src_pe}: {unmatched_task_ids[:10]}"
+                    )
+
+                # Plan kernel args for unpacking
+                recv_batch.gpu_plan = self._plan_kernel_args(
+                    ptrs, positions, sizes, is_pack=False, buffer_base=recv_slots[i % 2].data_ptr()
+                )
+
+                if recv_batch.gpu_plan is None:
+                    PELogger.error(
+                        f"  Iter {i} recv plan: FAILED - no gpu_plan created for "
+                        f"{len(sizes)} tasks from PE {recv_batch.src_pe}"
+                    )
+                else:
+                    PELogger.debug(
+                        f"  Iter {i} recv plan: {len(sizes)} tasks ← "
+                        f"PE {recv_batch.src_pe}, {recv_batch.total_size} bytes"
+                    )
+                    displayed_recv_ids = (
+                        matched_task_ids[:10]
+                        if len(matched_task_ids) <= 10
+                        else matched_task_ids[:10] + ["..."]
+                    )
+                    PELogger.debug(f"    Recv task IDs: {displayed_recv_ids}")
+
+    def _plan_kernel_args(
+        self,
+        ptrs: List[int],
+        positions: List[int],
+        sizes: List[int],
+        is_pack: bool,
+        buffer_base: int,
+    ) -> Optional[Tuple[object, object, object, int]]:
+        """
+        Generate GPU-ready pointer arrays for kernel execution.
+
+        Applies 128KB chunking to break large transfers into smaller pieces.
+
+        Args:
+            ptrs: List of tensor data pointers
+            positions: List of positions within tensors
+            sizes: List of transfer sizes
+            is_pack: True for pack (user->buffer), False for unpack (buffer->user)
+            buffer_base: Base pointer of the buffer
+
+        Returns:
+            Tuple of (cp_src_addrs, cp_dst_addrs, cp_sizes, num_chunks) as
+            CuPy arrays, or None if no work.
+        """
+        h_src_addrs: List[int] = []
+        h_dst_addrs: List[int] = []
+        h_sizes: List[int] = []
+
+        packed_offset = 0
+
+        for ptr, pos, size in zip(ptrs, positions, sizes):
+            num_chunks = (size + self.CHUNK_SIZE - 1) // self.CHUNK_SIZE
+
+            for c in range(num_chunks):
+                chunk_offset = c * self.CHUNK_SIZE
+                chunk_size = min(self.CHUNK_SIZE, size - chunk_offset)
+
+                if is_pack:
+                    # Pack: user tensor -> buffer
+                    h_src_addrs.append(ptr + pos + chunk_offset)
+                    h_dst_addrs.append(buffer_base + packed_offset + chunk_offset)
+                else:
+                    # Unpack: buffer -> user tensor
+                    h_src_addrs.append(buffer_base + packed_offset + chunk_offset)
+                    h_dst_addrs.append(ptr + pos + chunk_offset)
+
+                h_sizes.append(chunk_size)
+
+            packed_offset += size
+
+        total_chunks = len(h_sizes)
+        if total_chunks == 0:
+            return None
+
+        # Move to GPU using PyTorch, then convert to CuPy for kernel launching
+        d_src_addrs = torch.tensor(h_src_addrs, dtype=torch.int64, device="cuda")
+        d_dst_addrs = torch.tensor(h_dst_addrs, dtype=torch.int64, device="cuda")
+        d_sizes = torch.tensor(h_sizes, dtype=torch.int64, device="cuda")
+
+        # Convert to CuPy arrays (zero-copy) for kernel launching
+        cp_src_addrs = cp.asarray(d_src_addrs)
+        cp_dst_addrs = cp.asarray(d_dst_addrs)
+        cp_sizes = cp.asarray(d_sizes)
+
+        return (cp_src_addrs, cp_dst_addrs, cp_sizes, total_chunks)
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/task_segmenter.py b/megatron/core/resharding/nvshmem_copy_service/planning/task_segmenter.py
new file mode 100644
index 00000000000..fdeaea33ae5
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/task_segmenter.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+import logging
+from typing import List
+
+from ..nvshmem_types import MAX_SEGMENT_SIZE, ReceiveRequest, SendRequest
+
+logger = logging.getLogger(__name__)
+
+# Constants for ID encoding (from C++ implementation)
+REQUEST_ID_BASE = 1000000000
+SEGMENT_ID_MULTIPLIER = 1000
+MAX_REQUESTS = 1000000
+MAX_SEGMENTS_PER_REQUEST = 1000
+
+
+class TaskSegmenter:
+    """
+    Splits large tasks (>256MB) into smaller segments to fit
+    into the fixed-size communication slots.
+    """
+
+    def _encode_segment_id(self, task_id: int, segment_index: int) -> int:
+        return REQUEST_ID_BASE + (task_id * SEGMENT_ID_MULTIPLIER) + segment_index
+
+    def _calculate_num_segments(self, size: int) -> int:
+        return (size + MAX_SEGMENT_SIZE - 1) // MAX_SEGMENT_SIZE
+
+    def _validate_segmentation(self, task_id: int, size: int) -> bool:
+        num_segments = self._calculate_num_segments(size)
+        if num_segments > MAX_SEGMENTS_PER_REQUEST:
+            logger.error(
+                f"Error: Task {task_id} requires {num_segments} segments, "
+                f"exceeds max {MAX_SEGMENTS_PER_REQUEST}"
+            )
+            return False
+        if task_id >= MAX_REQUESTS:
+            logger.error(f"Error: Task ID {task_id} exceeds max {MAX_REQUESTS}")
+            return False
+        return True
+
+    def segment_send_request(self, req: SendRequest) -> List[SendRequest]:
+        """
+        Splits a single send request into multiple segments
+        if larger than MAX_SEGMENT_SIZE.
+        """
+        if req.size <= MAX_SEGMENT_SIZE:
+            return [req]
+
+        if not self._validate_segmentation(req.task_id, req.size):
+            raise ValueError(f"Task {req.task_id} validation failed")
+
+        num_segments = self._calculate_num_segments(req.size)
+        output_requests: List[SendRequest] = []
+
+        for i in range(num_segments):
+            segment_offset = i * MAX_SEGMENT_SIZE
+            segment_size = min(MAX_SEGMENT_SIZE, req.size - segment_offset)
+            segment_task_id = self._encode_segment_id(req.task_id, i)
+
+            new_req = SendRequest(
+                task_id=segment_task_id,
+                src_tensor=req.src_tensor,
+                src_pos=req.src_pos + segment_offset,
+                size=segment_size,
+                dest_pe=req.dest_pe,
+            )
+            output_requests.append(new_req)
+
+        return output_requests
+
+    def segment_receive_request(self, req: ReceiveRequest) -> List[ReceiveRequest]:
+        """
+        Splits a single receive request into multiple segments
+        if larger than MAX_SEGMENT_SIZE.
+        """
+        if req.size <= MAX_SEGMENT_SIZE:
+            return [req]
+
+        if not self._validate_segmentation(req.task_id, req.size):
+            raise ValueError(f"Task {req.task_id} validation failed")
+
+        num_segments = self._calculate_num_segments(req.size)
+        output_requests: List[ReceiveRequest] = []
+
+        for i in range(num_segments):
+            segment_offset = i * MAX_SEGMENT_SIZE
+            segment_size = min(MAX_SEGMENT_SIZE, req.size - segment_offset)
+            segment_task_id = self._encode_segment_id(req.task_id, i)
+
+            new_req = ReceiveRequest(
+                task_id=segment_task_id,
+                dest_tensor=req.dest_tensor,
+                dest_pos=req.dest_pos + segment_offset,
+                size=segment_size,
+                src_pe=req.src_pe,
+            )
+            output_requests.append(new_req)
+
+        return output_requests
diff --git a/megatron/core/resharding/nvshmem_copy_service/planning/workload_packer.py b/megatron/core/resharding/nvshmem_copy_service/planning/workload_packer.py
new file mode 100644
index 00000000000..1f2374bc187
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/planning/workload_packer.py
@@ -0,0 +1,89 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+from typing import Dict, List
+
+from ..logger import PELogger
+from ..nvshmem_types import MAX_SEGMENT_SIZE, MAX_TASKS_PER_BATCH, SendRequest, WorkloadGroup
+
+
+class WorkloadPacker:
+    """
+    Packs individual SendRequests into WorkloadGroups (batches)
+    destined for the same PE, respecting size limits.
+    """
+
+    def pack_workloads(
+        self, send_requests: List[SendRequest], n_pes: int
+    ) -> Dict[int, List[WorkloadGroup]]:
+        """
+        Groups requests by destination PE and packs them into batches.
+        Returns a map: dest_pe -> list of batches
+        """
+        PELogger.debug(f"Packing {len(send_requests)} send requests for {n_pes} PEs")
+        workloads: Dict[int, List[WorkloadGroup]] = {}
+
+        # Group requests by destination PE
+        tasks_by_dest: Dict[int, List[SendRequest]] = {}
+        for req in send_requests:
+            tasks_by_dest.setdefault(req.dest_pe, []).append(req)
+
+        # Pack tasks for each destination
+        for dest_pe in range(n_pes):
+            if dest_pe not in tasks_by_dest:
+                workloads[dest_pe] = []
+                PELogger.debug(f"  Dest PE {dest_pe}: 0 tasks → 0 batches")
+                continue
+
+            tasks = tasks_by_dest[dest_pe]
+            workloads[dest_pe] = self._pack_single_destination(tasks, dest_pe)
+
+            if workloads[dest_pe]:
+                total_size = sum(b.total_size for b in workloads[dest_pe])
+                PELogger.debug(
+                    f"  Dest PE {dest_pe}: {len(tasks)} tasks → "
+                    f"{len(workloads[dest_pe])} batches, {total_size} bytes total"
+                )
+            else:
+                PELogger.debug(
+                    f"  Dest PE {dest_pe}: {len(tasks)} tasks → 0 batches (empty after packing)"
+                )
+
+        return workloads
+
+    def _pack_single_destination(
+        self, tasks: List[SendRequest], dest_pe: int
+    ) -> List[WorkloadGroup]:
+        if not tasks:
+            return []
+
+        # Sort tasks by size (descending) for better bin packing efficiency
+        tasks.sort(key=lambda x: x.size, reverse=True)
+
+        batches: List[WorkloadGroup] = []
+        current_batch = WorkloadGroup(dest_pe=dest_pe, tasks=[], total_size=0)
+
+        for task in tasks:
+            # Check if adding this task would exceed batch constraints
+            would_exceed_size = current_batch.total_size + task.size > MAX_SEGMENT_SIZE
+            would_exceed_task_cap = len(current_batch.tasks) >= MAX_TASKS_PER_BATCH
+
+            if (would_exceed_size or would_exceed_task_cap) and current_batch.tasks:
+                # Finalize current batch
+                batches.append(current_batch)
+                task_first_10_string = ", ".join([str(t.task_id) for t in current_batch.tasks[:10]])
+                PELogger.debug(
+                    f"  Packed batch to PE {dest_pe} idx {len(batches) - 1}: "
+                    f"{task_first_10_string}... (total {len(current_batch.tasks)} tasks)"
+                )
+                # Start new batch
+                current_batch = WorkloadGroup(dest_pe=dest_pe, tasks=[], total_size=0)
+
+            # Add task to current batch
+            current_batch.tasks.append(task)
+            current_batch.total_size += task.size
+
+        # Add final batch if not empty
+        if current_batch.tasks:
+            batches.append(current_batch)
+
+        return batches
diff --git a/megatron/core/resharding/nvshmem_copy_service/service.py b/megatron/core/resharding/nvshmem_copy_service/service.py
new file mode 100644
index 00000000000..c538fd64f5b
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/service.py
@@ -0,0 +1,428 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Remote Copy Service - Main orchestrator for NVSHMEM-based GPU-to-GPU transfers.
+
+This service coordinates task segmentation, workload packing, scheduling,
+
+GPU resource management, and pipelined execution.
+"""
+
+from typing import Dict, List, Optional, Tuple
+
+try:
+    import nvshmem.core
+
+    HAVE_NVSHMEM = True
+except ImportError:
+    HAVE_NVSHMEM = False
+
+import torch.cuda.nvtx as nvtx
+
+from .core import GPUResourceManager, KernelLauncher, PipelineExecutor
+from .logger import PELogger
+from .memory import DoubleBufferManager
+from .nvshmem_types import ReceiveRequest, ScheduledBatch, SendRequest, WorkloadSummary
+from .planning import CommunicationScheduler, GPUExecutionPlanner, TaskSegmenter, WorkloadPacker
+
+
+class RemoteCopyService:
+    """
+    Main service for managing remote GPU-to-GPU data transfers.
+
+    Provides high-level API for registering transfers, scheduling,
+    and executing pipelined communication with NVSHMEM.
+    """
+
+    def __init__(self):
+        # Core components
+        self.gpu_resources = GPUResourceManager()
+        self.buffer_manager = DoubleBufferManager()
+        self.kernel_launcher = KernelLauncher()
+        self.pipeline_executor = None  # Created after init
+
+        # Planning components
+        self.task_segmenter = TaskSegmenter()
+        self.workload_packer = WorkloadPacker()
+        self.comm_scheduler = CommunicationScheduler()
+        self.gpu_planner = GPUExecutionPlanner()
+
+        # State
+        self.send_requests: List[SendRequest] = []
+        self.receive_requests: List[ReceiveRequest] = []
+        self.iter_schedules: Optional[List[Dict]] = None
+        self.num_iterations: int = 0
+
+        # Events for double-buffering
+        self.pack_events = []
+        self.unpack_events = []
+
+    @property
+    def my_pe(self) -> int:
+        """Get this PE's rank."""
+        return self.gpu_resources.my_pe
+
+    @property
+    def n_pes(self) -> int:
+        """Get total number of PEs."""
+        return self.gpu_resources.n_pes
+
+    @property
+    def device(self):
+        """Get CUDA device."""
+        return self.gpu_resources.device
+
+    @property
+    def initialized(self) -> bool:
+        """Check if service is initialized."""
+        return self.gpu_resources.initialized
+
+    def init(self, log_level: str = "INFO") -> None:
+        """
+        Initialize the service.
+
+        Sets up NVSHMEM, CUDA device, streams, buffers, and kernels.
+        Expects to be launched with torchrun.
+
+        Args:
+            log_level: Logging level (TRACE, DEBUG, INFO, WARN, ERROR)
+        """
+        if not HAVE_NVSHMEM:
+            raise RuntimeError(
+                "nvshmem.core is not available. Please install nvshmem to use NVSHMEMCopyService."
+            )
+
+        # Initialize GPU resources (NVSHMEM, device, streams)
+        self.gpu_resources.init()
+
+        # Initialize logger after PE ID is known
+        PELogger.init(self.my_pe, level=log_level)
+        PELogger.info(f"Initializing RemoteCopyService on PE {self.my_pe}/{self.n_pes}")
+
+        # Barrier to ensure ALL PEs finish NVSHMEM init before ANY PE starts buffer allocation
+        # buffer_manager.allocate() calls bytetensor() which is a collective operation
+        # Without this barrier, early PEs call bytetensor() while late PEs
+        # are still in init() -> deadlock
+        nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
+        self.gpu_resources.send_stream.sync()  # Ensure barrier completes on CPU
+
+        # Allocate double-buffered send/recv slots
+        self.buffer_manager.allocate()
+
+        # Barrier to ensure all PEs complete buffer allocation before proceeding
+        nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
+
+        PELogger.debug("Allocated double-buffered send/recv slots")
+
+        # Load CUDA kernels
+        self.kernel_launcher.load_kernels()
+        PELogger.debug("Loaded CUDA kernels")
+
+        # Cache CuPy stream wrappers for efficient kernel launching
+        self.kernel_launcher.set_streams(
+            self.gpu_resources.pack_stream, self.gpu_resources.unpack_stream
+        )
+        PELogger.debug("Cached CuPy stream wrappers")
+
+        # Create pipeline executor with dependencies
+        self.pipeline_executor = PipelineExecutor(
+            self.kernel_launcher, self.buffer_manager, self.my_pe
+        )
+
+        # Set streams on pipeline executor
+        self.pipeline_executor.set_streams(
+            self.gpu_resources.pack_stream,
+            self.gpu_resources.unpack_stream,
+            self.gpu_resources.send_stream,
+            self.gpu_resources.copy_stream,
+            self.gpu_resources.torch_pack_stream,
+            self.gpu_resources.torch_unpack_stream,
+            self.gpu_resources.torch_copy_stream,
+        )
+
+        # Synchronize all NVSHMEM streams before returning
+        # This ensures all barrier operations complete and streams are idle
+        # Without this, subsequent torch.cuda.synchronize() may hang waiting for pending work
+        self.gpu_resources.send_stream.sync()
+        self.gpu_resources.pack_stream.sync()
+        self.gpu_resources.unpack_stream.sync()
+        self.gpu_resources.copy_stream.sync()
+
+        PELogger.info("Initialization complete")
+
+    def register_send(
+        self, task_id: int, src_tensor, src_pos: int, size: int, dest_pe: int
+    ) -> None:
+        """
+        Register a send operation.
+
+        Args:
+            task_id: Unique task identifier
+            src_tensor: Source tensor (PyTorch/CuPy tensor or pointer)
+            src_pos: Starting position in source tensor
+            size: Number of bytes to send
+            dest_pe: Destination PE rank
+        """
+        if dest_pe >= self.n_pes or dest_pe < 0:
+            PELogger.error(f"Error: Invalid destination PE {dest_pe}")
+            return
+
+        req = SendRequest(task_id, src_tensor, src_pos, size, dest_pe)
+        self.send_requests.append(req)
+
+    def register_receive(
+        self, task_id: int, dest_tensor, dest_pos: int, size: int, src_pe: int
+    ) -> None:
+        """
+        Register a receive operation.
+
+        Args:
+            task_id: Unique task identifier
+            dest_tensor: Destination tensor (PyTorch/CuPy tensor or pointer)
+            dest_pos: Starting position in destination tensor
+            size: Number of bytes to receive
+            src_pe: Source PE rank
+        """
+        if src_pe >= self.n_pes or src_pe < 0:
+            PELogger.error(f"Error: Invalid source PE {src_pe}")
+            return
+
+        req = ReceiveRequest(task_id, dest_tensor, dest_pos, size, src_pe)
+        self.receive_requests.append(req)
+
+    def schedule(self) -> None:
+        """
+        Build execution schedule.
+
+        Can be called once and followed by multiple run() calls for
+        repeated execution with the same communication pattern.
+
+        Steps:
+        1. Segment large tasks into manageable chunks
+        2. Pack tasks into batches
+        3. Schedule batches to iterations (conflict-free)
+        4. Build GPU execution plans (pointer arrays, chunking)
+        5. Create synchronization events
+        """
+        if not self.initialized:
+            raise RuntimeError("RemoteCopyService not initialized")
+
+        PELogger.info(
+            f"Starting schedule: {len(self.send_requests)} send requests, "
+            f"{len(self.receive_requests)} receive requests"
+        )
+
+        # Step 1: Segment tasks (break large tasks into chunks)
+        PELogger.debug("Step 1: Segmenting tasks...")
+        orig_send_count = len(self.send_requests)
+        orig_recv_count = len(self.receive_requests)
+        self._segment_tasks()
+        PELogger.info(
+            f"Segmented: {orig_send_count} sends → {len(self.send_requests)} segments, "
+            f"{orig_recv_count} recvs → {len(self.receive_requests)} segments"
+        )
+
+        # Step 2: Pack tasks into workload groups
+        PELogger.debug("Step 2: Packing workloads...")
+        workloads = self.workload_packer.pack_workloads(self.send_requests, self.n_pes)
+        total_batches = sum(len(batches) for batches in workloads.values())
+        active_pes = sum(1 for batches in workloads.values() if batches)
+        PELogger.info(f"Packed: {total_batches} batches across {active_pes} destination PEs")
+
+        # Step 3: Schedule workloads to iterations
+        PELogger.debug("Step 3: Building communication schedule...")
+        schedule, global_summaries = self.comm_scheduler.build_schedule(
+            workloads, self.my_pe, self.n_pes
+        )
+
+        self.num_iterations = self.comm_scheduler.num_iterations
+        PELogger.info(f"Scheduled: {total_batches} batches → {self.num_iterations} iterations")
+
+        # Step 4: Prepare iteration schedules
+        PELogger.debug("Step 4: Preparing iteration schedules...")
+        self.iter_schedules = self._prepare_iter_schedules(
+            schedule, workloads, global_summaries, self.num_iterations
+        )
+
+        # Step 5: Build GPU execution plans
+        PELogger.debug("Step 5: Building GPU execution plans...")
+        self.gpu_planner.create_gpu_plans(
+            self.iter_schedules,
+            self.buffer_manager.send_slots,
+            self.buffer_manager.recv_slots,
+            self.receive_requests,
+        )
+
+        # Step 6: Create double-buffered events
+        PELogger.debug("Step 6: Creating synchronization events...")
+        self.pack_events, self.unpack_events = self.gpu_resources.create_events(num_events=2)
+        self.pipeline_executor.set_events(self.pack_events, self.unpack_events)
+
+        PELogger.info(f"Schedule complete: {self.num_iterations} iterations ready")
+
+    def run(self) -> None:
+        """
+        Execute the scheduled communication.
+
+        Can be called multiple times after a single schedule() call
+        to repeat the same communication pattern.
+        """
+        # import torch
+        # torch.save(self.send_requests, f"send_requests_{torch.distributed.get_rank()}.pt")
+        # torch.save(self.receive_requests, f"receive_requests_{torch.distributed.get_rank()}.pt")
+
+        if not self.initialized:
+            raise RuntimeError("RemoteCopyService not initialized")
+        if self.iter_schedules is None:
+            raise RuntimeError("Must call schedule() before run()")
+
+        PELogger.info(f"Starting execution: {self.num_iterations} iterations")
+
+        # Start timing
+        nvtx.range_push("RemoteCopyService.run_total")
+
+        # Global barrier before execution
+        PELogger.debug("Barrier: Synchronizing all PEs before execution")
+        nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
+        self.gpu_resources.send_stream.sync()
+
+        # Execute pipelined communication
+        nvtx.range_push("execute_pipeline")
+        self.pipeline_executor.execute_pipeline(self.iter_schedules, self.num_iterations)
+        nvtx.range_pop()  # execute_pipeline
+
+        # Global barrier after execution
+        PELogger.debug("Barrier: Synchronizing all PEs after pipeline")
+        nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
+
+        # Process same-PE transfers
+        self.pipeline_executor.process_self_moves(self.send_requests, self.receive_requests)
+
+        # End timing range
+        nvtx.range_pop()  # RemoteCopyService.run_total
+
+    def clear_requests(self) -> None:
+        """
+        Clear registered requests and schedule.
+
+        Call this before registering a new set of transfers.
+        """
+        self.send_requests = []
+        self.receive_requests = []
+        self.iter_schedules = None
+        self.num_iterations = 0
+        self.pack_events = []
+        self.unpack_events = []
+
+    def finalize(self) -> None:
+        """Cleanup resources."""
+        PELogger.info("Finalizing RemoteCopyService")
+
+        # Barrier to ensure all PEs are ready to finalize
+        try:
+            PELogger.debug("Barrier: Synchronizing all PEs before finalize")
+            nvshmem.core.barrier_all(stream=self.gpu_resources.send_stream)
+            self.gpu_resources.send_stream.sync()
+        except Exception as e:
+            PELogger.error(f"Error in final barrier: {e}")
+
+        # Free buffers
+        self.buffer_manager.free()
+
+        # Finalize GPU resources (this will call nvshmem.core.finalize internally)
+        self.gpu_resources.finalize()
+
+        PELogger.info("RemoteCopyService finalized")
+        PELogger.shutdown()
+
+    def _segment_tasks(self) -> None:
+        """Segment tasks into manageable chunks."""
+        new_sends: List[SendRequest] = []
+        for req in self.send_requests:
+            segments = self.task_segmenter.segment_send_request(req)
+            new_sends.extend(segments)
+            if len(segments) > 1:
+                PELogger.debug(
+                    f"  Segmented send task {req.task_id}: "
+                    f"{req.size} bytes → {len(segments)} segments"
+                )
+        self.send_requests = new_sends
+
+        new_recvs: List[ReceiveRequest] = []
+        for req in self.receive_requests:
+            segments = self.task_segmenter.segment_receive_request(req)
+            new_recvs.extend(segments)
+            if len(segments) > 1:
+                PELogger.debug(
+                    f"  Segmented recv task {req.task_id}: "
+                    f"{req.size} bytes → {len(segments)} segments"
+                )
+        self.receive_requests = new_recvs
+
+    def _prepare_iter_schedules(
+        self,
+        schedule_batches: Dict[int, List[ScheduledBatch]],
+        workloads: Dict[int, List],
+        global_summaries: Dict[Tuple[int, int, int], WorkloadSummary],
+        num_iterations: int,
+    ) -> List[Dict]:
+        """
+        Organize schedule into iteration-based structure.
+
+        Returns:
+            List of dicts with 'send' and 'recv' keys for each iteration
+        """
+        iter_schedules: List[Dict[str, Optional[ScheduledBatch]]] = []
+
+        for i in range(num_iterations):
+            sched: Dict[str, Optional[ScheduledBatch]] = {"send": None, "recv": None}
+
+            if i in schedule_batches:
+                batches = schedule_batches[i]
+
+                for b in batches:
+                    # Skip same-PE transfers (handled separately by process_self_moves)
+                    if b.src_pe == b.dest_pe:
+                        PELogger.debug(
+                            f"  Iter {i}: Skipping same-PE batch " f"({b.src_pe} → {b.dest_pe})"
+                        )
+                        continue
+
+                    if b.src_pe == self.my_pe:
+                        # This PE sends in this iteration
+                        b.tasks = workloads[b.dest_pe][b.batch_index].tasks
+                        b.total_size = workloads[b.dest_pe][b.batch_index].total_size
+                        sched["send"] = b
+                        PELogger.debug(
+                            f"  Iter {i}: Send to PE {b.dest_pe}, batch "
+                            f"{b.batch_index}, {len(b.tasks)} tasks, "
+                            f"{b.total_size} bytes"
+                        )
+
+                    elif b.dest_pe == self.my_pe:
+                        # This PE receives in this iteration
+                        key = (b.src_pe, b.dest_pe, b.batch_index)
+                        if key in global_summaries:
+                            summary = global_summaries[key]
+                            b.tasks_summary = summary
+                            b.total_size = summary.total_size
+                        else:
+                            PELogger.error(
+                                f"  Iter {i}: Missing workload summary for "
+                                f"recv from PE {b.src_pe}, batch {b.batch_index}"
+                            )
+                            PELogger.error(
+                                "  Available keys in global_summaries: "
+                                f"{list(global_summaries.keys())}"
+                            )
+                            b.tasks_summary = None
+                            b.total_size = 0
+                        sched["recv"] = b
+                        PELogger.debug(
+                            f"  Iter {i}: Recv from PE {b.src_pe}, batch "
+                            f"{b.batch_index}, {b.total_size} bytes"
+                        )
+
+            iter_schedules.append(sched)
+
+        return iter_schedules
diff --git a/megatron/core/resharding/nvshmem_copy_service/validation.py b/megatron/core/resharding/nvshmem_copy_service/validation.py
new file mode 100644
index 00000000000..fafb1321024
--- /dev/null
+++ b/megatron/core/resharding/nvshmem_copy_service/validation.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+"""
+Validation utilities for GPU-to-GPU communication.
+
+Provides deterministic data generation and validation for verifying
+
+correctness of communication operations."""
+
+from dataclasses import dataclass
+from typing import List
+
+import torch
+
+from .logger import PELogger
+
+
+@dataclass
+class ValidationResult:
+    """Result of validating a single task."""
+
+    task_id: int
+    size: int
+    passed: bool
+    src_pe: int = -1
+    mismatches: int = 0
+    first_mismatch_idx: int = -1
+    first_mismatch_expected: int = 0
+    first_mismatch_actual: int = 0
+    # Scheduling info - which batch/iteration this task was supposed to be handled
+    batch_index: int = -1
+    iteration: int = -1
+
+
+@dataclass
+class ValidationSummary:
+    """Summary of validation across all tasks."""
+
+    total_tasks: int
+    passed_tasks: int
+    failed_tasks: int
+    total_bytes: int
+    results: List[ValidationResult]
+
+    @property
+    def all_passed(self) -> bool:
+        """Check if all validated tasks passed."""
+        return self.failed_tasks == 0
+
+
+def generate_deterministic_data(task_id: int, size: int, device: str = "cuda") -> torch.Tensor:
+    """
+    Generate deterministic data pattern for a task.
+
+    Pattern: Each byte = (task_id * 31 + position) % 256
+    This creates a unique pattern per task that varies along the data.
+
+    Args:
+        task_id: Unique task identifier
+        size: Number of bytes to generate
+        device: Device to create tensor on ('cuda' or 'cpu')
+
+    Returns:
+        torch.Tensor of uint8 with deterministic pattern
+    """
+    positions = torch.arange(size, dtype=torch.int64, device=device)
+    pattern = ((task_id * 31 + positions) % 256).to(torch.uint8)
+    return pattern
+
+
+def validate_received_data(
+    task_id: int, tensor: torch.Tensor, size: int, src_pe: int = -1
+) -> ValidationResult:
+    """
+    Validate received data against expected deterministic pattern.
+
+    Args:
+        task_id: Task identifier to regenerate expected data
+        tensor: Received tensor to validate
+        size: Number of bytes to validate
+
+    Returns:
+        ValidationResult with pass/fail status and details
+    """
+    # Get the data slice to validate
+    recv_data = tensor[:size]
+
+    # Generate expected pattern on same device
+    expected = generate_deterministic_data(task_id, size, device=recv_data.device.type)
+
+    # Compare
+    mismatches_mask = recv_data != expected
+    num_mismatches = mismatches_mask.sum().item()
+
+    result = ValidationResult(
+        task_id=task_id,
+        size=size,
+        passed=(num_mismatches == 0),
+        src_pe=src_pe,
+        mismatches=num_mismatches,
+    )
+
+    if num_mismatches > 0:
+        # Find first mismatch for debugging
+        first_idx = mismatches_mask.nonzero(as_tuple=True)[0][0].item()
+        result.first_mismatch_idx = first_idx
+        result.first_mismatch_expected = expected[first_idx].item()
+        result.first_mismatch_actual = recv_data[first_idx].item()
+
+    return result
+
+
+def log_validation_summary(summary: ValidationSummary) -> None:
+    """Log validation summary."""
+    if summary.all_passed:
+        PELogger.info(
+            "Validation PASSED: %d/%d tasks, %d bytes validated",
+            summary.passed_tasks,
+            summary.total_tasks,
+            summary.total_bytes,
+        )
+    else:
+        PELogger.error(
+            "Validation FAILED: %d/%d tasks passed, %d failed",
+            summary.passed_tasks,
+            summary.total_tasks,
+            summary.failed_tasks,
+        )
+
+        # Group failures by source PE
+        failures_by_src = {}
+        for r in summary.results:
+            if not r.passed:
+                failures_by_src.setdefault(r.src_pe, []).append(r)
+
+        PELogger.error("  Failures by source PE:")
+        for src_pe in sorted(failures_by_src.keys()):
+            failed_tasks = failures_by_src[src_pe]
+            task_ids = [r.task_id for r in failed_tasks]
+            PELogger.error(
+                "    PE %d: %d failed tasks: %s",
+                src_pe,
+                len(failed_tasks),
+                task_ids[:15] if len(task_ids) <= 15 else task_ids[:15] + ["..."],
+            )
diff --git a/megatron/core/resharding/planner.py b/megatron/core/resharding/planner.py
index 31045fbfc01..f9d8752a26b 100644
--- a/megatron/core/resharding/planner.py
+++ b/megatron/core/resharding/planner.py
@@ -90,6 +90,7 @@ def _plan_multi_dim_lcm(
     d = descriptors[0]
     if my_global_rank not in d.dst_dim_ranks:
         return []
+
     src_shape = tuple(src_metadata.shape)
     dst_shape = tuple(dst_metadata.shape)
     dim = d.dim
@@ -174,8 +175,8 @@ def _finalize_dp_transfers(
 
     dst_shape = dst_metadata.shape
 
-    # Same DP layout - local copy
-    if src_dp_ranks == dst_dp_ranks:
+    # Same DP layout - local copy (only if this rank has the source parameter)
+    if src_dp_ranks == dst_dp_ranks and my_global_rank in src_dp_ranks:
         full_slice = tuple(slice(None) for _ in range(len(dst_shape)))
         return [(my_global_rank, full_slice, full_slice)]
 
@@ -183,7 +184,17 @@ def _finalize_dp_transfers(
     # better load balancing across source ranks. This ensures that destination
     # ranks are distributed across source ranks even when they have the same
     # position within their respective DP groups.
+    #
+    # In non-collocated mode, src_dp_ranks might include ranks that don't
+    # have the source model (e.g., idle ranks or destination ranks). Filter to only
+    # include the rank that provided this metadata (src_metadata.owner_rank).
+    # src_metadata was selected by select_src_metadata_balanced, so owner_rank is the
+    # actual source rank for this parameter.
+    actual_src_rank = src_metadata.owner_rank
     src_global_rank = src_dp_ranks[my_global_rank % len(src_dp_ranks)]
+    # Override with the actual source rank if the selected rank doesn't have the parameter
+    if src_global_rank != actual_src_rank:
+        src_global_rank = actual_src_rank
     full_slice = tuple(slice(None) for _ in range(len(dst_shape)))
     return [(src_global_rank, full_slice, full_slice)]
 
@@ -215,46 +226,63 @@ def build_centralized_reshard_plan(
 ) -> ReshardPlan:
     """
     Centralized planning: Rank 0 builds complete plan for all ranks, then scatters.
+
+    Supports None for src_module and/or dst_module to enable non-collocated mode:
+    - src_module=None: Rank doesn't have source model (destination-only)
+    - dst_module=None: Rank doesn't have destination model (source-only)
+    - Both provided: Rank has both models (collocated mode)
+
+    Each rank provides metadata only for the models it owns, including parallel group
+    membership (tensor_parallel_group_ranks, expert_parallel_group_ranks, etc.).
+    This metadata is sufficient for rank 0 to build correct transfer plans without
+    requiring dummy models.
     """
     my_global_rank = dist.get_rank()
     world_size = dist.get_world_size()
 
-    # Get process groups
-    src_pg = getattr(src_module, "pg_collection", None)
-    dst_pg = getattr(dst_module, "pg_collection", None)
-    if src_pg is None or dst_pg is None:
-        raise ValueError("Both modules must have pg_collection")
-
-    # Gather param metadata from all ranks
-    my_src_params = {name: p for name, p in src_module.named_parameters(recurse=True)}
-    my_dst_params = {name: p for name, p in dst_module.named_parameters(recurse=True)}
-
-    # Build PP layer prefix maps to be used for parameter name rewriting
-    src_layer_prefix_map = _build_layer_module_prefix_map(src_module)
-    dst_layer_prefix_map = _build_layer_module_prefix_map(dst_module)
-
-    my_src_metadata = [
-        extract_param_metadata(
-            p,
-            name,
-            my_global_rank,
-            src_pg,
-            num_experts=num_experts,
-            layer_module_prefix_map=src_layer_prefix_map,
-        )
-        for name, p in my_src_params.items()
-    ]
-    my_dst_metadata = [
-        extract_param_metadata(
-            p,
-            name,
-            my_global_rank,
-            dst_pg,
-            num_experts=num_experts,
-            layer_module_prefix_map=dst_layer_prefix_map,
-        )
-        for name, p in my_dst_params.items()
-    ]
+    # Extract metadata from source model if present
+    if src_module is not None:
+        src_pg = getattr(src_module, "pg_collection", None)
+        if src_pg is None:
+            raise ValueError("Source module must have pg_collection")
+        my_src_params = {name: p for name, p in src_module.named_parameters(recurse=True)}
+        src_layer_prefix_map = _build_layer_module_prefix_map(src_module)
+        my_src_metadata = [
+            extract_param_metadata(
+                p,
+                name,
+                my_global_rank,
+                src_pg,
+                num_experts=num_experts,
+                layer_module_prefix_map=src_layer_prefix_map,
+            )
+            for name, p in my_src_params.items()
+        ]
+    else:
+        # No source model on this rank - provide empty metadata
+        my_src_metadata = []
+
+    # Extract metadata from destination model if present
+    if dst_module is not None:
+        dst_pg = getattr(dst_module, "pg_collection", None)
+        if dst_pg is None:
+            raise ValueError("Destination module must have pg_collection")
+        my_dst_params = {name: p for name, p in dst_module.named_parameters(recurse=True)}
+        dst_layer_prefix_map = _build_layer_module_prefix_map(dst_module)
+        my_dst_metadata = [
+            extract_param_metadata(
+                p,
+                name,
+                my_global_rank,
+                dst_pg,
+                num_experts=num_experts,
+                layer_module_prefix_map=dst_layer_prefix_map,
+            )
+            for name, p in my_dst_params.items()
+        ]
+    else:
+        # No destination model on this rank - provide empty metadata
+        my_dst_metadata = []
 
     all_src_metadata_by_rank = [None] * world_size
     all_dst_metadata_by_rank = [None] * world_size
diff --git a/megatron/core/resharding/refit.py b/megatron/core/resharding/refit.py
index 491a42b9116..2a9e58ecc22 100644
--- a/megatron/core/resharding/refit.py
+++ b/megatron/core/resharding/refit.py
@@ -7,7 +7,10 @@
 - reshard_model_weights: transport-agnostic core; builds/caches plan and executes.
 """
 
-from typing import Any, Literal, Optional, Union
+from dataclasses import dataclass
+from typing import Any, Literal, Optional, Tuple, Union
+
+import torch
 
 from megatron.core import parallel_state
 from megatron.core.models.common.language_module.language_module import LanguageModule
@@ -17,9 +20,132 @@
 from .copy_services.base import CopyService
 from .copy_services.gloo_copy_service import GlooCopyService
 from .copy_services.nccl_copy_service import NCCLCopyService
+from .copy_services.nvshmem_copy_service import NVSHMEMCopyService
 
 # Supported refit backend names
-RefitBackendName = Literal["nccl", "gloo"]
+RefitBackendName = Literal["nccl", "gloo", "nvshmem"]
+
+
+@dataclass(frozen=True)
+class _PlanCacheKey:
+    """
+    Cache key for reshard plans.
+    """
+
+    rank: int
+    # Parallelism configuration: (TP, PP, EP, DP, expt_tp) or None for non-collocated ranks
+    src_config: Optional[Tuple[int, int, int, int, int]]
+    dst_config: Optional[Tuple[int, int, int, int, int]]
+    num_experts: Optional[int]
+
+
+def _get_config_tuple(core) -> Optional[Tuple[int, int, int, int, int]]:
+    """Extract (TP, PP, EP, DP, expt_tp) sizes from a model core.
+
+    Returns:
+        Tuple of (TP, PP, EP, DP, expt_tp) sizes, or None if core is None.
+        - TP: Tensor parallelism
+        - PP: Pipeline parallelism
+        - EP: Expert parallelism
+        - DP: Data parallelism
+        - expt_tp: Expert tensor parallelism
+    """
+    if core is None:
+        return None
+    pg = core.pg_collection
+    return (
+        len(torch.distributed.get_process_group_ranks(pg.tp)) if pg.tp else 1,
+        len(torch.distributed.get_process_group_ranks(pg.pp)) if pg.pp else 1,
+        len(torch.distributed.get_process_group_ranks(pg.ep)) if pg.ep else 1,
+        len(torch.distributed.get_process_group_ranks(pg.dp)) if pg.dp else 1,
+        (
+            len(torch.distributed.get_process_group_ranks(pg.expt_tp))
+            if hasattr(pg, 'expt_tp') and pg.expt_tp
+            else 1
+        ),
+    )
+
+
+def _build_plan_cache_key(src_core, tgt_core, num_experts: Optional[int]) -> _PlanCacheKey:
+    """Build cache key for reshard plan.
+
+    Args:
+        src_core: Source model core (or None for non-collocated destination/idle ranks)
+        tgt_core: Target model core (or None for non-collocated source/idle ranks)
+        num_experts: Number of MoE experts (or None for non-MoE models)
+
+    Returns:
+        Cache key that uniquely identifies this reshard configuration for this rank
+    """
+    rank = torch.distributed.get_rank()
+    src_config = _get_config_tuple(src_core)
+    dst_config = _get_config_tuple(tgt_core)
+    return _PlanCacheKey(
+        rank=rank, src_config=src_config, dst_config=dst_config, num_experts=num_experts
+    )
+
+
+# Module-level cache for refit services to avoid repeated allocations
+_service_cache: dict[str, CopyService] = {}
+_plan_cache: dict[_PlanCacheKey, Any] = {}
+
+
+def get_or_create_service(backend: RefitBackendName) -> CopyService:
+    """Get or create a cached CopyService instance for the given backend.
+
+    This avoids expensive repeated allocations (especially for NVSHMEM buffers)
+    when swap_model_weights is called multiple times with the same backend.
+    """
+    if backend in _service_cache:
+        return _service_cache[backend]
+
+    if backend == "nccl":
+        service = NCCLCopyService()
+    elif backend == "gloo":
+        service = GlooCopyService()
+    elif backend == "nvshmem":
+        service = NVSHMEMCopyService()
+    else:
+        raise ValueError(f"Unknown backend '{backend}'")
+
+    _service_cache[backend] = service
+    return service
+
+
+def clear_service_cache():
+    """Clear the cached refit services.
+
+    Call this if you need to invalidate the cache, for example when
+    reinitializing distributed state.
+
+    This properly finalizes services to free GPU buffers
+    before clearing the cache.
+    """
+    global _service_cache
+
+    # Finalize services to free resources for NVSHMEM backend
+    # NCCL/Gloo services have no cleanup needed
+    for backend_name, service in _service_cache.items():
+        if hasattr(service, '_remote') and hasattr(service._remote, 'finalize'):
+            service._remote.finalize()
+
+    _service_cache.clear()
+
+
+def clear_plan_cache():
+    """
+    Clear the cached refit plans.
+    """
+    global _plan_cache
+    _plan_cache.clear()
+
+
+def clear_all_caches():
+    """
+    Clear both service and plan caches.
+    """
+    clear_service_cache()
+    clear_plan_cache()
 
 
 def swap_model_weights(
@@ -37,15 +163,8 @@ def swap_model_weights(
         service = refit_method
         reshard_model_weights(src_model, target_model, service=service)
     elif isinstance(refit_method, str):
-        if refit_method == "nccl":
-            service = NCCLCopyService()
-            reshard_model_weights(src_model, target_model, service=service)
-        elif refit_method == "gloo":
-            # Debug / fallback backend: run refit over CPU/Gloo instead of NCCL.
-            service = GlooCopyService()
-            reshard_model_weights(src_model, target_model, service=service)
-        else:
-            raise ValueError(f"Unknown refit_method '{refit_method}'")
+        service = get_or_create_service(refit_method)
+        reshard_model_weights(src_model, target_model, service=service)
     else:
         raise TypeError("refit_method must be a str backend name or a CopyService instance")
 
@@ -53,33 +172,70 @@ def swap_model_weights(
 def reshard_model_weights(
     src_model: LanguageModule, target_model: LanguageModule, service: CopyService
 ):
-    """Reshard and copy model weights from ``src_model`` to ``target_model`` using ``service``."""
-    # Handle list-wrapped modules used throughout training utils
-    src_lm = src_model[0] if isinstance(src_model, (list, tuple)) else src_model
-    tgt_lm = target_model[0] if isinstance(target_model, (list, tuple)) else target_model
-
-    num_experts = src_lm.config.num_moe_experts
-
-    # Unwrap to get owning modules (with parameters and pg_collection)
-    src_core = unwrap_model(src_lm)
-    tgt_core = unwrap_model(tgt_lm)
-
-    # Ensure pg_collection exists
-    if not hasattr(src_core, "pg_collection") or src_core.pg_collection is None:
-        raise RuntimeError("Source model missing pg_collection required for NCCL reshard")
-    if not hasattr(tgt_core, "pg_collection") or tgt_core.pg_collection is None:
-        raise RuntimeError("Target model missing pg_collection required for NCCL reshard")
-
-    # Fill missing DP group on the source using Megatron's parallel state if not provided
-    if getattr(src_core.pg_collection, "dp", None) is None:
-        src_core.pg_collection.dp = parallel_state.get_data_parallel_group()
-
-    # caching plan for reuse
-    cached_plan: Optional[Any] = getattr(tgt_core, "_cached_reshard_plan", None)
-    if cached_plan is None:
+    """Reshard and copy model weights from ``src_model`` to ``target_model`` using ``service``.
+
+    Supports None for src_model and/or target_model to enable non-collocated mode:
+    - (src_model, target_model): Both models present (collocated mode)
+    - (src_model, None): Source rank - only sends data (non-collocated)
+    - (None, target_model): Destination rank - only receives data (non-collocated)
+    - (None, None): Idle rank - participates in collectives but has no transfers (non-collocated)
+
+    In non-collocated mode, metadata includes local rank positions within parallel groups,
+    allowing the planner to correctly map between different process group configurations
+    without requiring dummy models on every rank.
+    """
+    global _plan_cache
+
+    # Handle idle ranks (both models None) - they participate in collectives but have no work
+    if src_model is None and target_model is None:
+        cache_key = _build_plan_cache_key(src_core=None, tgt_core=None, num_experts=None)
+
+        # Use cached plan if available, otherwise build (with collective participation)
+        if cache_key not in _plan_cache:
+            plan = build_centralized_reshard_plan(None, None, num_experts=None)
+            _plan_cache[cache_key] = plan
+        else:
+            plan = _plan_cache[cache_key]
+        execute_reshard_plan(plan, None, None, service=service)
+        return
+
+    # Handle None models - extract core modules only from non-None models
+    src_core = None
+    tgt_core = None
+    num_experts = None
+
+    if src_model is not None:
+        # Handle list-wrapped modules
+        src_lm = src_model[0] if isinstance(src_model, (list, tuple)) else src_model
+        num_experts = src_lm.config.num_moe_experts
+        # Unwrap to get owning modules (with parameters and pg_collection)
+        src_core = unwrap_model(src_lm)
+        # Ensure pg_collection exists
+        if not hasattr(src_core, "pg_collection") or src_core.pg_collection is None:
+            raise RuntimeError("Source model missing pg_collection required for reshard")
+        # Fill missing DP group on the source using Megatron's parallel state if not provided
+        if getattr(src_core.pg_collection, "dp", None) is None:
+            src_core.pg_collection.dp = parallel_state.get_data_parallel_group()
+
+    if target_model is not None:
+        # Handle list-wrapped modules
+        tgt_lm = target_model[0] if isinstance(target_model, (list, tuple)) else target_model
+        if num_experts is None:
+            num_experts = tgt_lm.config.num_moe_experts
+        # Unwrap to get owning modules (with parameters and pg_collection)
+        tgt_core = unwrap_model(tgt_lm)
+        # Ensure pg_collection exists
+        if not hasattr(tgt_core, "pg_collection") or tgt_core.pg_collection is None:
+            raise RuntimeError("Target model missing pg_collection required for reshard")
+
+    # Build or retrieve cached plan
+    cache_key = _build_plan_cache_key(src_core, tgt_core, num_experts)
+
+    if cache_key not in _plan_cache:
+        # All ranks must participate in planning (collective operations)
         plan = build_centralized_reshard_plan(src_core, tgt_core, num_experts=num_experts)
-        setattr(tgt_core, "_cached_reshard_plan", plan)
+        _plan_cache[cache_key] = plan
     else:
-        plan = cached_plan
+        plan = _plan_cache[cache_key]
 
     execute_reshard_plan(plan, src_core, tgt_core, service=service)
diff --git a/megatron/core/resharding/utils.py b/megatron/core/resharding/utils.py
index 7fc9e9ad3a7..1dc2f8f85a8 100644
--- a/megatron/core/resharding/utils.py
+++ b/megatron/core/resharding/utils.py
@@ -324,38 +324,132 @@ def select_src_metadata_balanced(
 ) -> ParameterMetadata:
     """Choose a representative source `ParameterMetadata` for a destination rank.
 
-    Multiple source data-parallel (DP) groups may hold the same logical parameter.
-    To avoid always reading from the same group, we:
-      - bucket `src_meta_list` by their DP group (tuple of ranks)
-      - if there is only one bucket, just return the first entry
-      - otherwise, use the destination rank's global rank to select a source
-        DP group in a round-robin fashion, ensuring even distribution of load
-        across all source DP groups.
+    The selected metadata provides topology information (TP/EP/DP group ranks) that the
+    LCM transfer planner uses to compute actual source ranks and slices. This function
+    doesn't perform transfers itself - it just picks which source configuration to use
+    as reference for planning.
+
+    Two scenarios for EP-sharded parameters:
+    1. Non-collocated mode (same EP size, different rank numbering):
+       - Filter by matching EP local rank to pair ranks with same expert position
+       - Example: src ranks [0-63] and dst ranks [64-127] both with EP=8
+       - Dst EP local 0 should use src EP local 0 as reference (same experts)
+
+    2. Resharding mode (different EP sizes):
+       - Skip EP local rank filtering (sizes don't correspond)
+       - Example: EP=8→EP=16 means dst EP local 8 has no matching src EP local
+       - Expert matching handled by resolved_name; LCM handles TP dimension changes
+
+    Finally, balances across data-parallel (DP) groups to distribute load:
+      - Groups src_meta_list by DP group
+      - Selects source DP group via round-robin: dst_rank % num_src_dp_groups
+      - Ensures even distribution of transfer load across source DP replicas
     """
     if not src_meta_list:
         raise ValueError("src_meta_list must be non-empty")
 
-    # Group source metadata by their DP group layout so we can balance across groups.
-    #   (dp_rank0, dp_rank1, ...) -> [ParameterMetadata for that DP group]
+    # ============================================================================
+    # EXPERT PARALLELISM (EP) LOCAL RANK FILTERING
+    # ============================================================================
+    # Purpose: In non-collocated mode with same EP size, ensure destination ranks
+    # use source metadata from ranks with the same EP local position (same experts).
+    #
+    # Why size check matters:
+    #   - Same size (EP=8→EP=8): Local ranks 0-7 exist in both src and dst
+    #     → Filter ensures dst EP local 0 uses src EP local 0 (same global experts)
+    #   - Different size (EP=8→EP=16): Local ranks 0-15 in dst, only 0-7 in src
+    #     → Dst EP local 8 has no corresponding src EP local rank
+    #     → Skip filter; expert reassignment handled by resolved_name matching
+    #
+    # Expert routing: When EP size changes, each expert parameter is matched via
+    # resolved_name (which includes global expert index). The LCM/TP planner
+    # handles any TP dimension changes, and DP round-robin distributes load.
+    # ============================================================================
+    dst_ep_group = dst_metadata.expert_parallel_group_ranks
+    if dst_ep_group is not None:
+        dst_ep_local = dst_ep_group.index(dst_rank)
+        # Check if EP sizes match between source and destination
+        src_ep_size = (
+            len(src_meta_list[0].expert_parallel_group_ranks)
+            if src_meta_list[0].expert_parallel_group_ranks
+            else None
+        )
+        dst_ep_size = len(dst_ep_group)
+
+        # Only filter by EP local rank when sizes match (non-collocated, not resharding)
+        if src_ep_size == dst_ep_size:
+            matching_ep = [
+                m
+                for m in src_meta_list
+                if m.expert_parallel_group_ranks
+                and m.expert_parallel_group_ranks.index(m.owner_rank) == dst_ep_local
+            ]
+            if not matching_ep:
+                # This indicates a configuration bug: sizes match but no local rank match
+                def _ep_local(m):
+                    return (
+                        m.expert_parallel_group_ranks.index(m.owner_rank)
+                        if m.expert_parallel_group_ranks
+                        else None
+                    )
+
+                available = [(m.owner_rank, _ep_local(m)) for m in src_meta_list]
+                raise ValueError(
+                    f"No source metadata with EP local rank {dst_ep_local}"
+                    f" found for dst rank {dst_rank}. Available: {available}"
+                )
+            src_meta_list = matching_ep
+        # else: EP resharding mode (sizes differ) - skip filter, keep all source candidates
+
+    # ============================================================================
+    # LOCAL COPY OPTIMIZATION (COLLOCATED MODE)
+    # ============================================================================
+    # In collocated mode, prefer local copies when available. If dst_rank appears
+    # in the source metadata list (after TP/EP filtering), use it directly to
+    # avoid unnecessary data transfers.
+    #
+    # A local copy is essentially free
+    # (tensor.copy_() on same GPU), while any remote transfer incurs significant
+    # overhead even within the same node.
+    # ============================================================================
+    local_meta = [m for m in src_meta_list if m.owner_rank == dst_rank]
+    if local_meta:
+        # Found local metadata - use it for a free local copy
+        return local_meta[0]
+
+    # ============================================================================
+    # DATA PARALLELISM (DP) LOAD BALANCING
+    # ============================================================================
+    # After TP/EP filtering (if applicable), balance transfer load across source
+    # data-parallel replicas. Each DP group holds a complete copy of the model,
+    # so we can read from any DP group - choosing via round-robin spreads load.
+    #
+    # Load distribution: dst_rank % num_src_dp_groups ensures even distribution
+    # even when destination has different DP configuration than source.
+    # ============================================================================
     grouped_by_dp: dict[tuple[int, ...], list[ParameterMetadata]] = {}
     for meta in src_meta_list:
         dp_group = tuple(meta.data_parallel_group_ranks or [])
         grouped_by_dp.setdefault(dp_group, []).append(meta)
 
-    # Fast path: only one DP layout present; no balancing necessary.
+    # Fast path: only one DP group present; no balancing necessary
     if len(grouped_by_dp) == 1:
         return src_meta_list[0]
 
-    # Use the destination rank's global rank to select a source DP group in a
-    # round-robin fashion. This ensures that even when multiple destination ranks
-    # have the same DP index (e.g., ranks 0,1,2,3 all being at position 0 in their
-    # respective DP groups), they still get distributed across different source
-    # DP groups based on their global rank.
+    # Round-robin selection across source DP groups based on destination global rank
+    # This ensures even distribution: if we have 4 src DP groups and 128 dst ranks,
+    # each src DP group will be selected by 32 dst ranks (128 / 4 = 32)
     sorted_dp_groups = sorted(grouped_by_dp.keys())
     chosen_group = sorted_dp_groups[dst_rank % len(sorted_dp_groups)]
 
-    # Within the chosen group, any representative metadata works; use the first.
-    return grouped_by_dp[chosen_group][0]
+    # Within the chosen DP group, distribute across available metadata entries
+    # to balance load across all TP groups in the DP replica.
+    # Example: With 4 TP groups in a DP group, dst_ranks will cycle through all 4
+    # instead of always using the first one, better distributing transfer load.
+    group_metadata = grouped_by_dp[chosen_group]
+    within_group_idx = (dst_rank // len(sorted_dp_groups)) % len(group_metadata)
+    selected = group_metadata[within_group_idx]
+    return selected
 
 
 logger = logging.getLogger(__name__)
diff --git a/megatron/core/ssm/mamba_block.py b/megatron/core/ssm/mamba_block.py
index ef41faae143..3eb37955901 100644
--- a/megatron/core/ssm/mamba_block.py
+++ b/megatron/core/ssm/mamba_block.py
@@ -16,6 +16,7 @@
 from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding
 from megatron.core.enums import Fp8Recipe
 from megatron.core.extensions.transformer_engine import TENorm
+from megatron.core.fp4_utils import get_fp4_context
 from megatron.core.fp8_utils import get_fp8_context
 from megatron.core.inference.contexts import BaseInferenceContext
 from megatron.core.packed_seq_params import PackedSeqParams
@@ -25,7 +26,7 @@
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.identity_op import IdentityOp
-from megatron.core.transformer.module import MegatronModule
+from megatron.core.transformer.module import GraphableMegatronModule, MegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
 from megatron.core.transformer.transformer_layer import TransformerLayer
 from megatron.core.transformer.utils import sharded_state_dict_default
@@ -42,17 +43,16 @@ class MambaStackSubmodules:
     attention_layer: Union[ModuleSpec, type] = IdentityOp
     mlp_layer: Union[ModuleSpec, type] = IdentityOp
     moe_layer: Union[ModuleSpec, type] = IdentityOp
+    mtp_block_spec: Optional[ModuleSpec] = None
 
 
-class MambaStack(MegatronModule):
+class MambaStack(GraphableMegatronModule, MegatronModule):
     """
     Constructor for the MambaStack class.
 
     Args:
         config (TransformerConfig): the model configuration
         submodules (MambaStackSubmodules): the submodules for the stack
-        residual_in_fp32 (bool, optional): whether to do residual connections
-            in fp32. Defaults to False.
         pre_process (bool, optional): whether to include an embedding layer.
             Defaults to True.
         hybrid_attention_ratio (float, optional): the target ratio of attention layers to
@@ -75,7 +75,6 @@ def __init__(
         self,
         config: TransformerConfig,
         submodules: MambaStackSubmodules,
-        residual_in_fp32=False,
         pre_process: bool = True,
         hybrid_attention_ratio: float = 0.0,
         hybrid_mlp_ratio: float = 0.0,
@@ -85,12 +84,13 @@ def __init__(
         device=None,
         dtype=None,
         pg_collection: ProcessGroupCollection = None,
+        is_mtp_layer: bool = False,
     ) -> None:
         super().__init__(config=config)
-        self.residual_in_fp32 = residual_in_fp32
         self.pre_process = pre_process
         self.post_layer_norm = post_layer_norm
         self.post_process = post_process
+        self.is_mtp_layer = is_mtp_layer
 
         assert pg_collection is not None, "pg_collection must be provided for MambaStack"
 
@@ -103,29 +103,45 @@ def __init__(
         self.hybrid_attention_ratio = hybrid_attention_ratio
         self.hybrid_mlp_ratio = hybrid_mlp_ratio
         self.hybrid_override_pattern = hybrid_override_pattern
+        self.pg_collection = pg_collection
+
+        # For MTP layers, always use pattern length (config.num_layers is for main decoder)
+        if self.is_mtp_layer:
+            num_layers_for_allocation = len(self.hybrid_override_pattern)
+        else:
+            num_layers_for_allocation = (
+                self.config.num_layers
+                if self.config.num_layers is not None
+                else len(self.hybrid_override_pattern)
+            )
 
         self.layer_type_list = allocate_layers(
-            self.config.num_layers,
+            num_layers_for_allocation,
             self.hybrid_attention_ratio,
             self.hybrid_mlp_ratio,
             self.hybrid_override_pattern,
+            silent=self.is_mtp_layer,
         )
 
         pp_layer_offset = 0
-        if self.pp_group.size() > 1:
+        if self.pp_group.size() > 1 and not self.is_mtp_layer:
             pp_layer_offset, self.layer_type_list = self._select_layers_for_pipeline_parallel(
                 self.layer_type_list
             )
-
+        # Build main decoder layers using shared layer builder
         self.layers = nn.ModuleList()
         for i, layer_type in enumerate(self.layer_type_list):
-            fp8_init_context = get_fp8_context(self.config, i + pp_layer_offset, is_init=True)
-            with fp8_init_context:
+            if self.config.fp8:
+                quant_init_context = get_fp8_context(self.config, i + pp_layer_offset, is_init=True)
+            elif self.config.fp4:
+                quant_init_context = get_fp4_context(self.config, i + pp_layer_offset, is_init=True)
+            else:
+                quant_init_context = nullcontext()
+            with quant_init_context:
                 if layer_type == LayerSymbols.MAMBA:
                     layer = build_module(
                         submodules.mamba_layer,
                         config=self.config,
-                        residual_in_fp32=residual_in_fp32,
                         layer_number=i + 1 + pp_layer_offset,
                         pp_layer_offset=pp_layer_offset,
                         pg_collection=pg_collection,
@@ -137,9 +153,10 @@ def __init__(
                         config=self.config,
                         layer_number=i + 1,
                         pg_collection=pg_collection,
+                        is_mtp_layer=is_mtp_layer,
                     )
                 elif layer_type == LayerSymbols.MLP:
-                    # Transformer layers apply their own pp_layer_offset
+                    # MLP layers apply their own pp_layer_offset
                     layer = build_module(
                         submodules.mlp_layer,
                         config=self.config,
@@ -147,7 +164,7 @@ def __init__(
                         pg_collection=pg_collection,
                     )
                 elif layer_type == LayerSymbols.MOE:
-                    # Transformer layers apply their own pp_layer_offset
+                    # MoE layers apply their own pp_layer_offset
                     layer = build_module(
                         submodules.moe_layer,
                         config=self.config,
@@ -170,15 +187,53 @@ def __init__(
             )
 
     def _select_layers_for_pipeline_parallel(self, layer_type_list):
-        num_layers_per_pipeline_rank = self.config.num_layers // self.pp_group.size()
-
         assert self.config.virtual_pipeline_model_parallel_size is None, (
             "The Mamba hybrid model does not currently support "
             "virtual/interleaved pipeline parallelism"
         )
 
-        offset = self.pp_group.rank() * num_layers_per_pipeline_rank
-        selected_list = layer_type_list[offset : offset + num_layers_per_pipeline_rank]
+        pp_rank = self.pp_group.rank()
+        pp_size = self.pp_group.size()
+
+        num_layers_in_first = self.config.num_layers_in_first_pipeline_stage
+        num_layers_in_last = self.config.num_layers_in_last_pipeline_stage
+
+        if num_layers_in_first is not None or num_layers_in_last is not None:
+            # Uneven pipeline parallelism: mirror the logic in
+            # get_transformer_layer_offset so that MambaStack and
+            # TransformerLayer agree on layer placement.
+            first = 0 if num_layers_in_first is None else num_layers_in_first
+            last = 0 if num_layers_in_last is None else num_layers_in_last
+            middle_num_layers = self.config.num_layers - first - last
+
+            middle_pipeline_stages = pp_size - sum(
+                1 for x in (num_layers_in_first, num_layers_in_last) if x is not None
+            )
+
+            if middle_pipeline_stages > 0:
+                layers_per_middle = middle_num_layers // middle_pipeline_stages
+            else:
+                layers_per_middle = 0
+
+            is_first_stage = num_layers_in_first is not None and pp_rank == 0
+            is_last_stage = num_layers_in_last is not None and pp_rank == pp_size - 1
+
+            if is_first_stage:
+                offset = 0
+                num_layers_this_rank = first
+            elif is_last_stage:
+                offset = self.config.num_layers - last
+                num_layers_this_rank = last
+            else:
+                middle_rank = pp_rank if num_layers_in_first is None else pp_rank - 1
+                offset = middle_rank * layers_per_middle + first
+                num_layers_this_rank = layers_per_middle
+        else:
+            num_layers_per_pipeline_rank = self.config.num_layers // pp_size
+            offset = pp_rank * num_layers_per_pipeline_rank
+            num_layers_this_rank = num_layers_per_pipeline_rank
+
+        selected_list = layer_type_list[offset : offset + num_layers_this_rank]
 
         return offset, selected_list
 
@@ -202,6 +257,39 @@ def mamba_state_shapes_per_request(self) -> Optional[Tuple[Tuple[int], Tuple[int
                 return layer.mamba_state_shapes_per_request()
         return None
 
+    def _should_call_local_cudagraph(self, *args, **kwargs):
+        """
+        Check if we should call the local cudagraph path.
+        """
+        if (
+            not self.training
+            and hasattr(self, 'cudagraph_manager')
+            and kwargs['attention_mask'] is None
+            and (
+                kwargs.get('inference_context') is not None
+                or kwargs.get('inference_params') is not None
+            )
+            and CudaGraphScope.full_iteration_inference in self.config.cuda_graph_scope
+        ):
+            if kwargs['inference_context'].is_static_batching():
+                using_cuda_graph = kwargs['inference_context'].is_decode_only()
+            else:
+                using_cuda_graph = kwargs['inference_context'].using_cuda_graph_this_step()
+
+            if using_cuda_graph:
+                return True
+        return False
+
+    def __call__(self, *args, **kwargs):
+        if self._should_call_local_cudagraph(*args, **kwargs):
+            kwargs['hidden_states'] = (
+                kwargs['hidden_states'].unwrap()
+                if isinstance(kwargs['hidden_states'], WrappedTensor)
+                else kwargs['hidden_states']
+            )
+            return super().__call__(*args, **kwargs)[0]
+        return super().__call__(*args, **kwargs)
+
     def forward(
         self,
         hidden_states: Union[Tensor, WrappedTensor],
@@ -276,16 +364,29 @@ def forward(
         # control which layer will be fp8 or bf16
         use_outer_fp8_context = self.config.fp8 and self.config.fp8_recipe == Fp8Recipe.delayed
         use_inner_fp8_context = self.config.fp8 and self.config.fp8_recipe != Fp8Recipe.delayed
+        use_fp4_context = self.config.fp4 is not None
         outer_fp8_context = get_fp8_context(self.config) if use_outer_fp8_context else nullcontext()
 
+        if use_inner_fp8_context:
+
+            def get_inner_quant_context(config, layer_number):
+                return get_fp8_context(config, layer_number)
+
+        elif use_fp4_context:
+
+            def get_inner_quant_context(config, layer_number):
+                return get_fp4_context(config, layer_number)
+
+        else:
+
+            def get_inner_quant_context(config, layer_number):
+                return nullcontext()
+
         with outer_fp8_context:
             for layer in self.layers:
-                inner_fp8_context = (
-                    get_fp8_context(self.config, layer.layer_number - 1)
-                    if use_inner_fp8_context
-                    else nullcontext()
-                )
-                with inner_fp8_context:
+                # Layers have 1-indexed layer numbers attribute.
+                inner_quant_context = get_inner_quant_context(self.config, layer.layer_number - 1)
+                with inner_quant_context:
                     if isinstance(layer, TransformerLayer):
                         hidden_states, _ = layer(
                             hidden_states=hidden_states,
@@ -296,7 +397,7 @@ def forward(
                             packed_seq_params=packed_seq_params,
                             padding_mask=padding_mask,
                         )
-                    else:  # MambaLayer
+                    else:  # MambaLayer, Expert, or MLP
                         hidden_states = layer(
                             hidden_states=hidden_states,
                             attention_mask=attention_mask,
@@ -316,7 +417,7 @@ def forward(
 
         # Ensure that the tensor passed between pipeline parallel stages is
         # viewless. See related notes in TransformerBlock and TransformerLayer
-        output = make_viewless_tensor(
+        hidden_states = make_viewless_tensor(
             inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True
         )
 
diff --git a/megatron/core/ssm/mamba_hybrid_layer_allocation.py b/megatron/core/ssm/mamba_hybrid_layer_allocation.py
index fe997e2249a..d7002b2915d 100644
--- a/megatron/core/ssm/mamba_hybrid_layer_allocation.py
+++ b/megatron/core/ssm/mamba_hybrid_layer_allocation.py
@@ -1,7 +1,8 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
 
 import logging
-from typing import Dict, List, Tuple
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
 
 if __name__ != "__main__":
     from megatron.core.utils import log_single_rank
@@ -29,9 +30,129 @@ class Symbols:
     ATTENTION = "*"
     MLP = "-"
     MOE = 'E'
+    MTP_SEPARATOR = "/"
     VALID = {MAMBA, ATTENTION, MLP, MOE}
 
 
+@dataclass
+class ParsedHybridPattern:
+    """Result of parsing a unified hybrid pattern string.
+
+    A unified pattern encodes both the main decoder pattern and the MTP pattern
+    in a single string using "/" as a separator.
+
+    Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
+
+    Examples:
+        - "M*M*" -> main="M*M*", mtp=None, depths=0 (no MTP)
+        - "M*M*/MM/MM" -> main="M*M*", mtp="MM", depths=2
+        - "MMMM/*M/*M/*M" -> main="MMMM", mtp="*M", depths=3
+
+    The "/" symbol introduces MTP patterns. Each repeated pattern after the main
+    decoder represents one MTP prediction depth.
+
+    Attributes:
+        main_pattern: The main decoder layer pattern (e.g., "M*M*")
+        mtp_pattern: The MTP layer pattern per depth (e.g., "MM"), or None if no MTP
+        mtp_num_depths: Number of MTP prediction depths (0 if no MTP)
+    """
+
+    main_pattern: Optional[str]
+    mtp_pattern: Optional[str]
+    mtp_num_depths: int
+
+
+def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern:
+    """Parse a unified hybrid pattern string into main and MTP components.
+
+    The pattern uses "/" as a separator between the main decoder pattern and
+    MTP patterns. Each MTP pattern after the separator represents one prediction
+    depth.
+
+    Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
+
+    Args:
+        pattern: Unified pattern string, e.g., "M*M*/MM/MM" or just "M*M*"
+
+    Returns:
+        ParsedHybridPattern with main_pattern, mtp_pattern, and mtp_num_depths
+
+    Raises:
+        ValueError: If MTP patterns are inconsistent (all must be identical)
+        ValueError: If pattern contains invalid layer symbols
+
+    Examples:
+        >>> parse_hybrid_pattern("M*M*")
+        ParsedHybridPattern(main_pattern="M*M*", mtp_pattern=None, mtp_num_depths=0)
+
+        >>> parse_hybrid_pattern("M*M*/MM/MM")
+        ParsedHybridPattern(main_pattern="M*M*", mtp_pattern="MM", mtp_num_depths=2)
+
+        >>> parse_hybrid_pattern("MMMM/*M/*M/*M")
+        ParsedHybridPattern(main_pattern="MMMM", mtp_pattern="*M", mtp_num_depths=3)
+    """
+    if pattern is None:
+        return ParsedHybridPattern(main_pattern=None, mtp_pattern=None, mtp_num_depths=0)
+
+    parts = pattern.split(Symbols.MTP_SEPARATOR)
+
+    if len(parts) == 1:
+        # No MTP separator found - pattern is main decoder only
+        main_pattern = parts[0]
+        _validate_pattern(main_pattern, "main")
+        return ParsedHybridPattern(main_pattern=main_pattern, mtp_pattern=None, mtp_num_depths=0)
+
+    # First part is main decoder pattern
+    main_pattern = parts[0]
+    if main_pattern:
+        _validate_pattern(main_pattern, "main")
+
+    # Remaining parts are MTP patterns (one per depth)
+    mtp_parts = parts[1:]
+
+    if not mtp_parts or all(p == "" for p in mtp_parts):
+        # No MTP patterns after separator
+        return ParsedHybridPattern(
+            main_pattern=main_pattern if main_pattern else None, mtp_pattern=None, mtp_num_depths=0
+        )
+
+    # Validate all MTP patterns are identical
+    mtp_pattern = mtp_parts[0]
+    for i, part in enumerate(mtp_parts[1:], start=2):
+        if part != mtp_pattern:
+            raise ValueError(
+                f"All MTP patterns must be identical. "
+                f"Pattern 1 is '{mtp_pattern}', but pattern {i} is '{part}'. "
+                f"Full pattern: '{pattern}'"
+            )
+
+    _validate_pattern(mtp_pattern, "MTP")
+
+    return ParsedHybridPattern(
+        main_pattern=main_pattern if main_pattern else None,
+        mtp_pattern=mtp_pattern,
+        mtp_num_depths=len(mtp_parts),
+    )
+
+
+def _validate_pattern(pattern: str, pattern_name: str) -> None:
+    """Validate that a pattern contains only valid layer symbols.
+
+    Args:
+        pattern: Layer pattern string to validate
+        pattern_name: Name of pattern for error messages (e.g., "main" or "MTP")
+
+    Raises:
+        ValueError: If pattern contains invalid symbols
+    """
+    for char in pattern:
+        if char not in Symbols.VALID:
+            raise ValueError(
+                f"In {pattern_name} pattern, '{char}' is not a valid layer symbol. "
+                f"Valid symbols are: {Symbols.VALID}"
+            )
+
+
 def _allocate_auto(
     total_layers_count: int, target_attention_ratio: float, target_mlp_ratio: float
 ) -> list:
@@ -97,19 +218,21 @@ def allocate_layers(
     target_attention_ratio: float,
     target_mlp_ratio: float,
     override_pattern: str = None,
+    silent: bool = False,
 ) -> list:
     """Allocates layers according to the requested distribution of layer types."""
     assert total_layers_count > 0
     assert target_attention_ratio >= 0.0 and target_attention_ratio <= 1.0
     assert target_mlp_ratio >= 0.0 and target_mlp_ratio <= 1.0
     assert target_attention_ratio + target_mlp_ratio <= 1.0
+    maybe_log_single_rank = (lambda *args, **kwargs: None) if silent else log_single_rank
     # Note: target_mamba_ratio = 1.0 - target_attention_ratio - target_mlp_ratio
 
     layer_type_list = _allocate_auto(total_layers_count, target_attention_ratio, target_mlp_ratio)
 
     if override_pattern is not None:
         layer_type_list_override = _allocate_override(total_layers_count, override_pattern)
-        log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
+        maybe_log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
         if (target_attention_ratio > 0.0 or target_mlp_ratio > 0.0) and not _layer_counts_match(
             layer_type_list_override, layer_type_list
         ):
@@ -119,13 +242,15 @@ def allocate_layers(
                 "pattern."
             )
         if layer_type_list_override == layer_type_list:
-            log_single_rank(
+            maybe_log_single_rank(
                 logger, logging.INFO, "The override pattern matches the overridden pattern"
             )
         else:
-            log_single_rank(logger, logging.INFO, "Warning: overriding pattern A with pattern B")
-            log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
-            log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
+            maybe_log_single_rank(
+                logger, logging.INFO, "Warning: overriding pattern A with pattern B"
+            )
+            maybe_log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
+            maybe_log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
         layer_type_list = layer_type_list_override
 
     if target_attention_ratio > 0.0 or target_mlp_ratio > 0.0 or override_pattern is not None:
@@ -134,32 +259,32 @@ def allocate_layers(
         actual_mlp_layers_count = layer_type_list.count(Symbols.MLP)
         actual_mlp_ratio = actual_mlp_layers_count / total_layers_count
         allocation_string = "".join(layer_type_list)
-        log_single_rank(
+        maybe_log_single_rank(
             logger,
             logging.INFO,
             f"Hybrid allocation ({Symbols.MAMBA} is mamba, "
             f"{Symbols.ATTENTION} is attention, "
             f"{Symbols.MLP} is mlp):",
         )
-        log_single_rank(logger, logging.INFO, allocation_string)
-        log_single_rank(
+        maybe_log_single_rank(logger, logging.INFO, allocation_string)
+        maybe_log_single_rank(
             logger,
             logging.INFO,
             f"{actual_attention_layers_count} attention layers in "
             f"{total_layers_count} total layers.",
         )
-        log_single_rank(
+        maybe_log_single_rank(
             logger,
             logging.INFO,
             f"Target attention ratio: {target_attention_ratio:.2f}. "
             f"Actual attention ratio: {actual_attention_ratio:.2f}.",
         )
-        log_single_rank(
+        maybe_log_single_rank(
             logger,
             logging.INFO,
             f"{actual_mlp_layers_count} mlp layers in " f"{total_layers_count} total layers.",
         )
-        log_single_rank(
+        maybe_log_single_rank(
             logger,
             logging.INFO,
             f"Target mlp ratio: {target_mlp_ratio:.2f}. "
diff --git a/megatron/core/ssm/mamba_layer.py b/megatron/core/ssm/mamba_layer.py
index f86c7f53e1a..adf0711b36c 100644
--- a/megatron/core/ssm/mamba_layer.py
+++ b/megatron/core/ssm/mamba_layer.py
@@ -6,7 +6,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from dataclasses import dataclass, field
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict, Optional, Protocol, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -20,10 +20,18 @@
 from megatron.core.transformer.identity_op import IdentityOp
 from megatron.core.transformer.module import GraphableMegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormInterface
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.typed_torch import apply_module
 from megatron.core.utils import deprecate_inference_params
 
 
+class LayerNormBuilder(Protocol):
+    """A protocol showing how MambaLayer expects to construct its LayerNorm."""
+
+    def __call__(self, config: TransformerConfig, hidden_size: int, /) -> LayerNormInterface: ...
+
+
 @dataclass
 class MambaLayerSubmodules:
     """
@@ -40,7 +48,7 @@ class MambaLayerSubmodules:
             after the mixer.
     """
 
-    norm: Union[ModuleSpec, type] = IdentityOp
+    norm: LayerNormBuilder = IdentityOp
     mixer: Union[ModuleSpec, type] = IdentityOp
     mamba_bda: Union[ModuleSpec, type] = IdentityOp
 
@@ -61,7 +69,6 @@ def __init__(
         config: TransformerConfig,
         submodules: MambaLayerSubmodules,
         layer_number: int = 1,
-        residual_in_fp32=False,
         pg_collection: ProcessGroupCollection = None,
         pp_layer_offset: int = 0,
     ):
@@ -72,7 +79,6 @@ def __init__(
         self.config = config
         self.submodules_config = submodules
         self.layer_number = layer_number
-        self.residual_in_fp32 = residual_in_fp32
         self.hidden_dropout = config.hidden_dropout
         self.mixer = build_module(
             submodules.mixer,
@@ -82,7 +88,7 @@ def __init__(
             pg_collection=pg_collection,
             pp_layer_offset=pp_layer_offset,
         )
-        self.norm = build_module(submodules.norm, self.config, self.config.hidden_size)
+        self.norm = submodules.norm(self.config, self.config.hidden_size)
         self.mamba_bda = build_module(submodules.mamba_bda)
         self.bias_dropout_add_exec_handler = torch.enable_grad
 
@@ -128,11 +134,11 @@ def forward(
         inference_context = deprecate_inference_params(inference_context, inference_params)
 
         residual = hidden_states
-        if self.residual_in_fp32:
-            residual = residual.to(torch.float32)
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
 
         hidden_states = hidden_states.to(dtype=self.config.params_dtype)
-        hidden_states = self.norm(hidden_states)
+        hidden_states = apply_module(self.norm)(hidden_states)
 
         mixer_out_with_bias = self.mixer(
             hidden_states, inference_context=inference_context, packed_seq_params=packed_seq_params
@@ -192,10 +198,11 @@ def _should_call_local_cudagraph(self, *args, **kwargs):
             hasattr(self, 'cudagraph_manager')
             and kwargs.get('attention_mask') is None
             and kwargs.get('inference_context') is not None
+            and not self.config.cuda_graph_scope  # empty-list = per-layer CUDA graphs
         ):
-            if hasattr(kwargs['inference_context'], "using_cuda_graph_this_step"):
-                return kwargs['inference_context'].using_cuda_graph_this_step()
-            else:
-                # static
-                return kwargs['inference_context'].is_decode_only()
+            context = kwargs['inference_context']
+            using_cuda_graph = (context.is_static_batching() and context.is_decode_only()) or (
+                not context.is_static_batching() and context.using_cuda_graph_this_step()
+            )
+            return using_cuda_graph
         return False
diff --git a/megatron/core/ssm/mamba_mixer.py b/megatron/core/ssm/mamba_mixer.py
index 1910c96cb11..4df8fc3f4a0 100644
--- a/megatron/core/ssm/mamba_mixer.py
+++ b/megatron/core/ssm/mamba_mixer.py
@@ -7,7 +7,6 @@
 
 import logging
 import math
-import warnings
 from dataclasses import dataclass, replace
 from typing import List, Optional, Tuple, Union
 
@@ -39,6 +38,7 @@
     deprecate_inference_params,
     is_causal_conv1d_min_version,
     is_mamba_min_version,
+    is_using_quantization_scales,
     log_single_rank,
 )
 
@@ -163,10 +163,6 @@ def __init__(
         # Fused kernel and sharding options
         chunk_size=128,
         layer_number=None,
-        use_mem_eff_path=None,
-        d_state=None,
-        headdim=None,
-        ngroups=None,
         pg_collection: ProcessGroupCollection = None,
         pp_layer_offset: int = 0,
     ):
@@ -194,33 +190,6 @@ def __init__(
         self.cached_batch_size = None
         assert pg_collection is not None, "pg_collection must be provided for MambaMixer"
         self.pg_collection = pg_collection
-
-        # Check for deprecated arguments and raise warnings
-        if use_mem_eff_path is not None:
-            warnings.warn(
-                "The 'use_mem_eff_path' argument is deprecated and will be removed in the future. "
-                "Please use the value from the TransformerConfig object instead.",
-                DeprecationWarning,
-            )
-        if d_state is not None:
-            warnings.warn(
-                "The 'd_state' argument is deprecated and will be removed in the future. "
-                "Please use the value from the TransformerConfig object instead.",
-                DeprecationWarning,
-            )
-        if headdim is not None:
-            warnings.warn(
-                "The 'headdim' argument is deprecated and will be removed in the future. "
-                "Please use the value from the TransformerConfig object instead.",
-                DeprecationWarning,
-            )
-        if ngroups is not None:
-            warnings.warn(
-                "The 'ngroups' argument is deprecated and will be removed in the future. "
-                "Please use the value from the TransformerConfig object instead.",
-                DeprecationWarning,
-            )
-
         self.use_mem_eff_path = self.config.use_mamba_mem_eff_path
         self.d_state = self.config.mamba_state_dim
         self.headdim = self.config.mamba_head_dim
@@ -470,123 +439,48 @@ def _dynamic_inference(self, hidden_states: torch.Tensor, context: DynamicInfere
         conv_state, ssm_state = context.mamba_states_cache(self.layer_number - self.pp_layer_offset)
 
         padded_dims = context.padded_batch_dimensions
-
         token_count = padded_dims.token_count
         decode_req_count = padded_dims.decode_req_count
         prefill_req_count = padded_dims.prefill_req_count
-        has_explicit_chunked_prefill_req = padded_dims.has_explicit_chunked_prefill_req
 
         # Input projection
         zxBCdt, _ = self.in_proj(hidden_states)
 
-        if decode_req_count > 0 and prefill_req_count == 0:
-            # Decode-only
-            y = self._ssm_decode(
-                zxBCdt.transpose(0, 1),
-                conv_state,
-                ssm_state,
-                context.mamba_metadata.batch_indices_decode,
-            ).transpose(0, 1)
-        elif decode_req_count == 0 and (prefill_req_count > 0 or has_explicit_chunked_prefill_req):
-            if prefill_req_count > 0:
-                # Prefill only (regular prefill requests)
-                y_prefill = self._ssm_prefill(
-                    zxBCdt,
-                    conv_state=conv_state,
-                    ssm_state=ssm_state,
-                    seq_idx=context.mamba_metadata.seq_idx,
-                    cu_seqlens=context.mamba_metadata.cu_seqlens,
-                    return_varlen_states=True,
-                    batch_indices=context.mamba_metadata.batch_indices_prefill,
-                )
-            if has_explicit_chunked_prefill_req:
-                # Prefill only (chunked prefill request)
-                zxBCdt_chunked_prefill = torch.empty_like(zxBCdt)
-                tensor_get_slice_after(
-                    zxBCdt,
-                    zxBCdt_chunked_prefill,
-                    context.mamba_metadata.device_chunked_prefill,
-                    check_bounds=False,
-                )
-                y_chunked_prefill = self._ssm_prefill(
-                    zxBCdt_chunked_prefill[: context.mamba_metadata.device_chunked_prefill[1]],
-                    conv_state=conv_state,
-                    ssm_state=ssm_state,
-                    batch_indices=context.mamba_metadata.batch_indices_chunked_prefill,
-                    is_chunked_prefill=True,
-                )
-            if prefill_req_count > 0 and has_explicit_chunked_prefill_req:
-                # Merge regular prefill and chunked prefill parts
-                tensor_merge(
-                    y_prefill, y_chunked_prefill, context.mamba_metadata.device_chunked_prefill
-                )
-                y = y_prefill
-            elif prefill_req_count > 0:
-                # Prefill-only without chunked prefill
-                y = y_prefill
-            else:
-                # Prefill-only with only chunked prefill
-                y = y_chunked_prefill
-        else:
-            # Mix of decode and prefill
-            zxBCdt_prefill = torch.empty_like(zxBCdt)
-            tensor_get_slice_after(
-                zxBCdt,
-                zxBCdt_prefill,
-                context.mamba_metadata.device_decode_prefill,
-                check_bounds=False,
-            )
-            # Decode requests
+        y_decode = None
+        y_prefill = None
+
+        # Decode
+        if decode_req_count > 0:
+            # For mixed batch, the decode tokens are at the start of zxBCdt
+            zxBCdt_decode = zxBCdt[:decode_req_count] if prefill_req_count > 0 else zxBCdt
+
             y_decode = self._ssm_decode(
-                zxBCdt[:decode_req_count].transpose(0, 1),
+                zxBCdt_decode.transpose(0, 1),
                 conv_state,
                 ssm_state,
                 context.mamba_metadata.batch_indices_decode,
             ).transpose(0, 1)
-            y_prefill, y_chunked_prefill = None, None
-            if prefill_req_count > 0:
-                # Regular prefill requests
-                y_prefill = self._ssm_prefill(
-                    zxBCdt_prefill,
-                    conv_state=conv_state,
-                    ssm_state=ssm_state,
-                    seq_idx=context.mamba_metadata.seq_idx,
-                    cu_seqlens=context.mamba_metadata.cu_seqlens,
-                    return_varlen_states=True,
-                    batch_indices=context.mamba_metadata.batch_indices_prefill,
-                )
-            if has_explicit_chunked_prefill_req:
-                # Chunked prefill request
-                zxBCdt_chunked_prefill = torch.empty_like(zxBCdt_prefill)
+
+        # Prefill
+        if prefill_req_count > 0:
+            if decode_req_count > 0:
+                # If mixed, slice the prefill portion out of zxBCdt
+                zxBCdt_prefill = torch.empty_like(zxBCdt)
                 tensor_get_slice_after(
+                    zxBCdt,
                     zxBCdt_prefill,
-                    zxBCdt_chunked_prefill,
-                    context.mamba_metadata.device_chunked_prefill,
+                    context.mamba_metadata.device_decode_prefill,
                     check_bounds=False,
                 )
-                y_chunked_prefill = self._ssm_prefill(
-                    zxBCdt_chunked_prefill[: context.mamba_metadata.device_chunked_prefill[1]],
-                    conv_state=conv_state,
-                    ssm_state=ssm_state,
-                    batch_indices=context.mamba_metadata.batch_indices_chunked_prefill,
-                    is_chunked_prefill=True,
-                )
-            if prefill_req_count > 0 and has_explicit_chunked_prefill_req:
-                # Merge regular prefill and chunked prefill parts
-                assert y_prefill is not None
-                assert y_chunked_prefill is not None
-                tensor_merge(
-                    y_prefill, y_chunked_prefill, context.mamba_metadata.device_chunked_prefill
-                )
-            elif has_explicit_chunked_prefill_req:
-                # Chunked prefill only
-                assert y_prefill is None
-                assert y_chunked_prefill is not None
-                y_prefill = y_chunked_prefill
             else:
-                # Regular prefill only; y_prefill is already set, nothing more to be done
-                assert y_prefill is not None
-            # Merge decode and prefill parts
+                zxBCdt_prefill = zxBCdt
+
+            y_prefill = self._dynamic_inference_prefill(
+                zxBCdt_prefill, context, conv_state, ssm_state
+            )
+
+        # Merge decode and prefill results if necessary
+        if y_decode is not None and y_prefill is not None:
             y = torch.empty(
                 [token_count, 1, y_prefill.shape[-1]],
                 dtype=y_prefill.dtype,
@@ -595,12 +489,92 @@ def _dynamic_inference(self, hidden_states: torch.Tensor, context: DynamicInfere
             tensor_merge(
                 y_decode, y_prefill, context.mamba_metadata.device_decode_prefill, output_tensor=y
             )
+        elif y_decode is not None:
+            y = y_decode
+        elif y_prefill is not None:
+            y = y_prefill
+        else:
+            raise RuntimeError("Dynamic inference called with 0 decode and 0 prefill requests")
+
+        # Clear the outputs for padding tokens when using quantization scales
+        # to avoid corrupting amax calculations
+        if is_using_quantization_scales(self.config):
+            y[context.padding_slice] = 0.0
 
         # Output projection
         out, out_bias = self.out_proj(y)
 
         return out, out_bias
 
+    def _dynamic_inference_prefill(
+        self,
+        zxBCdt: torch.Tensor,
+        context: DynamicInferenceContext,
+        conv_state: torch.Tensor,
+        ssm_state: torch.Tensor,
+    ) -> torch.Tensor:
+        """Helper to run dynamic inference prefill (chunked prefill request separately)."""
+        metadata = context.mamba_metadata
+
+        # Use the regular prefill request count to determine if regular
+        # prefill path needs to be run when chunked prefill is enabled
+        prefill_req_count = context.batch_dimensions.prefill_req_count
+
+        # Padded prefill token count
+        prefill_token_count = zxBCdt.shape[0]
+
+        enable_chunked_prefill = context.is_chunked_prefill_enabled()
+
+        y_chunked = None
+        y_regular = None
+
+        # Chunked prefill
+        if enable_chunked_prefill:
+            y_chunked = self._ssm_prefill(
+                zxBCdt[: metadata.device_chunked_prefill[0]],
+                conv_state=conv_state,
+                ssm_state=ssm_state,
+                batch_indices=metadata.batch_indices_chunked_prefill,
+                is_chunked_prefill=True,
+            )
+
+            # Update zxBCdt to contain the remaining slice for regular prefill
+            zxBCdt_remainder = torch.empty_like(zxBCdt)
+            tensor_get_slice_after(
+                zxBCdt, zxBCdt_remainder, metadata.device_chunked_prefill, check_bounds=False
+            )
+            zxBCdt = zxBCdt_remainder
+
+        # Regular prefill
+        if not enable_chunked_prefill or prefill_req_count > 1:
+            y_regular = self._ssm_prefill(
+                zxBCdt,
+                conv_state=conv_state,
+                ssm_state=ssm_state,
+                seq_idx=metadata.seq_idx,
+                cu_seqlens=metadata.cu_seqlens,
+                return_varlen_states=True,
+                batch_indices=metadata.batch_indices_prefill,
+            )
+
+        # Merge chunked prefill and regular prefill results
+        if y_chunked is not None and y_regular is not None:
+            y_combined = torch.empty_like(y_regular)
+            tensor_merge(
+                y_chunked, y_regular, metadata.device_chunked_prefill, output_tensor=y_combined
+            )
+            return y_combined
+        elif y_chunked is not None:
+            y_prefill = torch.empty(
+                (prefill_token_count, 1, y_chunked.shape[-1]),
+                dtype=y_chunked.dtype,
+                device=y_chunked.device,
+            )
+            y_prefill[: metadata.device_chunked_prefill[0]] = y_chunked
+            return y_prefill
+        else:
+            return y_regular
+
     def _decode(
         self, hidden_states, conv_state, ssm_state, batch_indices: Optional[torch.Tensor] = None
     ) -> Tuple[torch.Tensor, torch.Tensor]:
diff --git a/megatron/core/tensor_parallel/inference_layers.py b/megatron/core/tensor_parallel/inference_layers.py
index 9c1adbc6717..0addc64a65f 100644
--- a/megatron/core/tensor_parallel/inference_layers.py
+++ b/megatron/core/tensor_parallel/inference_layers.py
@@ -1,5 +1,5 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-from typing import Callable, Optional, Tuple
+from typing import Callable, Optional, Tuple, Union
 
 import torch
 import torch.distributed as dist
@@ -13,6 +13,8 @@
     multimem_all_gather,
     multimem_reduce_scatter,
 )
+from megatron.core.inference.quantization.mxfp8_tensor import MXFP8Tensor
+from megatron.core.inference.quantization.utils import mm_mxfp8
 from megatron.core.model_parallel_config import ModelParallelConfig
 from megatron.core.parallel_state import get_global_symmetric_memory_buffer
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -41,6 +43,21 @@ def _te_rms_norm_kernel(x: torch.Tensor, weight: torch.Tensor, eps: float):
     return out.to(x.dtype)
 
 
+def _apply_linear(
+    x: torch.Tensor,
+    weight: Union[torch.Tensor, MXFP8Tensor],
+    config: TransformerConfig,
+    out: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """
+    Helper to apply either MXFP8 or standard GEMM based on the configuration.
+    """
+    kwargs = {"out": out} if out is not None else {}
+    if config.fp8_recipe == "mxfp8":
+        return mm_mxfp8(x, weight, **kwargs)
+    return torch.matmul(x, weight.t(), **kwargs)
+
+
 class InferenceLayerNormColumnParallelLinear(TELayerNormColumnParallelLinear):
     """
     Inference optimized version of TELayerNormColumnParallelLinear.
@@ -156,7 +173,7 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
             x = _te_rms_norm_kernel(x=x, weight=self.layer_norm_weight, eps=self.eps)
             x = self._all_gather(x, symm_mem_buffer)
 
-        x = torch.matmul(x, self.weight.t())
+        x = _apply_linear(x, self.weight, self.config)
 
         return x, None
 
@@ -218,10 +235,15 @@ def _matmul_reduce_scatter(self, x, residual=None):
         """
         # 1. check if bf16
         is_bf16 = x.dtype == torch.bfloat16
-        # 2. check if hopper
+        # 2. check if mxfp8
+        use_mxfp8 = self.config.fp8_recipe == "mxfp8"
+        # 3. check if hopper or newer
         is_hopper_or_newer = torch.cuda.get_device_properties(x.device).major >= 9
-        # 3. attempt to ask for symmetric memory
+        # 4. attempt to ask for symmetric memory
         symm_mem_buffer_dims = list(x.size())
+        if use_mxfp8:
+            # Remove batch dimension for FlashInfer mxfp8
+            del symm_mem_buffer_dims[1]
         symm_mem_buffer_dims[-1] = self.weight.size(0)
         symm_mem_buffer = get_global_symmetric_memory_buffer().maybe_get_tensor(
             symm_mem_buffer_dims, dtype=x.dtype
@@ -230,10 +252,12 @@ def _matmul_reduce_scatter(self, x, residual=None):
         can_use_custom_nvls_collectives = (
             is_bf16 and is_hopper_or_newer and has_enough_symmetric_memory
         )
+
         if can_use_custom_nvls_collectives:
             # Write output of matmul directly onto the symmetric memory buffer
-            torch.matmul(x, self.weight.t(), out=symm_mem_buffer["tensor"])
-            x = symm_mem_buffer["tensor"]
+
+            x = _apply_linear(x, self.weight, self.config, out=symm_mem_buffer["tensor"])
+
             # perform nvls reduce-scatter
             if self.next_layer_norm_weights is None:
                 output_dims = list(x.size())
@@ -263,7 +287,7 @@ def _matmul_reduce_scatter(self, x, residual=None):
                 return residual
         else:
             # revert to torch dist (NCCL) reduce-scatter
-            x = torch.matmul(x, self.weight.t())
+            x = _apply_linear(x, self.weight, self.config)
             x, _ = reduce_scatter_along_first_dim(x, tp_group=self.tp_group)
         return x
 
@@ -280,12 +304,14 @@ def _set_residual(self, residual: torch.Tensor):
         self.residual = residual
 
     @torch.no_grad()
-    def forward(self, x: torch.Tensor, residual: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(
+        self, x: torch.Tensor, residual: Optional[torch.Tensor] = None
+    ) -> tuple[torch.Tensor, None]:
         """
         Forward pass.
         """
         if self.tp_size == 1:
-            x = torch.matmul(x, self.weight.t())
+            x = _apply_linear(x, self.weight, self.config)
             return x, None
         else:
             x = self._matmul_reduce_scatter(x)
diff --git a/megatron/core/tensor_parallel/layers.py b/megatron/core/tensor_parallel/layers.py
index b2b254dec32..40c7e712a1d 100644
--- a/megatron/core/tensor_parallel/layers.py
+++ b/megatron/core/tensor_parallel/layers.py
@@ -2,6 +2,7 @@
 
 # Parts of the code here are adapted from PyTorch
 # repo: https://github.com/pytorch/pytorch
+from __future__ import annotations
 
 import os
 import warnings
@@ -11,6 +12,7 @@
 import torch
 import torch.nn.functional as F
 from torch.nn.parameter import Parameter
+from typing_extensions import override
 
 from megatron.core.model_parallel_config import ModelParallelConfig
 from megatron.core.parallel_state import (
@@ -375,7 +377,6 @@ def linear_with_frozen_weight(
     tp_group: Optional[torch.distributed.ProcessGroup],
     grad_output_buffer: Optional[List[torch.Tensor]] = None,
     wgrad_deferral_limit: None = None,
-    async_grad_allreduce: Optional[bool] = None,
 ) -> torch.Tensor:
     """Linear layer execution with weight.requires_grad == False.
 
@@ -412,19 +413,8 @@ def linear_with_frozen_weight(
 
     wgrad_deferral_limit (int optional): dummy argument, used to
     keep the API unified between all forward implementation functions.
-
-
-    async_grad_allreduce (bool optional): Will be removed with 0.11.0.
-                                          Please use allreduce_dgrad instead.
-
     """
 
-    if async_grad_allreduce is not None:
-        warnings.warn(
-            "async_grad_allreduce is deprecated, not in use anymore and will"
-            " be fully removed with 0.11.0. Please use allreduce_dgrad instead."
-        )
-
     assert grad_output_buffer is None, (
         "grad_output_buffer kwarg is only supported with "
         "linear_with_grad_accumulation_and_async_allreduce"
@@ -643,7 +633,6 @@ def linear_with_grad_accumulation_and_async_allreduce(
     sequence_parallel: bool,
     grad_output_buffer: Optional[List[torch.Tensor]] = None,
     wgrad_deferral_limit: Optional[int] = 0,
-    async_grad_allreduce: Optional[bool] = None,
     tp_group: Optional[torch.distributed.ProcessGroup] = None,
 ) -> torch.Tensor:
     """Linear layer execution with asynchronous communication and
@@ -707,17 +696,8 @@ def linear_with_grad_accumulation_and_async_allreduce(
         wgrad_deferral_limit (int optional): Limit on the number of
             micro-batches for which embedding weight gradient GEMM should be
             deferred. Disable by setting this to 0. Defaults to 0.
-
-        async_grad_allreduce (bool optional): Will be removed with 0.11.0.
-                                            Please use allreduce_dgrad instead.
     """
 
-    if async_grad_allreduce is not None:
-        warnings.warn(
-            "async_grad_allreduce is deprecated, not in use anymore and will"
-            " be fully removed with 0.11.0. Please use allreduce_dgrad instead."
-        )
-
     tp_group = get_tensor_model_parallel_group_if_none(tp_group)
 
     args = [
@@ -1092,12 +1072,16 @@ def get_extra_state(self) -> None:
         """Keep compatibility with TE state dict."""
         return None
 
-    def __repr__(self):
+    @override
+    def extra_repr(self) -> str:
+        """Extra context to add to the module's string representation."""
         tp = self.output_size // self.output_size_per_partition
         use_bias = self.bias is not None and self.bias is True
         return (
-            f"{type(self).__name__}(in_features={self.input_size}, "
-            f"out_features={self.output_size}, bias={use_bias}, TP={tp})"
+            f"in_features={self.input_size}, "
+            f"out_features={self.output_size}, "
+            f"bias={use_bias}, "
+            f"TP={tp}"
         )
 
 
@@ -1150,7 +1134,7 @@ def __init__(
         stride: int = 1,
         keep_master_weight_for_test: bool = False,
         is_expert: bool = False,
-        tp_comm_buffer_name: str = None,  # Not used
+        tp_comm_buffer_name: str | None = None,  # Not used
         tp_group: Optional[torch.distributed.ProcessGroup] = None,
     ):
         super(RowParallelLinear, self).__init__()
@@ -1266,7 +1250,7 @@ def _forward_impl(self, input, weight, *args, **kwargs):
         else:
             return linear_with_grad_accumulation_and_async_allreduce(input, weight, *args, **kwargs)
 
-    def forward(self, input_):
+    def forward(self, input_: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """Forward of RowParallelLinear
 
         Args:
@@ -1324,6 +1308,13 @@ def forward(self, input_):
             output_bias = self.bias
         return output, output_bias
 
+    def backward_dw(self) -> None:
+        """Compute weight gradients during the backward pass if delay_wgrad_compute is enabled.
+
+        Not supported - does nothing.
+        """
+        pass
+
     def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
         """Sharding along axis 1, bias not sharded"""
         state_dict = self.state_dict(prefix="", keep_vars=True)
@@ -1343,10 +1334,14 @@ def get_extra_state(self) -> None:
         """Keep compatibility with TE state dict."""
         return None
 
-    def __repr__(self):
+    @override
+    def extra_repr(self) -> str:
+        """Extra context to add to the module's string representation."""
         tp = self.input_size // self.input_size_per_partition
         use_bias = self.bias is not None and self.bias is True
         return (
-            f"{type(self).__name__}(in_features={self.input_size}, "
-            f"out_features={self.output_size}, bias={use_bias}, TP={tp})"
+            f"in_features={self.input_size}, "
+            f"out_features={self.output_size}, "
+            f"bias={use_bias}, "
+            f"TP={tp}"
         )
diff --git a/megatron/core/tensor_parallel/random.py b/megatron/core/tensor_parallel/random.py
index bc11c9fbb61..dd8be947834 100644
--- a/megatron/core/tensor_parallel/random.py
+++ b/megatron/core/tensor_parallel/random.py
@@ -2,16 +2,19 @@
 
 # Parts of the code here are adapted from PyTorch
 # repo: https://github.com/pytorch/pytorch
+from __future__ import annotations
 
 import contextlib
 import logging
-from typing import Optional, Union
+from collections.abc import Callable
+from typing import Any, Optional, TypeVar, Union
 
 import torch
 from torch import _C
 from torch.cuda import _lazy_call, _lazy_init
 from torch.cuda import device as device_ctx_manager
 from torch.utils.checkpoint import detach_variable
+from typing_extensions import TypeVarTuple, Unpack
 
 from megatron.core.parallel_state import (
     get_expert_model_parallel_rank,
@@ -493,6 +496,10 @@ def is_checkpointing():
     return IS_CHECKPOINTING
 
 
+_R = TypeVar('_R')
+_Ts = TypeVarTuple('_Ts')
+
+
 class CheckpointFunction(torch.autograd.Function):
     """Checkpoint Function
 
@@ -503,7 +510,12 @@ class CheckpointFunction(torch.autograd.Function):
 
     # pylint: disable=missing-function-docstring
     @staticmethod
-    def forward(ctx, run_function, distribute_saved_activations, *args):
+    def forward(
+        ctx: Any,
+        run_function: Callable[[Unpack[_Ts]], _R],
+        distribute_saved_activations: bool,
+        *args: Unpack[_Ts],
+    ) -> _R:
         """Forward pass."""
         _set_checkpointing()
 
@@ -570,7 +582,9 @@ def backward(ctx, *args):
         return (None, None) + grads
 
 
-def checkpoint(function, distribute_saved_activations, *args):
+def checkpoint(
+    function: Callable[[Unpack[_Ts]], _R], distribute_saved_activations: bool, *args: Unpack[_Ts]
+) -> _R:
     """Checkpoint a model or part of the model.
     This has been directly copied from torch.utils.checkpoint."""
     return CheckpointFunction.apply(function, distribute_saved_activations, *args)
@@ -578,12 +592,17 @@ def checkpoint(function, distribute_saved_activations, *args):
 
 class CheckpointWithoutOutputFunction(torch.autograd.Function):
     """
-    Checkpoint Function Helper for CheckpointWithouOutput.
+    Checkpoint Function Helper for CheckpointWithoutOutput.
     Save context for recompute.
     """
 
     @staticmethod
-    def forward(ctx, run_function, checkpoint_without_output_obj, *args):
+    def forward(
+        ctx: Any,
+        run_function: Callable[[Unpack[_Ts]], _R],
+        checkpoint_without_output_obj: CheckpointWithoutOutput,
+        *args: Unpack[_Ts],
+    ) -> _R:
         """Forward pass."""
         if checkpoint_without_output_obj.fp8:
             fp8 = FP8GlobalStateManager.is_fp8_enabled()
@@ -641,7 +660,7 @@ def __init__(self, fp8=False):
         self.ctx = None
         self.outputs = None
 
-    def checkpoint(self, run_function, *args):
+    def checkpoint(self, run_function: Callable[[Unpack[_Ts]], _R], *args: Unpack[_Ts]) -> _R:
         """Checkpoint function."""
 
         # If in cuda graph warmup, disable checkpointing, as 'discard_output_and_register_recompute'
diff --git a/megatron/core/tokenizers/megatron_tokenizer.py b/megatron/core/tokenizers/megatron_tokenizer.py
index 14b273e909b..31694d91af0 100644
--- a/megatron/core/tokenizers/megatron_tokenizer.py
+++ b/megatron/core/tokenizers/megatron_tokenizer.py
@@ -1,5 +1,6 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
+import importlib
 import json
 import logging
 import os
@@ -10,15 +11,25 @@
 
 TOKENIZER_MAPPING_NAMES = OrderedDict(
     [
-        ("default", "DefaultTokenizerText"),
+        ("default-text", "DefaultTokenizerText"),
         ("gpt", "GPTTokenizer"),
         ("mamba", "MambaTokenizer"),
         ("bert", "BertTokenizer"),
         ("t5", "T5Tokenizer"),
+        ("default-vision", "DefaultTokenizerVision"),
     ]
 )
 
-TOKENIZER_LIBRARIES = ["sentencepiece", "huggingface", "megatron", "tiktoken", "byte-level", "null"]
+TEXT_LIBRARIES = [
+    "sentencepiece",
+    "huggingface",
+    "megatron",
+    "tiktoken",
+    "byte-level",
+    "null-text",
+    "sft",
+]
+VISION_LIBRARIES = ["multimodal", "null-multimodal"]
 
 logger = logging.getLogger(__name__)
 
@@ -69,19 +80,22 @@ def from_pretrained(
                 f"Expected metadata_path to be str or dict, but got {type(metadata_path)}."
             )
 
-        if metadata.get('library', None) not in ['byte-level', 'null']:
+        tokenizer_library = metadata.get('library', None)
+        if tokenizer_library not in ['byte-level', 'null-text', 'null-multimodal']:
             assert tokenizer_path, "Tokenizer path must be specified."
 
+        if tokenizer_library in ['multimodal']:
+            assert 'prompt_format' in kwargs, "Prompt format (`prompt_format`) must be specified."
+            assert (
+                'special_tokens' in kwargs
+            ), "Special tokens (`special_tokens`) must be specified."
+            assert (
+                'image_tag_type' in kwargs
+            ), "Image tag type (`image_tag_type`) must be specified."
+
         # Initialize tokenizer object
-        if metadata.get('tokenizer_class', None):
-            tokenizer_cls = getattr(
-                metadata['tokenizer_class_path'], metadata['tokenizer_class_name']
-            )
-        else:
-            import megatron.core.tokenizers.text.models as models
+        tokenizer_cls = _get_tokenizer_model_class(tokenizer_library, metadata)
 
-            model_type = metadata.get('model_type', 'default')
-            tokenizer_cls = getattr(models, TOKENIZER_MAPPING_NAMES[model_type])
         metadata['metadata_path'] = metadata_path
         tokenizer = tokenizer_cls(path=tokenizer_path, config=metadata, **kwargs)
 
@@ -122,12 +136,13 @@ def write_metadata(
         assert os.path.exists(
             tokenizer_path
         ), "Tokenizer path doesn't exist. Please, provide the correct path to the tokenizer."
-        assert tokenizer_library in TOKENIZER_LIBRARIES, (
+        assert tokenizer_library in TEXT_LIBRARIES or tokenizer_library in VISION_LIBRARIES, (
             "Tokenizer library is not supported. Please, see the list of available "
-            f"tokenizer libraries: {TOKENIZER_LIBRARIES}."
+            f"tokenizer libraries: text: {TEXT_LIBRARIES}, vision: {VISION_LIBRARIES}."
         )
+        tokenizer_type = 'text' if tokenizer_library in TEXT_LIBRARIES else 'vision'
         if model_type is None and tokenizer_class is None:
-            model_type = "default"
+            model_type = f"default-{tokenizer_type}"
 
         # Write metadata
         if not metadata_path:
@@ -168,3 +183,33 @@ def _get_metadata_path(tokenizer_path: str) -> str:
     metadata_path = f'{dir_path}/tokenizer_metadata.json'
 
     return metadata_path
+
+
+def _get_tokenizer_model_class(library: str, metadata: dict) -> MegatronTokenizerBase:
+    """
+    Returns a class which corresponds to choosen tokenizer model type.
+
+    Args:
+        library (str): tokenizer library.
+        metadata (dict): tokenizer metadata.
+
+    Returns:
+        MegatronTokenizerBase: class for choosen tokenizer model type.
+    """
+    # Return tokenizer class if it was specified in metadata.
+    if metadata.get('tokenizer_class', None):
+        return getattr(metadata['tokenizer_class_path'], metadata['tokenizer_class_name'])
+
+    # Define tokenizer type
+    tokenizer_type = 'text' if library in TEXT_LIBRARIES else 'vision'
+
+    module_name = f"megatron.core.tokenizers.{tokenizer_type}.models"
+    models = importlib.import_module(module_name)
+
+    model_type = metadata.get("model_type", None)
+    if model_type is None:
+        model_type = f"default-{tokenizer_type}"
+
+    tokenizer_cls = getattr(models, TOKENIZER_MAPPING_NAMES[model_type])
+
+    return tokenizer_cls
diff --git a/megatron/core/tokenizers/text/libraries/__init__.py b/megatron/core/tokenizers/text/libraries/__init__.py
index e9141b81b8d..92a97b31f9a 100644
--- a/megatron/core/tokenizers/text/libraries/__init__.py
+++ b/megatron/core/tokenizers/text/libraries/__init__.py
@@ -5,4 +5,5 @@
 from megatron.core.tokenizers.text.libraries.megatron_hf_tokenizer import MegatronHFTokenizer
 from megatron.core.tokenizers.text.libraries.null_tokenizer import NullTokenizer
 from megatron.core.tokenizers.text.libraries.sentencepiece_tokenizer import SentencePieceTokenizer
+from megatron.core.tokenizers.text.libraries.sft_tokenizer import SFTTokenizer
 from megatron.core.tokenizers.text.libraries.tiktoken_tokenizer import TikTokenTokenizer
diff --git a/megatron/core/tokenizers/text/libraries/huggingface_tokenizer.py b/megatron/core/tokenizers/text/libraries/huggingface_tokenizer.py
index 965f43733a6..4e3387b125c 100644
--- a/megatron/core/tokenizers/text/libraries/huggingface_tokenizer.py
+++ b/megatron/core/tokenizers/text/libraries/huggingface_tokenizer.py
@@ -36,9 +36,9 @@ def __init__(
         cls_token: Optional[str] = None,
         unk_token: Optional[str] = None,
         additional_special_tokens: Optional[List] = [],
-        use_fast: Optional[bool] = False,
+        use_fast: Optional[bool] = True,
         trust_remote_code: Optional[bool] = False,
-        include_special_tokens: bool = False,
+        include_special_tokens: bool = True,
         chat_template: str = None,
     ):
         """
@@ -249,13 +249,22 @@ def text_to_ids(self, text: str) -> List[int]:
         ids = self.tokens_to_ids(tokens)
         return ids
 
-    def ids_to_text(self, ids: List[int], remove_special_tokens: bool = True) -> str:
-        """Converts list of ids to text."""
+    def ids_to_text(self, ids: List[int], remove_special_tokens: Optional[bool] = None) -> str:
+        """Converts list of ids to text.
+
+        When remove_special_tokens is None, uses not self.include_special_tokens so that
+        --tokenizer-hf-include-special-tokens keeps EOS (and other special tokens) in
+        detokenized output (e.g. for RL trajectory consistency).
+        """
+        if remove_special_tokens is None:
+            remove_special_tokens = not self.include_special_tokens
         tokens = self.ids_to_tokens(ids)
         if remove_special_tokens:
-            tokens_clean = [t for t in tokens if t not in self.tokenizer.all_special_tokens]
+            tokens_clean = [
+                t for t in tokens if t is not None and t not in self.tokenizer.all_special_tokens
+            ]
         else:
-            tokens_clean = tokens
+            tokens_clean = [t for t in tokens if t is not None]
         text = self.tokens_to_text(tokens_clean)
         return text
 
diff --git a/megatron/core/tokenizers/text/libraries/sentencepiece_tokenizer.py b/megatron/core/tokenizers/text/libraries/sentencepiece_tokenizer.py
index c7a5fe0b7dc..feaf1c4e9a1 100644
--- a/megatron/core/tokenizers/text/libraries/sentencepiece_tokenizer.py
+++ b/megatron/core/tokenizers/text/libraries/sentencepiece_tokenizer.py
@@ -310,6 +310,10 @@ def add_special_tokens(self, special_tokens: Union[list, dict]) -> None:
                 "Expected special_tokens to be a list or a dict " + str(type(special_tokens))
             )
 
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Calculate offsets."""
+        return [p.begin for p in self.tokenizer.decode_ids_as_immutable_proto(ids).pieces]
+
     @property
     def pad_id(self) -> int:
         """Returns id of padding token."""
diff --git a/megatron/training/tokenizer/sft_tokenizer.py b/megatron/core/tokenizers/text/libraries/sft_tokenizer.py
similarity index 63%
rename from megatron/training/tokenizer/sft_tokenizer.py
rename to megatron/core/tokenizers/text/libraries/sft_tokenizer.py
index 274c6f6c944..8a418f2dd7f 100644
--- a/megatron/training/tokenizer/sft_tokenizer.py
+++ b/megatron/core/tokenizers/text/libraries/sft_tokenizer.py
@@ -1,24 +1,52 @@
-# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
-"""SFT tokenizer."""
+from dataclasses import dataclass
 from typing import Dict, List, Union
+
 import numpy as np
 
-nemotron_h_aligned_custom_template = """{% for message in messages %}{% if message['role'] == 'system' %}{{ '<SPECIAL_10>System\n' + message['content'].strip() + '\n' }}{% elif message['role'] == 'user' %}{{ '<SPECIAL_11>User\n' + message['content'].strip() + '\n' + '<SPECIAL_11>Assistant\n' }}{% elif message['role'] == 'assistant' %}{{ message['content'].strip() + '\n' }}{% endif %}{% endfor %}"""
-nemotron_nano_v2_custom_template = """{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'system' %}{{ '<SPECIAL_10>System\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}{% elif message['role'] == 'user' %}{{ '<SPECIAL_11>User\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}{% elif message['role'] == 'assistant' %}{{ '<SPECIAL_11>Assistant\n' + content.strip() + '\n<SPECIAL_12>\n' }}{% endif %}{% endfor %}"""
+try:
+    import transformers
+
+    HAVE_TRANSFORMERS = True
+except ModuleNotFoundError:
+    HAVE_TRANSFORMERS = False
+
+
+# fmt: off
+nemotron_h_aligned_custom_template = """{% for message in messages %}{% if message['role'] == 'system' %}{{ '<SPECIAL_10>System\n' + message['content'].strip() + '\n' }}{% elif message['role'] == 'user' %}{{ '<SPECIAL_11>User\n' + message['content'].strip() + '\n' + '<SPECIAL_11>Assistant\n' }}{% elif message['role'] == 'assistant' %}{{ message['content'].strip() + '\n' }}{% endif %}{% endfor %}""" # pylint: disable=line-too-long
+nemotron_nano_v2_custom_template = """{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'system' %}{{ '<SPECIAL_10>System\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}{% elif message['role'] == 'user' %}{{ '<SPECIAL_11>User\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}{% elif message['role'] == 'assistant' %}{{ '<SPECIAL_11>Assistant\n' + content.strip() + '\n<SPECIAL_12>\n' }}{% endif %}{% endfor %}""" # pylint: disable=line-too-long
+identity_template = """{% for message in messages %}{{ message['content'] }}{% endfor %}"""
+# fmt: on
+
+
+IGNORE_INDEX = -100
+
+
+@dataclass
+class PromptConfig:
+    """Config options for different prompt formats."""
 
-from megatron.core.datasets.megatron_tokenizer import MegatronLegacyTokenizer
-from megatron.training.datasets.sft_dataset import IGNORE_INDEX
-from megatron.training.tokenizer.multimodal_tokenizer import PromptConfig
+    # How many tokens are used for the assistant prefix, e.g. "<|im_start|>assistant\n".
+    # Used for masking the assistant prefix.
+    assistant_prefix_len: int
+    # Padding token ID.
+    pad_token_id: int
+    # For overriding the default chat format template.
+    custom_chat_template: str
+    # If the tokenizer inserts BOS token by default.
+    has_bos: bool
+    # If the tokenizer supports a separate role for system messages.
+    has_system_role: bool
+    # Wether to force a specific system message.
+    force_system_message: bool = False
+    system_default: dict = None
 
-class SFTTokenizer(MegatronLegacyTokenizer):  
+
+class SFTTokenizer:
     """SFT Tokenizer."""
 
-    def __init__(
-        self,
-        tokenizer_path: str,
-        prompt_format: str,
-    ):
+    def __init__(self, tokenizer_path: str, prompt_format: str):
         """
         Note: Currently, only HuggingFaceTokenizer is supported as the underlying text tokenizer.
 
@@ -26,19 +54,16 @@ def __init__(
             tokenizer_path (str): Underlying tokenizer path.
             prompt_format (str): Prompt format for the tokenizer.
         """
-        super().__init__(tokenizer_path, prompt_format=prompt_format)
-        try:
-            import transformers
-        except ImportError:
+        if HAVE_TRANSFORMERS:
+            # Currently, only HuggingFace tokenizers are supported.
+            tokenizer = transformers.AutoTokenizer.from_pretrained(
+                pretrained_model_name_or_path=tokenizer_path
+            )
+        else:
             raise ImportError(
                 "SFTTokenizer currently requires transformers library to be installed"
             )
 
-        # Currently, only HuggingFace tokenizers are supported.
-        tokenizer = transformers.AutoTokenizer.from_pretrained(
-            pretrained_model_name_or_path=tokenizer_path,
-        )
-
         self._vocab_size = len(tokenizer)
         self._tokenizer = tokenizer
 
@@ -58,12 +83,31 @@ def __init__(
                 has_bos=False,
                 has_system_role=True,
             )
+        elif prompt_format == "identity":
+            self._prompt_config = PromptConfig(
+                assistant_prefix_len=0,
+                pad_token_id=tokenizer.convert_tokens_to_ids("<unk>"),
+                custom_chat_template=identity_template,
+                has_bos=False,
+                has_system_role=True,
+            )
+        elif prompt_format == "default":
+            self._prompt_config = PromptConfig(
+                assistant_prefix_len=0,
+                pad_token_id=(
+                    tokenizer.pad_token_id
+                    if tokenizer.pad_token_id is not None
+                    else tokenizer.eos_token_id
+                ),
+                custom_chat_template=tokenizer.chat_template,
+                has_bos=tokenizer.bos_token_id is not None,
+                has_system_role=True,
+            )
         else:
             raise NotImplementedError("unknown SFT prompt format", prompt_format)
 
         self._prompt_format = prompt_format
 
-
     def tokenize_conversation(
         self, conversation: List[Dict], return_target: bool, add_generation_prompt: bool
     ):
@@ -98,14 +142,19 @@ def tokenize_conversation(
 
         target = tokens.copy()
 
+        # When using the default prompt format, we do not replace any tokens with IGNORE_INDEX.
+        # Instead, all token losses will be used for simplicity.
+        if self._prompt_format == "default":
+            return tokens, target
+
         # Mask system and user tokens in the target.
         idx = 0
         for turn_idx, turn in enumerate(conversation):
-            
+
             if turn["role"].lower() == "assistant" and len(turn["content"]) == 0:
                 raise ValueError(f"empty assistant turn in conversation: {conversation}.")
             if turn["role"].lower() == "assistant":
-                assert conversation[turn_idx-1]["role"].lower() == "user"
+                assert conversation[turn_idx - 1]["role"].lower() in ("user", "tool")
 
             turn_tokens = self._tokenizer.apply_chat_template(
                 [turn], tokenize=True, chat_template=self._prompt_config.custom_chat_template
@@ -118,55 +167,69 @@ def tokenize_conversation(
             turn_len = len(turn_tokens)
 
             role = turn["role"].lower()
-            if role in ("system", "user"):
+            if role in ("system", "user", "tool"):
                 target[idx : idx + turn_len] = IGNORE_INDEX
             elif role == "assistant":
                 if self._prompt_config.assistant_prefix_len > 0:
                     target[idx : idx + self._prompt_config.assistant_prefix_len] = IGNORE_INDEX
             else:
-                raise ValueError(f"Wrong role value.")
+                raise ValueError("Wrong role value.")
 
             assert np.allclose(
                 tokens[idx : idx + turn_len], turn_tokens
             ), f"expected turn tokens to match tokens in conversation {conversation}"
 
             idx += turn_len
-        
+
         assert idx == len(tokens), f"mismatch in target masking the conversation {conversation}"
 
         return tokens, target
 
-    def tokenize(self, text: Union[str, List[Dict]]):
+    def text_to_ids(self, text: Union[str, List[Dict]]):
         """Tokenize conversation or string input."""
         if isinstance(text, list):
             # This code path is used by the inference code currently.
-            return self.tokenize_conversation(text, return_target=False, add_generation_prompt=True).tolist()
-
-        return self._encode(text)
+            return self.tokenize_conversation(
+                text, return_target=False, add_generation_prompt=True
+            ).tolist()
 
-    def _encode(self, text: str):
-        """Tokenize text input, w/o chat template"""
         return self._tokenizer.encode(text)
 
-    def convert_tokens_to_ids(self, tokens: List[str]):
+    def tokens_to_ids(self, tokens: List[str]):
         """Convert tokens to IDs."""
         return self._tokenizer.convert_tokens_to_ids(tokens)
 
-    def detokenize(self, tokens: List[int]):
+    def ids_to_text(self, tokens: List[int]):
         """Detokenize tokens."""
         return self._tokenizer.decode(tokens)
 
+    def ids_to_tokens(self):
+        """Converts ids to tokens."""
+        raise NotImplementedError("This method is not supported for SFTTokenizer.")
+
+    def text_to_tokens(self):
+        """Converts text to tokens."""
+        raise NotImplementedError("This method is not supported for SFTTokenizer.")
+
+    def tokens_to_text(self):
+        """Converts tokens to text."""
+        raise NotImplementedError("This method is not supported for SFTTokenizer.")
+
     def get_special_tokens(self):
         """Get special tokens."""
         return self._tokenizer.get_added_vocab()
 
+    def add_special_tokens(self):
+        """Add special tokens."""
+        raise NotImplementedError("This method is not supported for SFTTokenizer.")
+
     @property
-    def pad(self):
+    def pad_id(self):
         """Pad token ID."""
         return self._prompt_config.pad_token_id
 
     @property
-    def bos(self):
+    def bos_id(self):
         """Beginning of sequence token ID."""
         return self._tokenizer.bos_token_id
 
diff --git a/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py b/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
index 20e13206ceb..48c46e9949f 100644
--- a/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
+++ b/megatron/core/tokenizers/text/libraries/tiktoken_tokenizer.py
@@ -15,6 +15,8 @@
 from .abstract_tokenizer import MegatronTokenizerTextAbstract
 from .chat_template import MegatronTokenizerChatTemplate
 
+logger = logging.getLogger(__name__)
+
 PATTERN_TIKTOKEN_V1 = (
     r"[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+"
 )
@@ -156,9 +158,7 @@ def __init__(
 
         self.shifted_id2token = {i: tok for i, tok in enumerate(self.special_tokens)}
         for key, value in self.id2token.items():
-            self.shifted_id2token[key + self.num_special_tokens] = value.decode(
-                'utf-8', errors='replace'
-            )
+            self.shifted_id2token[key + self.num_special_tokens] = value
 
         special_tokens_dict = {t: i for i, t in enumerate(self.special_tokens)}
         self.tokenizer = tiktoken.Encoding(
@@ -168,6 +168,8 @@ def __init__(
             special_tokens=special_tokens_dict,  # special tokens are handled manually
         )
 
+        self._vocab = special_tokens_dict | self.token2id
+
     def text_to_tokens(self, text: str) -> List[str]:
         """Converts text to tokens."""
         token_ids = self.tokenizer.encode(text)
@@ -232,6 +234,24 @@ def add_special_tokens(self, special_tokens_dict: dict):
         """Adds special tokens to the tokenizer."""
         raise NotImplementedError("This method is not supported for TikToken tokenizers.")
 
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Calculate offsets."""
+        try:
+            return self.tokenizer.decode_with_offsets(ids)[1]
+        except UnicodeDecodeError:
+            # Tiktoken has an unnecessary check that raises UnicodeDecodeError
+            # from `text = b"".join(token_bytes).decode("utf-8", errors="strict")`
+            # which is not needed for our use case. So we re-implement it, without
+            # the check.
+
+            token_bytes = self.tokenizer.decode_tokens_bytes(ids)
+            text_len = 0
+            offsets = []
+            for token in token_bytes:
+                offsets.append(max(0, text_len - (0x80 <= token[0] < 0xC0)))
+                text_len += sum(1 for c in token if not 0x80 <= c < 0xC0)
+            return offsets
+
     @property
     def additional_special_tokens_ids(self) -> list:
         """
@@ -291,7 +311,7 @@ def sep_id(self) -> int:
     @property
     def vocab(self):
         """Returns tokenizer vocab."""
-        return self.token2id
+        return self._vocab
 
     @property
     def decoder(self):
@@ -301,7 +321,7 @@ def decoder(self):
     @property
     def encoder(self):
         """ """
-        return self.vocab
+        return self._vocab
 
     @property
     def vocab_size(self) -> int:
diff --git a/megatron/core/tokenizers/text/parsers/__init__.py b/megatron/core/tokenizers/text/parsers/__init__.py
new file mode 100644
index 00000000000..dc27763f905
--- /dev/null
+++ b/megatron/core/tokenizers/text/parsers/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from megatron.core.tokenizers.text.parsers.deepseek_r1_reasoning_parser import (
+    DeepSeekR1ReasoningParser,
+)
+from megatron.core.tokenizers.text.parsers.qwen3_coder_tool_parser import Qwen3CoderToolParser
+
+PARSER_MAPPING = {
+    "deepseek-r1-reasoning": DeepSeekR1ReasoningParser,
+    "qwen3-coder-tool": Qwen3CoderToolParser,
+}
+
+__all__ = ["PARSER_MAPPING"]
diff --git a/megatron/core/tokenizers/text/parsers/base_parser.py b/megatron/core/tokenizers/text/parsers/base_parser.py
new file mode 100644
index 00000000000..afd847d1bfc
--- /dev/null
+++ b/megatron/core/tokenizers/text/parsers/base_parser.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from typing import Any
+
+
+class BaseParser:
+    """Base class for text parsers."""
+
+    @staticmethod
+    def parse(text: str, **kwargs) -> tuple[str, dict[str, Any]]:
+        """
+        Parses the text into a tuple containing extracted content
+        and a dictionary of additional information.
+
+        Args:
+            text (str): The text to parse.
+
+        Returns:
+            tuple[str, dict[str, Any]]: A tuple containing the unprocessed text
+            and a dictionary with the extracted information.
+        """
+        return text, {}
diff --git a/megatron/core/tokenizers/text/parsers/deepseek_r1_reasoning_parser.py b/megatron/core/tokenizers/text/parsers/deepseek_r1_reasoning_parser.py
new file mode 100644
index 00000000000..17952c61daf
--- /dev/null
+++ b/megatron/core/tokenizers/text/parsers/deepseek_r1_reasoning_parser.py
@@ -0,0 +1,33 @@
+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from megatron.core.tokenizers.text.parsers.base_parser import BaseParser
+
+
+class DeepSeekR1ReasoningParser(BaseParser):
+    """Parser for DeepSeek R1 style reasoning output."""
+
+    @staticmethod
+    def parse(text: str, **kwargs) -> tuple[str, dict[str, str]]:
+        """
+        Extracts the reasoning content from the text using <think>...</think> tags.
+        Only extracts the first set of think tags.
+        If an initial <think> tag is not present but a </think> tag is,
+        it will infer a <think> tag at the beginning of the text.
+
+        Args:
+            text (str): The text to parse.
+
+        Returns:
+            tuple[str, dict[str, str]]: A tuple containing the unprocessed text
+            and a dictionary with the extracted reasoning content.
+        """
+
+        if "</think>" in text:
+            if "<think>" in text:
+                # Strip the <think> prefix (it might not be present if it was part of the prompt)
+                pre_text, text = text.split("<think>", maxsplit=1)
+            else:
+                pre_text = ""
+            reasoning_content, remaining_text = text.split("</think>", maxsplit=1)
+            return pre_text + remaining_text, {'reasoning': reasoning_content}
+        else:
+            return text, {}
diff --git a/megatron/core/tokenizers/text/parsers/qwen3_coder_tool_parser.py b/megatron/core/tokenizers/text/parsers/qwen3_coder_tool_parser.py
new file mode 100644
index 00000000000..1d1f20a3a5c
--- /dev/null
+++ b/megatron/core/tokenizers/text/parsers/qwen3_coder_tool_parser.py
@@ -0,0 +1,282 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import ast
+import json
+import logging
+import re
+import uuid
+from types import SimpleNamespace
+from typing import Any
+
+from megatron.core.tokenizers.text.parsers.base_parser import BaseParser
+
+logger = logging.getLogger(__name__)
+
+# These map to vLLM types but we just use dictionaries for now
+ToolCall = dict[str, Any]
+FunctionCall = dict[str, Any]
+ChatCompletionToolsParam = dict[str, Any]
+ChatCompletionRequest = dict[str, Any]
+ExtractedToolCallInformation = dict
+
+
+class _Qwen3CoderToolParser:
+
+    # Sentinel tokens for streaming mode
+    tool_call_start_token: str = "<tool_call>"
+    tool_call_end_token: str = "</tool_call>"
+    tool_call_prefix: str = "<function="
+
+    # Regex patterns
+    tool_call_complete_regex = re.compile(r"<tool_call>(.*?)</tool_call>", re.DOTALL)
+    tool_call_regex = re.compile(r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL)
+    tool_call_function_regex = re.compile(r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL)
+    tool_call_parameter_regex = re.compile(
+        r"<parameter=(.*?)(?:</parameter>|(?=<parameter=)|(?=</function>)|$)", re.DOTALL
+    )
+
+    def _generate_tool_call_id(self) -> str:
+        """Generate a unique tool call ID."""
+        return f"call_{uuid.uuid4().hex[:24]}"
+
+    def _get_arguments_config(
+        self, func_name: str, tools: list[ChatCompletionToolsParam] | None
+    ) -> dict:
+        """Extract argument configuration for a function."""
+        if tools is None:
+            return {}
+        for config in tools:
+            config = SimpleNamespace(**config)  # Convert to SimpleNamespace for ease of access
+            if not hasattr(config, "type") or not (
+                hasattr(config, "function") and hasattr(config.function, "name")
+            ):
+                continue
+            if config.type == "function" and config.function.name == func_name:
+                if not hasattr(config.function, "parameters"):
+                    return {}
+                params = config.function.parameters
+                if isinstance(params, dict) and "properties" in params:
+                    return params["properties"]
+                elif isinstance(params, dict):
+                    return params
+                else:
+                    return {}
+        logger.debug("Tool '%s' is not defined in the tools list.", func_name)
+        return {}
+
+    def _convert_param_value(
+        self, param_value: str, param_name: str, param_config: dict, func_name: str
+    ) -> Any:
+        """Convert parameter value based on its type in the schema."""
+        # Handle null value for any type
+        if param_value.lower() == "null":
+            return None
+
+        if param_name not in param_config:
+            if param_config != {}:
+                logger.debug(
+                    "Parsed parameter '%s' is not defined in the tool "
+                    "parameters for tool '%s', directly returning the "
+                    "string value.",
+                    param_name,
+                    func_name,
+                )
+            return param_value
+
+        if isinstance(param_config[param_name], dict) and "type" in param_config[param_name]:
+            param_type = str(param_config[param_name]["type"]).strip().lower()
+        else:
+            param_type = "string"
+        if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+            return param_value
+        elif (
+            param_type.startswith("int")
+            or param_type.startswith("uint")
+            or param_type.startswith("long")
+            or param_type.startswith("short")
+            or param_type.startswith("unsigned")
+        ):
+            try:
+                return int(param_value)
+            except (ValueError, TypeError):
+                logger.debug(
+                    "Parsed value '%s' of parameter '%s' is not an "
+                    "integer in tool '%s', degenerating to string.",
+                    param_value,
+                    param_name,
+                    func_name,
+                )
+                return param_value
+        elif param_type.startswith("num") or param_type.startswith("float"):
+            try:
+                float_param_value = float(param_value)
+                return (
+                    float_param_value
+                    if float_param_value - int(float_param_value) != 0
+                    else int(float_param_value)
+                )
+            except (ValueError, TypeError):
+                logger.debug(
+                    "Parsed value '%s' of parameter '%s' is not a float "
+                    "in tool '%s', degenerating to string.",
+                    param_value,
+                    param_name,
+                    func_name,
+                )
+                return param_value
+        elif param_type in ["boolean", "bool", "binary"]:
+            param_value = param_value.lower()
+            if param_value not in ["true", "false"]:
+                logger.debug(
+                    "Parsed value '%s' of parameter '%s' is not a boolean "
+                    "(`true` or `false`) in tool '%s', degenerating to "
+                    "false.",
+                    param_value,
+                    param_name,
+                    func_name,
+                )
+            return param_value == "true"
+        else:
+            if (
+                param_type in ["object", "array", "arr"]
+                or param_type.startswith("dict")
+                or param_type.startswith("list")
+            ):
+                try:
+                    param_value = json.loads(param_value)
+                    return param_value
+                except (json.JSONDecodeError, TypeError, ValueError):
+                    logger.debug(
+                        "Parsed value '%s' of parameter '%s' cannot be "
+                        "parsed with json.loads in tool '%s', will try "
+                        "other methods to parse it.",
+                        param_value,
+                        param_name,
+                        func_name,
+                    )
+            try:
+                param_value = ast.literal_eval(param_value)  # safer
+            except (ValueError, SyntaxError, TypeError):
+                logger.debug(
+                    "Parsed value '%s' of parameter '%s' cannot be "
+                    "converted via Python `ast.literal_eval()` in tool "
+                    "'%s', degenerating to string.",
+                    param_value,
+                    param_name,
+                    func_name,
+                )
+            return param_value
+
+    def _parse_xml_function_call(
+        self, function_call_str: str, tools: list[ChatCompletionToolsParam] | None
+    ) -> ToolCall | None:
+        # Extract function name
+        end_index = function_call_str.index(">")
+        function_name = function_call_str[:end_index]
+        param_config = self._get_arguments_config(function_name, tools)
+        parameters = function_call_str[end_index + 1 :]
+        param_dict = {}
+        for match_text in self.tool_call_parameter_regex.findall(parameters):
+            idx = match_text.index(">")
+            param_name = match_text[:idx]
+            param_value = str(match_text[idx + 1 :])
+            # Remove prefix and trailing \n
+            if param_value.startswith("\n"):
+                param_value = param_value[1:]
+            if param_value.endswith("\n"):
+                param_value = param_value[:-1]
+
+            param_dict[param_name] = self._convert_param_value(
+                param_value, param_name, param_config, function_name
+            )
+        return ToolCall(
+            type="function",
+            id=self._generate_tool_call_id(),
+            function=FunctionCall(
+                name=function_name, arguments=json.dumps(param_dict, ensure_ascii=False)
+            ),
+        )
+
+    def _get_function_calls(self, model_output: str) -> list[str]:
+        # Find all tool calls
+        matched_ranges = self.tool_call_regex.findall(model_output)
+        raw_tool_calls = [match[0] if match[0] else match[1] for match in matched_ranges]
+
+        # Back-off strategy if no tool_call tags found
+        if len(raw_tool_calls) == 0:
+            raw_tool_calls = [model_output]
+
+        raw_function_calls = []
+        for tool_call in raw_tool_calls:
+            raw_function_calls.extend(self.tool_call_function_regex.findall(tool_call))
+
+        function_calls = [match[0] if match[0] else match[1] for match in raw_function_calls]
+        return function_calls
+
+    def extract_tool_calls(
+        self, model_output: str, tools: list[ChatCompletionToolsParam] | None
+    ) -> ExtractedToolCallInformation:
+        """Extracts the tool calls from the text using <tool_call>...</tool_call> tags."""
+        # Quick check to avoid unnecessary processing
+        if self.tool_call_prefix not in model_output:
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+        try:
+            function_calls = self._get_function_calls(model_output)
+            if len(function_calls) == 0:
+                return ExtractedToolCallInformation(
+                    tools_called=False, tool_calls=[], content=model_output
+                )
+
+            tool_calls = [
+                self._parse_xml_function_call(function_call_str, tools)
+                for function_call_str in function_calls
+            ]
+
+            # Extract content before tool calls
+            content_index = model_output.find(self.tool_call_start_token)
+            idx = model_output.find(self.tool_call_prefix)
+            content_index = content_index if content_index >= 0 else idx
+            content = model_output[:content_index]  # .rstrip()
+
+            return ExtractedToolCallInformation(
+                tools_called=(len(tool_calls) > 0),
+                tool_calls=tool_calls,
+                content=content if content else None,
+            )
+
+        except Exception:
+            logger.exception("Error in extracting tool call from response.")
+            return ExtractedToolCallInformation(
+                tools_called=False, tool_calls=[], content=model_output
+            )
+
+
+class Qwen3CoderToolParser(BaseParser):
+    """Parser for Qwen3 Coder style tool calls."""
+
+    @staticmethod
+    def parse(text: str, **kwargs) -> tuple[str, dict[str, list[dict]]]:
+        """
+        Extracts the tool calls from the text using <tool_call>...</tool_call> tags.
+        Uses the _Qwen3CoderToolParser class (copied from vLLM) to extract the tool calls.
+
+        Args:
+            text (str): The text to parse.
+
+        Returns:
+            tuple[str, dict[str, str]]: A tuple containing the unprocessed text
+            and a dictionary with the extracted tool calls.
+        """
+
+        information = _Qwen3CoderToolParser().extract_tool_calls(
+            text, tools=kwargs.get("tools", [])
+        )
+        if information.get("tools_called", False):
+            return information.get("content", ""), {"tool_calls": information.get("tool_calls", [])}
+        else:
+            return text, {}
diff --git a/megatron/core/tokenizers/text/text_tokenizer.py b/megatron/core/tokenizers/text/text_tokenizer.py
index 4e0c624e006..0145ae353ca 100644
--- a/megatron/core/tokenizers/text/text_tokenizer.py
+++ b/megatron/core/tokenizers/text/text_tokenizer.py
@@ -13,7 +13,8 @@
         ("megatron", "MegatronHFTokenizer"),
         ("tiktoken", "TikTokenTokenizer"),
         ("byte-level", "ByteLevelTokenizer"),
-        ("null", "NullTokenizer"),
+        ("null-text", "NullTokenizer"),
+        ("sft", "SFTTokenizer"),
     ]
 )
 
@@ -56,7 +57,7 @@ def _restore_model(self, **kwargs) -> MegatronTokenizerTextAbstract:
 
         library_class = getattr(tokenizers, TOKENIZER_MAPPING_LIBRARIES[self.library])
 
-        if self.library in ['byte-level', 'null']:
+        if self.library in ['byte-level', 'null-text']:
             return library_class(**kwargs)
         else:
             return library_class(self.path, **kwargs)
@@ -82,7 +83,7 @@ def detokenize(self, ids: List[int]) -> str:
             ids (list): text to be tokenized.
 
         Returns:
-            text: dettokenized text.
+            text: detokenized text.
         """
 
         return self._tokenizer.ids_to_text(ids)
@@ -112,6 +113,32 @@ def apply_chat_template(
             conversation=conversation, chat_template=chat_template, **kwargs
         )
 
+    def tokenize_conversation(
+        self, conversation: List[Dict], return_target: bool, add_generation_prompt: bool
+    ):
+        """Convert a conversation to tokens. Needed for SFTTokenizer.
+
+        Args:
+            conversation (List[Dict]): Sequence of system/user/assistant messages.
+                Must be in the following format:
+                [
+                    {"role": "system", "content": "something"},
+                    {"role": "user", "content": "something1"},
+                    {"role": "assistant", "content": "something2"},
+                ]
+            return_target (bool): Return target tokens with system and assistant masked.
+            add_generation_prompt (bool): Add assistant prefix to the end.
+        """
+
+        if self.library == 'sft':
+            return self._tokenizer.tokenize_conversation(
+                conversation=conversation,
+                return_target=return_target,
+                add_generation_prompt=add_generation_prompt,
+            )
+        else:
+            raise NotImplementedError("This method is supported only for SFTTokenizer.")
+
     def save_pretrained(self, path: str) -> None:
         """
         Saves HF tokenizer files.
@@ -141,6 +168,20 @@ def add_special_tokens(self, special_tokens: Union[list, dict]) -> None:
 
         self._tokenizer.add_special_tokens(special_tokens)
 
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Calculate offsets."""
+        return self._tokenizer.offsets(ids=ids, text=text)
+
+    @property
+    def space_sensitive(self):
+        """Check if tokenizer is space sensetive."""
+        if self.library in ['sentencepiece', 'huggingface']:
+            return self._tokenizer.space_sensitive
+        else:
+            raise NotImplementedError(
+                f"This method is not supported for {self.library} tokenizers."
+            )
+
     @property
     def additional_special_tokens_ids(self) -> list:
         """Returns a list of the additional special tokens."""
diff --git a/megatron/core/tokenizers/text/utils/build_tokenizer.py b/megatron/core/tokenizers/utils/build_tokenizer.py
similarity index 54%
rename from megatron/core/tokenizers/text/utils/build_tokenizer.py
rename to megatron/core/tokenizers/utils/build_tokenizer.py
index 2017945204d..bf02451ae6c 100644
--- a/megatron/core/tokenizers/text/utils/build_tokenizer.py
+++ b/megatron/core/tokenizers/utils/build_tokenizer.py
@@ -1,14 +1,19 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
+import logging
+import math
+
 from megatron.core.tokenizers import MegatronTokenizer
 
 MEGATRON_TOKENIZERS = ['BertWordPieceLowerCase', 'BertWordPieceCase', 'GPT2BPETokenizer']
 
 SP_TOKENIZERS = ['SentencePieceTokenizer', 'GPTSentencePieceTokenizer', 'Llama2Tokenizer']
 
+logger = logging.getLogger(__name__)
+
 
-def build_tokenizer(args):
-    """ """
+def build_tokenizer(args, **kwargs):
+    """Initialize tokenizer."""
     kwargs = {}
     tokenizer_library = None
     tokenizer_path = None
@@ -24,9 +29,9 @@ def build_tokenizer(args):
             kwargs = special_tokens
         kwargs['vocab_file'] = args.vocab_file
         kwargs['merges_file'] = args.merge_file
-        kwargs['use_fast'] = args.tokenizer_hf_use_fast
+        kwargs['use_fast'] = not args.tokenizer_hf_no_use_fast
         kwargs['trust_remote_code'] = args.trust_remote_code
-        kwargs['include_special_tokens'] = args.tokenizer_hf_include_special_tokens
+        kwargs['include_special_tokens'] = not args.tokenizer_hf_no_include_special_tokens
     elif args.tokenizer_type in SP_TOKENIZERS:
         tokenizer_library = 'sentencepiece'
         tokenizer_path = args.tokenizer_model
@@ -49,16 +54,31 @@ def build_tokenizer(args):
         kwargs['additional_special_tokens'] = (
             args.tokenizer_special_tokens if args.tokenizer_special_tokens else []
         )
-        kwargs['use_fast'] = args.tokenizer_hf_use_fast
+        kwargs['use_fast'] = not args.tokenizer_hf_no_use_fast
         kwargs['trust_remote_code'] = args.trust_remote_code
-        kwargs['include_special_tokens'] = args.tokenizer_hf_include_special_tokens
-    elif args.tokenizer_type == 'NullTokenizer':
-        tokenizer_library = 'null'
+        kwargs['include_special_tokens'] = not args.tokenizer_hf_no_include_special_tokens
+    elif args.tokenizer_type == 'MultimodalTokenizer':
+        tokenizer_library = 'multimodal'
+        kwargs['prompt_format'] = args.tokenizer_prompt_format
+        kwargs['special_tokens'] = args.special_tokens
+        kwargs['image_tag_type'] = args.image_tag_type
+        kwargs['force_system_message'] = args.force_system_message
+    elif args.tokenizer_type == 'SFTTokenizer':
+        tokenizer_library = 'sft'
+        tokenizer_path = args.tokenizer_model
+        kwargs['prompt_format'] = args.sft_tokenizer_prompt_format
+    elif args.tokenizer_type in ['NullTokenizer', 'NullMultimodalTokenizer']:
+        tokenizer_library = (
+            'null-text' if args.tokenizer_type == 'NullTokenizer' else 'null-multimodal'
+        )
         metadata = {'library': tokenizer_library}
         if args.vocab_size:
             kwargs['vocab_size'] = args.vocab_size
         tokenizer = MegatronTokenizer.from_pretrained(metadata_path=metadata, **kwargs)
 
+        # Add vocab size (if not already set from a checkpoint).
+        _set_padded_vocab_size(args, tokenizer)
+
         return tokenizer
 
     if args.tokenizer_metadata:
@@ -69,4 +89,29 @@ def build_tokenizer(args):
         tokenizer_path=tokenizer_path, metadata_path=metadata, **kwargs
     )
 
+    # Add vocab size (if not already set from a checkpoint).
+    _set_padded_vocab_size(args, tokenizer)
+
     return tokenizer
+
+
+def vocab_size_with_padding(orig_vocab_size, args, logging_enabled=True):
+    """Pad vocab size so it is divisible by model parallel size and
+    still having GPU friendly size."""
+
+    after = orig_vocab_size
+    multiple = args.make_vocab_size_divisible_by * args.tensor_model_parallel_size
+    after = int(math.ceil(after / multiple) * multiple)
+    if args.rank == 0 and logging_enabled:
+        logger.info(
+            f' > padded vocab (size: {orig_vocab_size}) with '
+            f'{after - orig_vocab_size} dummy tokens '
+            f'(new size: {after})'
+        )
+    return after
+
+
+def _set_padded_vocab_size(args, tokenizer):
+    """Sets padded vocab size if None."""
+    if getattr(args, "padded_vocab_size", None) is None:
+        args.padded_vocab_size = vocab_size_with_padding(tokenizer.vocab_size, args)
diff --git a/megatron/core/tokenizers/vision/__init__.py b/megatron/core/tokenizers/vision/__init__.py
new file mode 100644
index 00000000000..6684427279d
--- /dev/null
+++ b/megatron/core/tokenizers/vision/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.tokenizers.vision.vision_tokenizer import MegatronTokenizerVision
diff --git a/megatron/core/tokenizers/vision/libraries/__init__.py b/megatron/core/tokenizers/vision/libraries/__init__.py
new file mode 100644
index 00000000000..1dd57f4b7e5
--- /dev/null
+++ b/megatron/core/tokenizers/vision/libraries/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.tokenizers.vision.libraries.multimodal_tokenizer import (
+    MegatronMultimodalTokenizer,
+)
+from megatron.core.tokenizers.vision.libraries.null_multimodal_tokenizer import (
+    MegatronNullMultimodalTokenizer,
+)
diff --git a/megatron/training/tokenizer/multimodal_tokenizer.py b/megatron/core/tokenizers/vision/libraries/multimodal_tokenizer.py
similarity index 86%
rename from megatron/training/tokenizer/multimodal_tokenizer.py
rename to megatron/core/tokenizers/vision/libraries/multimodal_tokenizer.py
index 47aae9e43c1..80712351095 100644
--- a/megatron/training/tokenizer/multimodal_tokenizer.py
+++ b/megatron/core/tokenizers/vision/libraries/multimodal_tokenizer.py
@@ -1,16 +1,19 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
 
-"""Multimodal tokenizer."""
-from dataclasses import dataclass
-from typing import Dict, List, Union, Optional
+from typing import Dict, List, Union
 
 import numpy as np
 
-from megatron.core.datasets.megatron_tokenizer import MegatronLegacyTokenizer
-
-# Mark tokens that will be ignored in the loss function with this value.
-# Same ignore_index in https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
 from megatron.core.models.multimodal.llava_model import IGNORE_INDEX, IMAGE_TOKEN
+from megatron.core.tokenizers.text.libraries.sft_tokenizer import PromptConfig
+
+try:
+    import transformers
+
+    HAVE_TRANSFORMERS = True
+except (ImportError, ModuleNotFoundError):
+    HAVE_TRANSFORMERS = False
+
 
 IMAGE_TAGS = {
     "nvlm": ("<Image>", "</Image>"),
@@ -33,62 +36,55 @@
 """
 
 
-nvlm_yi_34b_template = "{{- bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+nvlm_yi_34b_template = "{{- bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"  # pylint: disable=line-too-long
 
 
-qwen2p0_custom_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+qwen2p0_custom_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"  # pylint: disable=line-too-long
 
 
-# Note: this is the same template as https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/tokenizer_config.json#L2053
+# Note: this is the same template as
+# https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/tokenizer_config.json#L2053
 # but we removed the forced system message.
-llama3p1_chat_template = """{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = none %}\n{%- endif %}\n\n{%- if system_message is not none %}{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{%-endif %}{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"""
-
-nemotron_custom_template = "{{- bos_token }}{% for message in messages %}{{'<SPECIAL_14>' + message['role'] + '\n' + message['content'] + '<SPECIAL_15>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<SPECIAL_14>assistant\n' }}{% endif %}"
+llama3p1_chat_template = """{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = none %}\n{%- endif %}\n\n{%- if system_message is not none %}{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{%-endif %}{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"""  # pylint: disable=line-too-long
 
-nemotron_aligned_custom_template = "{{- bos_token}}{% for message in messages %}{{message['role'] + '\n' + message['content'] + '\n' + '[PREFIX]'}}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant\n' }}{% endif %}"
+nemotron_custom_template = "{{- bos_token }}{% for message in messages %}{{'<SPECIAL_14>' + message['role'] + '\n' + message['content'] + '<SPECIAL_15>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<SPECIAL_14>assistant\n' }}{% endif %}"  # pylint: disable=line-too-long
 
+nemotron_aligned_custom_template = "{{- bos_token}}{% for message in messages %}{{message['role'] + '\n' + message['content'] + '\n' + '[PREFIX]'}}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant\n' }}{% endif %}"  # pylint: disable=line-too-long
 
-@dataclass
-class PromptConfig:
-    """Config options for different prompt formats."""
 
-    # How many tokens are used for the assistant prefix, e.g. "<|im_start|>assistant\n".
-    # Used for masking the assistant prefix.
-    assistant_prefix_len: int
-    # Padding token ID.
-    pad_token_id: int
-    # For overriding the default chat format template.
-    custom_chat_template: str
-    # If the tokenizer inserts BOS token by default.
-    has_bos: bool
-    # If the tokenizer supports a separate role for system messages.
-    has_system_role: bool
-    # Wether to force a specific system message.
-    force_system_message: bool = False
-    system_default: dict = None
-
-
-class MultimodalTokenizer(MegatronLegacyTokenizer):
+class MegatronMultimodalTokenizer:
     """Multimodal Tokenizer."""
 
     def __init__(
         self,
-        tokenizer: MegatronLegacyTokenizer,
+        path: str,
         prompt_format: str,
         special_tokens: List[str],
         image_tag_type: str,
         force_system_message: bool = False,
+        **kwargs,
     ):
         """Tokenizer with a support for non-text inputs.
 
         Note: Currently, only HuggingFaceTokenizer is supported as the underlying text tokenizer.
 
         Args:
-            tokenizer (MegatronLegacyTokenizer): Underlying tokenizer.
+            path (str): Path to the underlying tokenizer.
             prompt_format (str): Prompt format for the tokenizer.
             special_tokens (List[str]): Non-text tokens.
             image_tag_type (str): Image tag to apply, if any. For example <img><image></img>.
         """
+        if not HAVE_TRANSFORMERS:
+            raise ImportError(
+                "MegatronMultimodalTokenizer currently requires "
+                "transformers library to be installed."
+            )
+        if prompt_format == "nvlm-yi-34b":
+            kwargs.update({"from_slow": True, "legacy": False, "add_bos_token": True})
+        tokenizer = transformers.AutoTokenizer.from_pretrained(
+            pretrained_model_name_or_path=path, **kwargs
+        )
+
         self._vocab_size = len(tokenizer)
 
         num_added_tokens = tokenizer.add_tokens(special_tokens, special_tokens=True)
@@ -96,7 +92,7 @@ def __init__(
             special_tokens
         ), f"failed to add {len(special_tokens)} special tokens; only added {num_added_tokens}"
 
-        self._tokenizer = tokenizer
+        self.tokenizer = tokenizer
 
         if prompt_format == "mistral":
             # Mistral format doesn't have prefix for the assistant message.
@@ -170,8 +166,10 @@ def __init__(
                 has_bos=False,
                 has_system_role=True,
                 force_system_message=force_system_message,
-                system_default={"role": "system",
-                                "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
+                system_default={
+                    "role": "system",
+                    "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",  # pylint: disable=line-too-long
+                },
             )
         elif prompt_format == "llama3p1":
             # "<|start_header_id|>assistant<|end_header|>\n\n" is the prefix for assistant messages.
@@ -215,7 +213,7 @@ def tokenize(self, text: Union[str, List[Dict]]):
     def _encode(self, text: str):
         """Tokenize text input."""
         text = self._apply_image_tag(text)
-        return self._tokenizer.encode(text)
+        return self.tokenizer.encode(text)
 
     def tokenize_conversation(
         self, conversation: List[Dict], return_target: bool, add_generation_prompt: bool
@@ -237,7 +235,9 @@ def tokenize_conversation(
             conversation = conversation[1:]
 
         if self._prompt_config.force_system_message:
-            assert self._prompt_config.system_default is not None, "Trying to force system message with empty system default"
+            assert (
+                self._prompt_config.system_default is not None
+            ), "Trying to force system message with empty system default"
             if conversation[0]["role"] == "system":
                 conversation[0] = self._prompt_config.system_default
             else:
@@ -251,7 +251,7 @@ def tokenize_conversation(
         # Apply possible image tag.
         conversation = self._apply_image_tag(conversation)
 
-        tokens = self._tokenizer.apply_chat_template(
+        tokens = self.tokenizer.apply_chat_template(
             conversation,
             tokenize=True,
             add_generation_prompt=add_generation_prompt,
@@ -271,7 +271,7 @@ def tokenize_conversation(
             if len(turn["content"]) == 0:
                 raise ValueError(f"empty turn in conversation: {conversation}. Skipping.")
 
-            turn_tokens = self._tokenizer.apply_chat_template(
+            turn_tokens = self.tokenizer.apply_chat_template(
                 [turn], tokenize=True, chat_template=self._prompt_config.custom_chat_template
             )
 
@@ -304,15 +304,19 @@ def tokenize_conversation(
 
     def convert_tokens_to_ids(self, tokens: List[str]):
         """Convert tokens to IDs."""
-        return self._tokenizer.convert_tokens_to_ids(tokens)
+        return self.tokenizer.convert_tokens_to_ids(tokens)
 
     def detokenize(self, tokens: List[int]):
         """Detokenize tokens."""
-        return self._tokenizer.decode(tokens)
+        return self.tokenizer.decode(tokens)
+
+    def add_special_tokens(self, special_tokens: List[str]):
+        """Add special tokens."""
+        self.tokenizer.add_tokens(special_tokens, special_tokens=True)
 
     def get_special_tokens(self):
         """Get special tokens."""
-        return self._tokenizer.get_added_vocab()
+        return self.tokenizer.get_added_vocab()
 
     @property
     def pad(self):
@@ -322,19 +326,14 @@ def pad(self):
     @property
     def eod(self):
         """End of sentence token ID."""
-        return self._tokenizer.eos_token_id
-
-    @property
-    def vocab(self):
-        """Vocab."""
-        return NotImplementedError("not used")
-
-    @property
-    def inv_vocab(self):
-        """Inverse vocab."""
-        return NotImplementedError("not used")
+        return self.tokenizer.eos_token_id
 
     @property
     def vocab_size(self):
         """Vocabulary size."""
         return self._vocab_size
+
+    @property
+    def vocab(self):
+        """Tokenizer vocab."""
+        return self.tokenizer.get_vocab()
diff --git a/megatron/core/tokenizers/vision/libraries/null_multimodal_tokenizer.py b/megatron/core/tokenizers/vision/libraries/null_multimodal_tokenizer.py
new file mode 100644
index 00000000000..9c64c409bb2
--- /dev/null
+++ b/megatron/core/tokenizers/vision/libraries/null_multimodal_tokenizer.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+
+class MegatronNullMultimodalTokenizer:
+    """Megatron Null Multimodal Tokenizer"""
+
+    def __init__(self, vocab_size, image_token=None, image_token_id=None):
+        """ """
+        self._vocab_size_without_eod = int(vocab_size)
+        self._eod_id = self._vocab_size_without_eod
+
+        from megatron.core.models.multimodal.llava_model import (
+            DEFAULT_IMAGE_TOKEN_INDEX,
+            IMAGE_TOKEN,
+        )
+
+        self._image_token = image_token if image_token is not None else IMAGE_TOKEN
+        self._image_token_id = (
+            image_token_id if image_token_id is not None else DEFAULT_IMAGE_TOKEN_INDEX
+        )
+
+    def tokenize(self, text):
+        """
+        Text tokenization.
+
+        Args:
+            text (str | list): text to be tokenized.
+
+        Returns:
+            list: list of ids.
+        """
+        return [int(x) for x in text.split(' ')]
+
+    def detokenize(self, ids):
+        """
+        Text detokenization.
+
+        Args:
+            ids (list): text to be tokenized.
+
+        Returns:
+            text: detokenized text.
+        """
+        text = [str(x) for x in ids]
+        return ' '.join(text)
+
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Offsets calculation."""
+        offsets, start_idx = [], 0
+        for id_ in ids:
+            offsets.append(start_idx)
+            start_idx += 1 + len(str(id_))
+        return offsets
+
+    def convert_tokens_to_ids(self, tokens):
+        """Convert tokens to IDs."""
+        ids = [
+            (int(t) if t != self._image_token else self._image_token_id) for t in tokens.split('  ')
+        ]
+        return ids if len(ids) > 1 else ids[0]
+
+    @property
+    def vocab_size(self):
+        """Vocab size."""
+        return self._vocab_size_without_eod + 1
+
+    @property
+    def cls(self):
+        """CLS token id."""
+        return -1
+
+    @property
+    def sep(self):
+        """SEP token id."""
+        return -1
+
+    @property
+    def mask(self):
+        """MASK token id."""
+        return -1
+
+    @property
+    def eod(self):
+        """EOD token id."""
+        return self._eod_id
+
+    @property
+    def additional_special_tokens_ids(self):
+        """Returns IDs of additional special tokens."""
+        return None
diff --git a/megatron/core/tokenizers/vision/models/__init__.py b/megatron/core/tokenizers/vision/models/__init__.py
new file mode 100644
index 00000000000..b9387786024
--- /dev/null
+++ b/megatron/core/tokenizers/vision/models/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.tokenizers.vision.models.default_tokenizer import DefaultTokenizerVision
diff --git a/megatron/core/tokenizers/vision/models/default_tokenizer.py b/megatron/core/tokenizers/vision/models/default_tokenizer.py
new file mode 100644
index 00000000000..5366820727d
--- /dev/null
+++ b/megatron/core/tokenizers/vision/models/default_tokenizer.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+
+from megatron.core.tokenizers.vision.vision_tokenizer import MegatronTokenizerVision
+
+
+class DefaultTokenizerVision(MegatronTokenizerVision):
+    """Base class for Megatron default vision tokenizer."""
+
+    def __init__(self, path: str = None, config: dict = None, **kwargs) -> None:
+        config['class_name'] = self.__class__.__name__
+        config['class_path'] = self.__class__.__module__
+        super().__init__(path, config, **kwargs)
diff --git a/megatron/core/tokenizers/vision/vision_tokenizer.py b/megatron/core/tokenizers/vision/vision_tokenizer.py
new file mode 100644
index 00000000000..5e1769116a6
--- /dev/null
+++ b/megatron/core/tokenizers/vision/vision_tokenizer.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
+from collections import OrderedDict
+from typing import Dict, List, Union
+
+from megatron.core.tokenizers.base_tokenizer import MegatronTokenizerBase
+
+TOKENIZER_MAPPING_LIBRARIES = OrderedDict(
+    [
+        ("multimodal", "MegatronMultimodalTokenizer"),
+        ("null-multimodal", "MegatronNullMultimodalTokenizer"),
+    ]
+)
+
+
+class MegatronTokenizerVision(MegatronTokenizerBase):
+    """Base class for Megatron vision tokenizers."""
+
+    def __init__(self, path: str, config: dict, **kwargs) -> None:
+        """
+        Args:
+            path (str): path to the tokenizer model.
+            config (dict): tokenizer parameters.
+                library (str): tokenizer library.
+                class_name (str): name of tokenizer class.
+                class_path (str): path to tokenizer class.
+                model_type (str): type of the model to be used with tokenizer.
+        """
+
+        super().__init__(path, config, **kwargs)
+        self._tokenizer = self._restore_model(**kwargs)
+        self.path = path
+
+    def _restore_model(self, **kwargs):
+        """Returns tokenizer library object."""
+
+        import megatron.core.tokenizers.vision.libraries as tokenizers
+
+        library_class = getattr(tokenizers, TOKENIZER_MAPPING_LIBRARIES[self.library])
+
+        if self.library in ['null-multimodal']:
+            return library_class(**kwargs)
+        else:
+            return library_class(self.path, **kwargs)
+
+    def tokenize(self, text: Union[str, List[Dict]]) -> List[int]:
+        """
+        Text tokenization.
+
+        Args:
+            text (str | list): text to be tokenized.
+
+        Returns:
+            list: list of ids.
+        """
+
+        return self._tokenizer.tokenize(text)
+
+    def detokenize(self, ids: List[int]) -> str:
+        """
+        Text detokenization.
+
+        Args:
+            ids (list): text to be tokenized.
+
+        Returns:
+            text: detokenized text.
+        """
+
+        return self._tokenizer.detokenize(ids)
+
+    def tokenize_conversation(
+        self, conversation: List[Dict], return_target: bool, add_generation_prompt: bool
+    ):
+        """Convert a conversation to tokens.
+
+        Args:
+            conversation (List[Dict]): Sequence of system/user/assistant messages.
+                Must be in the following format:
+                [
+                    {"role": "user", "content": "something"},
+                    {"role": "assistant", "content": "something2"},
+                ]
+            return_target (bool): Return target tokens with system and assistant masked.
+            add_generation_prompt (bool): Add assistant prefix to the end.
+        """
+
+        return self._tokenizer.tokenize_conversation(
+            conversation=conversation,
+            return_target=return_target,
+            add_generation_prompt=add_generation_prompt,
+        )
+
+    def add_special_tokens(self, special_tokens: Union[list, dict]) -> None:
+        """
+        Adds a dictionary of special tokens (eos, pad, cls...).
+            Tokens are only added if they are not already in the vocabulary.
+            Indexed starting from the last index of the current vocabulary.
+
+        Args:
+            special_tokens_dict: dict of string. Keys should be in the list of predefined
+                special attributes: [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``,
+                ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``].
+        """
+
+        self._tokenizer.add_special_tokens(special_tokens)
+
+    def convert_tokens_to_ids(self, tokens: List[str]):
+        """Convert tokens to IDs."""
+        return self._tokenizer.convert_tokens_to_ids(tokens)
+
+    def apply_chat_template(self):
+        """Applies tokenizer's chat template."""
+        raise NotImplementedError("This method is not supported for vision tokenizers.")
+
+    def get_special_tokens(self) -> list:
+        """Returns a list of the additional special tokens."""
+        return self._tokenizer.get_special_tokens()
+
+    def offsets(self, ids: list[int], text: str) -> list[int]:
+        """Calculate offsets."""
+        return self._tokenizer.offsets(ids=ids, text=text)
+
+    @property
+    def vocab(self):
+        """Tokenizer vocab."""
+        return self._tokenizer.vocab
+
+    @property
+    def vocab_size(self) -> int:
+        """Returns vocabulary size."""
+        return self._tokenizer.vocab_size
+
+    @property
+    def pad(self):
+        """Pad token ID."""
+        return self._tokenizer.pad
+
+    @property
+    def eod(self):
+        """End of sentence token ID."""
+        return self._tokenizer.eod
diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py
index bc5e4e2ee0d..b8d9ef69443 100644
--- a/megatron/core/transformer/attention.py
+++ b/megatron/core/transformer/attention.py
@@ -34,6 +34,7 @@
 from megatron.core.transformer.identity_op import IdentityOp
 from megatron.core.transformer.module import MegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.typed_torch import apply_module, not_none
 from megatron.core.utils import (
     deprecate_inference_params,
@@ -219,8 +220,8 @@ class SelfAttentionSubmodules:
     linear_qkv: LinearQkvBuilder
     core_attention: CoreAttentionBuilder
     linear_proj: Union[ModuleSpec, type] = None
-    q_layernorm: Union[ModuleSpec, type] = None
-    k_layernorm: Union[ModuleSpec, type] = None
+    q_layernorm: LayerNormBuilder | None = None
+    k_layernorm: LayerNormBuilder | None = None
 
 
 @dataclass
@@ -1283,8 +1284,7 @@ def __init__(
         )
 
         if submodules.q_layernorm is not None:
-            self.q_layernorm = build_module(
-                submodules.q_layernorm,
+            self.q_layernorm = submodules.q_layernorm(
                 hidden_size=self.hidden_size_per_attention_head,
                 config=self.config,
                 eps=self.config.layernorm_epsilon,
@@ -1293,8 +1293,7 @@ def __init__(
             self.q_layernorm = None
 
         if submodules.k_layernorm is not None:
-            self.k_layernorm = build_module(
-                submodules.k_layernorm,
+            self.k_layernorm = submodules.k_layernorm(
                 hidden_size=self.hidden_size_per_attention_head,
                 config=self.config,
                 eps=self.config.layernorm_epsilon,
@@ -1480,10 +1479,10 @@ def get_query_key_value_tensors(
             query = query[:, :, idx * size : (idx + 1) * size, :]
 
         if self.q_layernorm is not None:
-            query = self.q_layernorm(query)
+            query = apply_module(self.q_layernorm)(query)
 
         if self.k_layernorm is not None:
-            key = self.k_layernorm(key)
+            key = apply_module(self.k_layernorm)(key)
 
         if self.config.test_mode:
             self.run_realtime_tests()
@@ -1491,6 +1490,14 @@ def get_query_key_value_tensors(
         if output_gate:
             # Gate [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
             gate = gate.reshape(*gate.shape[:2], -1, self.hidden_size_per_attention_head)
+            if self.config.num_query_groups < self.world_size:
+                idx = get_tensor_model_parallel_rank() % (
+                    self.world_size // self.config.num_query_groups
+                )
+                size = self.num_attention_heads_per_partition // (
+                    self.world_size // self.config.num_query_groups
+                )
+                gate = gate[:, :, idx * size : (idx + 1) * size, :]
             return query, key, value, gate
 
         return query, key, value
diff --git a/megatron/core/transformer/cuda_graphs.py b/megatron/core/transformer/cuda_graphs.py
index 3643c42c3ce..48a023e6ddc 100644
--- a/megatron/core/transformer/cuda_graphs.py
+++ b/megatron/core/transformer/cuda_graphs.py
@@ -242,7 +242,7 @@ def _check_supported_type(meta):
     }
     assert meta.type in _SUPPORTED_TYPES or is_dataclass(
         meta.value
-    ), f"Cudagraphs recieved an arg of type {meta.type} which is not supported."
+    ), f"Cudagraphs received an arg of type {meta.type} which is not supported."
 
 
 def _determine_if_first_last_layer_of_this_vp_chunk(base_module):
@@ -1555,7 +1555,6 @@ def __call__(self, megatron_module, args, kwargs):
             if is_inference_mode:
                 # Inference generation mode creates graphs immediately
                 runner = self.get_cudagraph_runner(megatron_module, args, kwargs, True)
-                runner.eval()
 
                 if not runner.fwd_graph_recorded:
                     # Reuse graph input-output buffers for inference
@@ -1586,6 +1585,7 @@ def __call__(self, megatron_module, args, kwargs):
                     )
                     runner.fwd_graph_recorded = True
                     runner.cudagraph_created = True
+                    runner = runner.eval()
 
                     # Record this to the global execution record
                     _CudagraphGlobalRecord.cudagraph_inference_record.append(
@@ -1738,7 +1738,7 @@ def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]):
                         callables.append(layer)
                         callables_is_mtp.append(False)
                 for layer_number in range(num_mtp_layers):
-                    layer = chunk_with_decoder.mtp.layers[layer_number].transformer_layer
+                    layer = chunk_with_decoder.mtp.layers[layer_number].mtp_model_layer
                     if _layer_is_graphable(layer, config):
                         num_graphable_layers += 1
                         callables.append(layer)
@@ -1855,7 +1855,7 @@ def _get_layer_static_inputs(layer, chunk_of_the_layer):
             Get the static inputs for a layer.
             """
             assert layer in chunk_of_the_layer.decoder.layers or any(
-                layer is mtp_layer.transformer_layer for mtp_layer in chunk_of_the_layer.mtp.layers
+                layer is mtp_layer.mtp_model_layer for mtp_layer in chunk_of_the_layer.mtp.layers
             ), "Layer is not in the chunk"
 
             def get_rotary_pos_emb(transformer_module, transformer_input):
diff --git a/megatron/core/transformer/custom_layers/transformer_engine.py b/megatron/core/transformer/custom_layers/transformer_engine.py
deleted file mode 100644
index 02ce9ad5a75..00000000000
--- a/megatron/core/transformer/custom_layers/transformer_engine.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
-
-import warnings
-
-warnings.warn(
-    """The 'megatron.core.transformer.custom_layers.transformer_engine' 
-    module is deprecated and will be removed in 0.10.0. Please use 
-    'megatron.core.extensions.transformer_engine' instead.""",
-    DeprecationWarning,
-    stacklevel=2,
-)
-from megatron.core.extensions.transformer_engine import *
diff --git a/megatron/core/transformer/enums.py b/megatron/core/transformer/enums.py
index d06d58d65f2..1bf16095908 100644
--- a/megatron/core/transformer/enums.py
+++ b/megatron/core/transformer/enums.py
@@ -9,19 +9,10 @@ class ModelType(enum.Enum):
     """Model Type
 
     encoder_or_decoder for bert, gpt etc
-    encoder_and_decoder for multimodal , T5 etc
     """
 
     encoder_or_decoder = 1
 
-    @property
-    def encoder_and_decoder(self):
-        """Deprecated property - use encoder_or_decoder instead."""
-        raise ValueError(
-            "ModelType.encoder_and_decoder is deprecated. Please use ModelType.encoder_or_decoder "
-            "instead."
-        )
-
 
 class LayerType(enum.Enum):
     """Layer type
@@ -70,10 +61,11 @@ class AttnBackend(enum.Enum):
 class CudaGraphScope(enum.Enum):
     """Cuda Graph Scope - defines which parts of the model to capture."""
 
-    full_iteration = 1  # Captures the entire training/inference iteration
+    full_iteration = 1  # Captures the entire training iteration
     attn = 2  # Captures attention layers
     mlp = 3  # Captures MLP layers (dense layers only)
     moe = 4  # Captures MoE layers (drop-and-pad MoE layers only)
     moe_router = 5  # Captures MoE router part
     moe_preprocess = 6  # Captures MoE preprocessing part (requires moe_router)
     mamba = 7  # Captures Mamba layers
+    full_iteration_inference = 8  # Captures the entire inference iteration
diff --git a/megatron/core/transformer/mlp.py b/megatron/core/transformer/mlp.py
index ad80bcfe4e4..8a19fef87ec 100644
--- a/megatron/core/transformer/mlp.py
+++ b/megatron/core/transformer/mlp.py
@@ -1,10 +1,12 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+from __future__ import annotations
 
 import gc
 import logging
 import warnings
+from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Optional, Union
+from typing import Optional, Protocol, cast
 
 import numpy as np
 import torch
@@ -24,8 +26,8 @@
 from megatron.core.fusions.fused_bias_gelu import bias_gelu_impl
 from megatron.core.fusions.fused_bias_swiglu import bias_swiglu_impl, weighted_bias_swiglu_impl
 from megatron.core.transformer.module import MegatronModule
-from megatron.core.transformer.spec_utils import ModuleSpec, build_module
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.typed_torch import apply_module, not_none
 from megatron.core.utils import (
     get_tensor_model_parallel_group_if_none,
     nvtx_range_pop,
@@ -43,7 +45,91 @@
 logger = logging.getLogger(__name__)
 
 
-# pylint: disable=missing-class-docstring
+class LinearFc1Interface(Protocol):
+    """Interface for linear_fc1 module in MLP."""
+
+    def forward(self, hidden_states: torch.Tensor, /) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Forward method for linear_fc1 module."""
+        ...
+
+    def backward_dw(self) -> None:
+        """Backward method for linear_fc1 module."""
+        ...
+
+
+class LinearFc1Builder(Protocol):
+    """Protocol describing how to build a linear_fc1 layer in MLP."""
+
+    def __call__(
+        self,
+        input_size: int,
+        output_size: int,
+        /,
+        *,
+        config: TransformerConfig,
+        init_method: Callable[[torch.Tensor], None],
+        gather_output: bool,
+        bias: bool,
+        skip_bias_add: bool,
+        is_expert: bool,
+        tp_comm_buffer_name: str | None,
+        tp_group: torch.distributed.ProcessGroup | None,
+        stride: int = 1,
+    ) -> LinearFc1Interface:
+        """Builds a linear_fc1 layer for MLP."""
+        ...
+
+
+class TEActivationFunctionInterface(Protocol):
+    """Interface for activation_function module in MLP."""
+
+    def forward(self, input_: torch.Tensor, /) -> torch.Tensor:
+        """Forward method for activation_function module."""
+        ...
+
+
+class TEActivationFunctionBuilder(Protocol):
+    """Protocol for activation_function module in MLP."""
+
+    def __call__(self, *, config: TransformerConfig) -> TEActivationFunctionInterface:
+        """Builds an activation function module for MLP."""
+        ...
+
+
+class LinearFc2Interface(Protocol):
+    """Interface for linear_fc2 module in MLP."""
+
+    def forward(self, hidden_states: torch.Tensor, /) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Forward method for linear_fc2 module."""
+        ...
+
+    def backward_dw(self) -> None:
+        """Backward method for linear_fc2 module."""
+        ...
+
+
+class LinearFc2Builder(Protocol):
+    """Protocol describing how to build a linear_fc2 layer in MLP."""
+
+    def __call__(
+        self,
+        input_size: int,
+        output_size: int,
+        /,
+        *,
+        config: TransformerConfig,
+        init_method: Callable[[torch.Tensor], None],
+        bias: bool,
+        input_is_parallel: bool,
+        skip_bias_add: bool,
+        is_expert: bool,
+        tp_comm_buffer_name: str | None,
+        tp_group: torch.distributed.ProcessGroup | None,
+    ) -> LinearFc2Interface:
+        """Builds a linear_fc2 layer for MLP."""
+        ...
+
+
 @dataclass
 class MLPSubmodules:
     """
@@ -51,9 +137,14 @@ class MLPSubmodules:
     including  linear fc1, activation function, linear fc2.
     """
 
-    linear_fc1: Union[ModuleSpec, type] = None
-    activation_func: Union[ModuleSpec, type] = None
-    linear_fc2: Union[ModuleSpec, type] = None
+    linear_fc1: LinearFc1Builder
+
+    linear_fc2: LinearFc2Builder
+
+    activation_func: TEActivationFunctionBuilder | None = None
+    """
+    Builder for an activation function module; only used if config.use_te_activation_func is True.
+    """
 
 
 class MLP(MegatronModule):
@@ -98,7 +189,7 @@ def __init__(
                 DeprecationWarning,
                 stacklevel=2,
             )
-            ffn_hidden_size = self.config.ffn_hidden_size
+            ffn_hidden_size = not_none(self.config.ffn_hidden_size)
 
         # If this is a gated linear unit we double the output width
         # see https://arxiv.org/pdf/2002.05202.pdf
@@ -118,12 +209,11 @@ def __init__(
         # shared_experts.
         use_latent_size = (self.config.moe_latent_size is not None) and is_expert
 
-        self.linear_fc1 = build_module(
-            submodules.linear_fc1,
-            self.input_size if not use_latent_size else self.config.moe_latent_size,
+        self.linear_fc1 = submodules.linear_fc1(
+            self.input_size if not use_latent_size else not_none(self.config.moe_latent_size),
             ffn_hidden_size,
             config=self.config,
-            init_method=self.config.init_method,
+            init_method=not_none(self.config.init_method),
             gather_output=False,
             bias=self.config.add_bias_linear,
             skip_bias_add=True,
@@ -134,16 +224,17 @@ def __init__(
         )
 
         if self.config.use_te_activation_func and not (submodules.activation_func is None):
-            self.activation_func = build_module(submodules.activation_func, config=self.config)
+            self.activation_func = apply_module(submodules.activation_func(config=self.config))
         else:
             self.activation_func = self.config.activation_func
 
-        self.linear_fc2 = build_module(
-            submodules.linear_fc2,
-            self.config.ffn_hidden_size,
-            self.config.hidden_size if not use_latent_size else self.config.moe_latent_size,
+        self.linear_fc2 = submodules.linear_fc2(
+            not_none(self.config.ffn_hidden_size),
+            not_none(
+                self.config.hidden_size if not use_latent_size else self.config.moe_latent_size
+            ),
             config=self.config,
-            init_method=self.config.output_layer_init_method,
+            init_method=not_none(self.config.output_layer_init_method),
             bias=self.config.add_bias_linear,
             input_is_parallel=True,
             skip_bias_add=True,
@@ -152,11 +243,13 @@ def __init__(
             tp_group=tp_group,
         )
 
-    def forward(self, hidden_states, per_token_scale=None, **kwargs):
+    def forward(
+        self, hidden_states: torch.Tensor, per_token_scale: torch.Tensor | None = None, **kwargs
+    ):
         """Perform the forward pass through the MLP block."""
         # [s, b, 4 * h/p]
         nvtx_range_push(suffix="linear_fc1")
-        intermediate_parallel, bias_parallel = self.linear_fc1(hidden_states)
+        intermediate_parallel, bias_parallel = apply_module(self.linear_fc1)(hidden_states)
         nvtx_range_pop(suffix="linear_fc1")
 
         nvtx_range_push(suffix="activation")
@@ -238,7 +331,9 @@ def glu(x):
         # [s, b, h]
         nvtx_range_push(suffix="linear_fc2")
 
-        output, output_bias = self.linear_fc2(intermediate_parallel)
+        output, output_bias = apply_module(self.linear_fc2)(
+            cast(torch.Tensor, intermediate_parallel)
+        )
         nvtx_range_pop(suffix="linear_fc2")
 
         if per_token_scale is not None and output_bias is not None:
diff --git a/megatron/core/transformer/module.py b/megatron/core/transformer/module.py
index c30c107e791..6539ee36105 100644
--- a/megatron/core/transformer/module.py
+++ b/megatron/core/transformer/module.py
@@ -10,6 +10,7 @@
 
 from megatron.core import parallel_state
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
+from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.utils import (
     ensure_metadata_has_dp_cp_group,
@@ -168,7 +169,10 @@ def __init__(self, config: TransformerConfig, vp_stage: Optional[int] = None):
         assert isinstance(config, TransformerConfig), "config must be a TransformerConfig"
 
         # Enable cuda graphs.
-        if config.cuda_graph_impl == "local":
+        if (
+            config.cuda_graph_impl == "local"
+            and CudaGraphScope.full_iteration not in config.cuda_graph_scope
+        ):
             if hasattr(self, "create_mcore_cudagraph_manager"):
                 self.create_mcore_cudagraph_manager(config)
             else:
diff --git a/megatron/core/transformer/moe/experts.py b/megatron/core/transformer/moe/experts.py
index d8e75342226..8a271ab4fb9 100644
--- a/megatron/core/transformer/moe/experts.py
+++ b/megatron/core/transformer/moe/experts.py
@@ -1,11 +1,14 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from __future__ import annotations
 
 import copy
 import logging
+from collections.abc import Callable
 from copy import deepcopy
+from dataclasses import dataclass
 from functools import partial
 from math import ceil
-from typing import Optional, Tuple
+from typing import Optional, Protocol, Tuple
 
 import torch
 import torch.nn.functional as F
@@ -34,20 +37,25 @@
     set_tensor_model_parallel_attributes,
 )
 from megatron.core.tensor_parallel.utils import divide
-from megatron.core.transformer.mlp import MLP, MLPSubmodules, apply_swiglu_sharded_factory
+from megatron.core.transformer.mlp import (
+    MLP,
+    MLPSubmodules,
+    TEActivationFunctionBuilder,
+    apply_swiglu_sharded_factory,
+)
 from megatron.core.transformer.module import MegatronModule
 from megatron.core.transformer.moe import grouped_gemm_util as gg
 from megatron.core.transformer.moe.moe_utils import (
     ProcessGroupCollection,
     get_align_size_for_quantization,
 )
-from megatron.core.transformer.spec_utils import build_module
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.utils import (
     ensure_metadata_has_dp_cp_group,
     make_sharded_object_for_checkpoint,
     sharded_state_dict_default,
 )
+from megatron.core.typed_torch import apply_module, not_none
 
 try:
     import transformer_engine as te  # pylint: disable=unused-import
@@ -526,6 +534,95 @@ def backward_dw(self):
         pass
 
 
+class GroupedLinearFc1Interface(Protocol):
+    """Interface for linear_fc1 module in TEGroupedMLP."""
+
+    def forward(
+        self, permuted_local_hidden_states: torch.Tensor, tokens_per_expert: list[int], /
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Forward method for linear_fc1 module."""
+        ...
+
+    def backward_dw(self) -> None:
+        """Backward method for linear_fc1 module."""
+        ...
+
+
+class GroupedLinearFc1Builder(Protocol):
+    """Protocol describing how to build a linear_fc1 layer in TEGroupedMLP."""
+
+    def __call__(
+        self,
+        num_local_experts: int,
+        input_size: int,
+        output_size: int,
+        /,
+        *,
+        config: TransformerConfig,
+        init_method: Callable[[torch.Tensor], None],
+        bias: bool,
+        skip_bias_add: bool,
+        is_expert: bool,
+        tp_comm_buffer_name: str | None,
+        pg_collection: ProcessGroupCollection | None,
+    ) -> GroupedLinearFc1Interface:
+        """Builds a linear_fc1 layer for TEGroupedMLP."""
+        ...
+
+
+class GroupedLinearFc2Interface(Protocol):
+    """Protocol for linear_fc2 module in TEGroupedMLP."""
+
+    def forward(
+        self, intermediate_parallel: torch.Tensor, tokens_per_expert: list[int], /
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Forward method for linear_fc2 module."""
+        ...
+
+    def backward_dw(self) -> None:
+        """Backward method for linear_fc2 module."""
+        ...
+
+
+class GroupedLinearFc2Builder(Protocol):
+    """Protocol describing how to build a linear_fc2 layer in TEGroupedMLP."""
+
+    def __call__(
+        self,
+        num_local_experts: int,
+        input_size: int,
+        output_size: int,
+        /,
+        *,
+        config: TransformerConfig,
+        init_method: Callable[[torch.Tensor], None],
+        bias: bool,
+        skip_bias_add: bool,
+        is_expert: bool,
+        tp_comm_buffer_name: str | None,
+        pg_collection: ProcessGroupCollection | None,
+    ) -> GroupedLinearFc2Interface:
+        """Builds a linear_fc2 layer for TEGroupedMLP."""
+        ...
+
+
+@dataclass
+class TEGroupedMLPSubmodules:
+    """
+    The dataclass for ModuleSpecs of TEGroupedMLP submodules
+    including  linear fc1, activation function, linear fc2.
+    """
+
+    linear_fc1: GroupedLinearFc1Builder
+
+    linear_fc2: GroupedLinearFc2Builder
+
+    activation_func: TEActivationFunctionBuilder | None = None
+    """
+    Builder for an activation function module; only used if config.use_te_activation_func is True.
+    """
+
+
 class TEGroupedMLP(MegatronModule):
     """An efficient implementation of the Experts layer using TE's GroupedLinear.
 
@@ -535,9 +632,9 @@ class TEGroupedMLP(MegatronModule):
     # TODO(M4): breaking api, switched from pass in tp_group to pass in pg_collection.
     def __init__(
         self,
-        num_local_experts,
+        num_local_experts: int,
         config: TransformerConfig,
-        submodules: MLPSubmodules,
+        submodules: TEGroupedMLPSubmodules,
         pg_collection: Optional[ProcessGroupCollection] = None,
     ):
         super().__init__(config=config)
@@ -551,17 +648,16 @@ def __init__(
         self.tp_group = pg_collection.expt_tp
 
         # Double the output width with gated linear unit, see https://arxiv.org/pdf/2002.05202.pdf
-        ffn_hidden_size = self.config.moe_ffn_hidden_size
+        ffn_hidden_size = not_none(self.config.moe_ffn_hidden_size)
         if self.config.gated_linear_unit:
             ffn_hidden_size *= 2
 
-        self.linear_fc1 = build_module(
-            submodules.linear_fc1,
+        self.linear_fc1 = submodules.linear_fc1(
             self.num_local_experts,
             self.input_size if self.config.moe_latent_size is None else self.config.moe_latent_size,
             ffn_hidden_size,
             config=self.config,
-            init_method=self.config.init_method,
+            init_method=not_none(self.config.init_method),
             bias=self.config.add_bias_linear,
             skip_bias_add=False,
             is_expert=True,
@@ -570,21 +666,20 @@ def __init__(
         )
 
         if self.config.use_te_activation_func and not (submodules.activation_func is None):
-            self.activation_func = build_module(submodules.activation_func, config=self.config)
+            self.activation_func = apply_module(submodules.activation_func(config=self.config))
         else:
             self.activation_func = self.config.activation_func
 
-        self.linear_fc2 = build_module(
-            submodules.linear_fc2,
+        self.linear_fc2 = submodules.linear_fc2(
             self.num_local_experts,
-            self.config.moe_ffn_hidden_size,
+            not_none(self.config.moe_ffn_hidden_size),
             (
                 self.config.hidden_size
                 if self.config.moe_latent_size is None
                 else self.config.moe_latent_size
             ),
             config=self.config,
-            init_method=self.config.output_layer_init_method,
+            init_method=not_none(self.config.output_layer_init_method),
             bias=self.config.add_bias_linear,
             skip_bias_add=True,
             is_expert=True,
@@ -659,7 +754,7 @@ def forward(
         Return:
             output (torch.Tensor): The output of the local experts.
         """
-        tokens_per_expert = tokens_per_expert.tolist()
+        tokens_per_expert: list[int] = tokens_per_expert.tolist()
         if self.config.fp8 or self.config.fp4:
             actual_tokens_per_expert = tokens_per_expert
             permuted_local_hidden_states, tokens_per_expert = self.quantization_padding(
@@ -684,7 +779,7 @@ def forward(
         with off_interface(
             self.offload_expert_fc1, permuted_local_hidden_states, "expert_fc1"
         ) as permuted_local_hidden_states:
-            fc1_output, bias_parallel = self.linear_fc1(
+            fc1_output, bias_parallel = apply_module(self.linear_fc1)(
                 permuted_local_hidden_states, tokens_per_expert
             )
         if self.offload_expert_fc1:
@@ -762,7 +857,7 @@ def glu(x):
             with off_interface(self.offload_moe_act, fc1_output, "moe_act") as fc1_output:
                 bias_act_output = bias_act_func(fc1_output, bias_parallel, permuted_probs)
 
-        output, output_bias = self.linear_fc2(bias_act_output, tokens_per_expert)
+        output, output_bias = apply_module(self.linear_fc2)(bias_act_output, tokens_per_expert)
         if self.activation_recompute:
             self.activation_checkpoint.discard_output_and_register_recompute(output)
 
diff --git a/megatron/core/transformer/moe/moe_layer.py b/megatron/core/transformer/moe/moe_layer.py
index 98d4d5fa505..982cb5cec8d 100644
--- a/megatron/core/transformer/moe/moe_layer.py
+++ b/megatron/core/transformer/moe/moe_layer.py
@@ -87,10 +87,12 @@ def __init__(
         config: TransformerConfig,
         layer_number: Optional[int] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        is_mtp_layer: bool = False,
     ):
         super(BaseMoELayer, self).__init__(config)
         self.config = config
         self.layer_number = layer_number
+        self.is_mtp_layer = is_mtp_layer
         self.ep_group = pg_collection.ep
         # use pg_collection.expt_tp_group as tensor parallel group in this module.
         self.attn_tp_group = pg_collection.tp
@@ -126,6 +128,7 @@ def set_layer_number(self, layer_number: int):
         self.router.set_layer_number(layer_number)
 
 
+
 class MoELayer(BaseMoELayer):
     """Mixture of Experts layer.
 
@@ -140,6 +143,7 @@ def __init__(
         submodules: Optional[MoESubmodules] = None,
         layer_number: Optional[int] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        is_mtp_layer: bool = False,
     ):
         self.submodules = submodules
         # TODO(Hepteract): delete the usage of the global parallel_state.
@@ -147,7 +151,10 @@ def __init__(
         if pg_collection is None:
             pg_collection = get_default_pg_collection()
         super(MoELayer, self).__init__(
-            config=config, layer_number=layer_number, pg_collection=pg_collection
+            config=config,
+            layer_number=layer_number,
+            pg_collection=pg_collection,
+            is_mtp_layer=is_mtp_layer,
         )
         # If using mcore cudagraphs, recompute is handled by transformer_layer.MoETransformerLayer
         self.moe_layer_recompute = (
@@ -163,7 +170,9 @@ def __init__(
         self.tp_group = pg_collection.tp
 
         # Initialize router.
-        self.router = submodules.router(config=self.config, pg_collection=pg_collection)
+        self.router = submodules.router(
+            config=self.config, pg_collection=pg_collection, is_mtp_layer=is_mtp_layer
+        )
         self.tp_group = pg_collection.tp
 
         # Initialize latent projections.
@@ -386,7 +395,7 @@ def forward(
             padding_mask = padding_mask.transpose(0, 1).bool()
 
         # MoE forward: route -> dispatch -> compute -> combine
-        def custom_forward(hidden_states, intermediate_tensors, padding_mask=None):
+        def custom_forward(hidden_states, intermediate_tensors=None, padding_mask=None):
             try:
                 if "route" in self.fwd_execution_map:
                     shared_expert_output = self.shared_experts_compute(hidden_states)
@@ -437,11 +446,12 @@ def custom_forward(hidden_states, intermediate_tensors, padding_mask=None):
                     tensor_parallel.random.get_cuda_rng_tracker,
                     parallel_state.get_tensor_model_parallel_group(),
                     hidden_states,
+                    intermediate_tensors,
                     padding_mask,
                 )
             else:
                 outputs = tensor_parallel.checkpoint(
-                    custom_forward, False, hidden_states, padding_mask
+                    custom_forward, False, hidden_states, intermediate_tensors, padding_mask
                 )
         else:
             outputs = custom_forward(hidden_states, intermediate_tensors, padding_mask)
@@ -450,10 +460,25 @@ def custom_forward(hidden_states, intermediate_tensors, padding_mask=None):
 
     def backward_dw(self, routed_experts: bool = True, shared_experts: bool = False):
         """Compute weight gradients for experts and shared experts."""
+        # TODO(Wohox): replace the "routed_experts" and "shared_experts" arguments with better
+        # naming to better explain that they are actually from different fine-grained callables,
+        # or use scanning to decide which backward_dw should be called.
         if routed_experts:
             self.experts.backward_dw()
-        if shared_experts and self.use_shared_expert and not self.shared_expert_overlap:
-            self.shared_experts.backward_dw()
+            if self.config.moe_latent_size:
+                # TODO(Wohox): fc2_latent_proj forward and backward are executed in comm stream,
+                # so we execute its backward_dw in the comm stream too. But this may harm the
+                # EP overlap performance. Better to check if there is a better way to handle this.
+                from megatron.core.pipeline_parallel.utils import get_comm_stream
+
+                comm_stream = get_comm_stream()
+                with torch.cuda.stream(comm_stream):
+                    self.fc2_latent_proj.backward_dw()
+        if shared_experts:
+            if self.use_shared_expert and not self.shared_expert_overlap:
+                self.shared_experts.backward_dw()
+            if self.config.moe_latent_size:
+                self.fc1_latent_proj.backward_dw()
 
     def set_for_recompute_pre_mlp_layernorm(self):
         """Set the MoE layer for recompute pre_mlp_layernorm. Only needed for fp8/fp4."""
diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py
index 75825cd373b..70d00567b42 100644
--- a/megatron/core/transformer/moe/moe_utils.py
+++ b/megatron/core/transformer/moe/moe_utils.py
@@ -18,9 +18,8 @@
 from megatron.core.tensor_parallel.mappings import reduce_from_tensor_model_parallel_region
 from megatron.core.transformer.cuda_graphs import is_graph_capturing
 from megatron.core.transformer.enums import CudaGraphScope
-from megatron.core.transformer.moe.router_replay import RouterReplay
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.utils import internal_api
+from megatron.core.utils import internal_api, is_te_min_version
 
 try:
     import transformer_engine as te  # pylint: disable=unused-import
@@ -29,6 +28,7 @@
         fused_compute_score_for_moe_aux_loss,
         fused_moe_aux_loss,
         fused_permute,
+        fused_permute_and_pad_with_probs,
         fused_permute_with_probs,
         fused_sort_chunks_by_index,
         fused_sort_chunks_by_index_with_probs,
@@ -298,7 +298,15 @@ def permute(
     num_out_tokens: Optional[int] = None,
     fused: bool = False,
     drop_and_pad: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor]:
+    tokens_per_expert: Optional[torch.Tensor] = None,
+    align_size: int = -1,
+) -> Tuple[
+    torch.Tensor,
+    Optional[torch.Tensor],
+    torch.Tensor,
+    Optional[torch.Tensor],
+    Optional[torch.Tensor],
+]:
     """Permute the tokens and probs based on the mask.
     Tokens with the same designated expert will be grouped together.
     The shape of mask is [tokens, num_experts], it indicates which experts were selected
@@ -307,6 +315,9 @@ def permute(
     When drop_and_pad=True, in routing_map, the number of non-zeros in each column equals to
     expert capacity. This function exploits this feature to use ops that support cuda graph.
 
+    If the fused permute and pad kernel is available, it will pad the tokens to the align_size
+    and return the padded permuted tokens, pad_offsets and padded tokens per expert.
+
     Args:
         tokens (torch.Tensor): The input token tensor, [num_tokens, hidden].
         routing_map (torch.Tensor): The sparse token to expert mapping, [num_tokens, num_experts].
@@ -318,10 +329,20 @@ def permute(
                                        and pads the number of tokens to the expert capacity.
                                        If set to true, routing_map has a fixed number of non-zeros
                                        in each column.
+        tokens_per_expert (torch.Tensor, optional): Tensor of shape `[num_experts]` containing
+                                                    actual token counts per expert.
+        align_size (int, optional): The alignment size for the input tensor for fp8 or fp4.
 
     Returns:
-        Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor]:
-            The permuted tokens, permuted probs, and sorted indices.
+        Tuple[
+            torch.Tensor,
+            Optional[torch.Tensor],
+            torch.Tensor,
+            Optional[torch.Tensor],
+            Optional[torch.Tensor],
+        ]:
+            The permuted tokens, (optional) permuted probs, sorted indices,
+            (optional) pad_offsets, (optional) padded_tokens_per_expert.
     """
     if fused and probs is None:
         if not HAVE_TE or fused_permute is None:
@@ -329,14 +350,26 @@ def permute(
         permuted_input, sorted_indices = fused_permute(
             tokens, routing_map, num_out_tokens=num_out_tokens
         )
-        return permuted_input, None, sorted_indices
+        return permuted_input, None, sorted_indices, None, tokens_per_expert
 
     if fused and probs is not None:
-        if not HAVE_TE or fused_permute_with_probs is None:
+        if not HAVE_TE or (
+            fused_permute_and_pad_with_probs is None and fused_permute_with_probs is None
+        ):
             raise ValueError(
-                "fused_permute_with_probs is not available. Please install TE >= 2.1.0."
+                "Transformer Engine (TE) fused kernel is not available. "
+                "fused_permute_with_probs typically requires TE >= 2.1.0, and "
+                "fused_permute_and_pad_with_probs` typically requires TE >= 2.12.0. "
+            )
+        if fused_permute_and_pad_with_probs is not None and tokens_per_expert is not None:
+            return fused_permute_and_pad_with_probs(
+                tokens, probs, routing_map, tokens_per_expert, align_size
             )
-        return fused_permute_with_probs(tokens, probs, routing_map, num_out_tokens=num_out_tokens)
+        else:
+            output, permuted_probs, row_id_map = fused_permute_with_probs(
+                tokens, probs, routing_map, num_out_tokens=num_out_tokens
+            )
+            return output, permuted_probs, row_id_map, None, tokens_per_expert
 
     num_tokens, hidden = tokens.shape
     num_experts = routing_map.shape[1]
@@ -379,7 +412,7 @@ def permute(
     # use the mapping to permute the tokens
     permuted_input = tokens.index_select(0, sorted_indices)
 
-    return permuted_input, permuted_probs, sorted_indices
+    return permuted_input, permuted_probs, sorted_indices, None, tokens_per_expert
 
 
 def unpermute(
@@ -390,6 +423,7 @@ def unpermute(
     routing_map: Optional[torch.Tensor] = None,
     fused: bool = False,
     drop_and_pad: bool = False,
+    pad_offsets: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     """
     Restore the original order of tokens after permutation. If probs are provided, it
@@ -411,6 +445,10 @@ def unpermute(
         fused (bool, optional): Whether use the fused unpermute function.
         drop_and_pad (bool, optional): Whether or not the token dispatcher uses token-drop
                                        and pads the number of tokens to the expert capacity.
+        pad_offsets (torch.Tensor, optional):
+            Tensor of per-expert cumulative padding offsets used to remove padding added
+            during permutation. This is the fourth output of `moe_permute_and_pad_with_probs`
+            and is required when unpermuting padded outputs. Defaults to None.
 
     Returns:
         torch.Tensor: The tokens restored to their original order.
@@ -418,8 +456,15 @@ def unpermute(
     if fused:
         if not HAVE_TE or fused_unpermute is None:
             raise ValueError("fused_unpermute is not available. Please install TE >= 2.1.0.")
+        extra_kwargs = {}
+        if is_te_min_version("2.12.0"):
+            extra_kwargs["pad_offsets"] = pad_offsets
         return fused_unpermute(
-            permuted_tokens, sorted_indices, merging_probs=probs, restore_shape=restore_shape
+            permuted_tokens,
+            sorted_indices,
+            merging_probs=probs,
+            restore_shape=restore_shape,
+            **extra_kwargs,
         )
 
     _, hidden = restore_shape
@@ -620,7 +665,7 @@ def topk_routing_with_score_function(
     score_function: str = "softmax",
     expert_bias: Optional[torch.Tensor] = None,
     fused: bool = False,
-    router_replay: Optional['RouterReplay'] = None,
+    is_mtp: bool = False,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """Compute the routing probabilities and map for top-k selection with score function.
 
@@ -638,11 +683,8 @@ def topk_routing_with_score_function(
         expert_bias (torch.Tensor, optional): The bias added to logits for expert routing.
                                               Defaults to None.
         fused (bool, optional): Whether to use the fused version. Defaults to False.
-        router_replay (Optional['RouterReplay']): For debugging and development, allows for
-                                             deterministic routing by replaying a previously
-                                             recorded routing sequence.
-
-                                              Defaults to None.
+        is_mtp (bool, optional): Whether this is an MTP layer. MTP layers bypass routing replay.
+                                 Defaults to False.
 
     Returns:
         Tuple[torch.Tensor, torch.Tensor]:
@@ -701,15 +743,13 @@ def _compute_topk(
         else:
             return torch.topk(scores, k=topk, dim=1)
 
-    def compute_topk(scores, topk, num_groups=None, group_topk=None):
-        # Default behavior if no replay is active
+    from miles.utils.replay_base import routing_replay_manager
 
-        if router_replay is None:
-            return _compute_topk(scores, topk, num_groups=num_groups, group_topk=group_topk)
-        else:
-            return router_replay.get_replay_topk(
-                scores, topk, num_groups, group_topk, _compute_topk
-            )
+    # MTP layers cannot use rollout routing replay
+    if not is_mtp:
+        compute_topk = routing_replay_manager.get_topk_fn(_compute_topk, return_probs=True)
+    else:
+        compute_topk = _compute_topk
 
     if score_function == "softmax":
         if use_pre_softmax:
@@ -999,13 +1039,20 @@ def track_moe_metrics(
     """
     # Aux loss logging
     tracker = get_moe_layer_wise_logging_tracker()
-    # Initialize the tracker if force_initialize is True
+    # Initialize the tracker if force_initialize is True.
+    # The values tensor size must match what the router creates in save_to_aux_losses_tracker,
+    # which uses (num_layers + mtp_num_layers). This is important for PP ranks that have no
+    # MoE layers (so the tracker is empty and force_initialize creates the entry); their tensor
+    # size must match ranks that do have MoE layers, otherwise all_reduce across PP will hang.
+    tracker_num_layers = num_layers
+    if mtp_num_layers is not None:
+        tracker_num_layers += mtp_num_layers
     if force_initialize:
         if track_names is not None:
             for key in track_names:
                 if key not in tracker:
                     tracker[key] = {}
-                    tracker[key]["values"] = torch.zeros(num_layers, device="cuda")
+                    tracker[key]["values"] = torch.zeros(tracker_num_layers, device="cuda")
                     tracker[key]["reduce_group"] = None
                     tracker[key]["avg_group"] = None
                     tracker[key]["reduce_group_has_dp"] = False
diff --git a/megatron/core/transformer/moe/router.py b/megatron/core/transformer/moe/router.py
index a2f3e90bd0f..4d570240922 100644
--- a/megatron/core/transformer/moe/router.py
+++ b/megatron/core/transformer/moe/router.py
@@ -22,7 +22,6 @@
     topk_routing_with_score_function,
     z_loss_func,
 )
-from megatron.core.transformer.moe.router_replay import RouterReplay
 from megatron.core.transformer.transformer_config import TransformerConfig
 
 
@@ -30,7 +29,10 @@ class Router(ABC, MegatronModule):
     """Base Router class"""
 
     def __init__(
-        self, config: TransformerConfig, pg_collection: Optional[ProcessGroupCollection] = None
+        self,
+        config: TransformerConfig,
+        pg_collection: Optional[ProcessGroupCollection] = None,
+        is_mtp_layer: bool = False,
     ) -> None:
         """
         Initialize the Router module.
@@ -38,12 +40,14 @@ def __init__(
         Args:
             config (TransformerConfig): Configuration object for the Transformer model.
             pg_collection (ProcessGroupCollection, optional): Process groups for MoE operations.
+            is_mtp_layer (bool): Flag indicating if this router is part of an MTP layer.
         """
         super().__init__(config)
         self.config = config
         self.num_experts = self.config.num_moe_experts
         self.moe_aux_loss_func = None
         self.layer_number = None
+        self.is_mtp_layer = is_mtp_layer
         self.tp_group = pg_collection.tp
         self.cp_group = pg_collection.cp
         self.tp_cp_group = pg_collection.tp_cp
@@ -146,15 +150,19 @@ class TopKRouter(Router):
     """
 
     def __init__(
-        self, config: TransformerConfig, pg_collection: Optional[ProcessGroupCollection] = None
+        self,
+        config: TransformerConfig,
+        pg_collection: Optional[ProcessGroupCollection] = None,
+        is_mtp_layer: bool = False,
     ) -> None:
         """Initialize the zero token dropping router.
 
         Args:
             config (TransformerConfig): The configuration for the transformer model.
             pg_collection (ProcessGroupCollection, optional): Process groups for MoE operations.
+            is_mtp_layer (bool): Flag indicating if this router is part of an MTP layer.
         """
-        super().__init__(config=config, pg_collection=pg_collection)
+        super().__init__(config=config, pg_collection=pg_collection, is_mtp_layer=is_mtp_layer)
         self.topk = self.config.moe_router_topk
         self.routing_type = self.config.moe_router_load_balancing_type
         self.score_function = self.config.moe_router_score_function
@@ -203,9 +211,8 @@ def __init__(
             self.global_tokens_per_expert = None
             self.ga_steps = None
 
-        self.router_replay = None
-        if self.config.moe_enable_routing_replay:
-            self.router_replay = RouterReplay()
+        from miles.utils.replay_base import routing_replay_manager
+        routing_replay_manager.register_to_module(self, "routing_replay")
 
     def _maintain_float32_expert_bias(self):
         """
@@ -440,6 +447,16 @@ def attach_and_log_load_balancing_loss(
                 padding tokens. Can be a Python int or a torch.Tensor (typically 0-d tensor).
                 If None, uses activation.shape[0]. Defaults to None.
         """
+        # When using repeated MTP layers, the loss is counted "mtp_num_layers" times.
+        # To avoid accumulating the load balancing loss multiple times, we scale it by
+        # 1/mtp_num_layers so the total loss is correct.
+        if (
+            self.is_mtp_layer
+            and self.config.mtp_use_repeated_layer
+            and self.config.mtp_num_layers is not None
+        ):
+            aux_loss = aux_loss / self.config.mtp_num_layers
+
         # TODO (zijiey): fix the per_layer_logging for MTP, currently it will incorrectly
         # add the aux loss logging value to other layer's since it is difficult to get the
         # correct layer_number for MTP. It does not affect the correctness of the calculation
@@ -447,10 +464,16 @@ def attach_and_log_load_balancing_loss(
         num_layers = self.config.num_layers
         if self.config.mtp_num_layers is not None:
             num_layers += self.config.mtp_num_layers
+
+        if self.is_mtp_layer:
+            layer_number = self.layer_number + self.config.num_layers
+        else:
+            layer_number = self.layer_number
+
         save_to_aux_losses_tracker(
             aux_loss_name,
             aux_loss / aux_loss_coeff,
-            self.layer_number,
+            layer_number,
             num_layers,
             reduce_group=reduce_group,
             reduce_group_has_dp=reduce_group_has_dp,
@@ -501,11 +524,27 @@ def apply_z_loss(self, logits, padding_mask: Optional[torch.Tensor] = None):
             else:
                 logits = MoEAuxLossAutoScaler.apply(logits, z_loss)
 
+            # When using repeated MTP layers, the same MTP layer is called mtp_num_layers times.
+            # To avoid accumulating the z_loss multiple times, we scale it by 1/mtp_num_layers
+            # so the total loss is correct.
+            if (
+                self.is_mtp_layer
+                and self.config.mtp_use_repeated_layer
+                and self.config.mtp_num_layers is not None
+            ):
+                z_loss = z_loss / self.config.mtp_num_layers
+
             num_layers = self.config.num_layers
             if self.config.mtp_num_layers is not None:
                 num_layers += self.config.mtp_num_layers
+
+            if self.is_mtp_layer:
+                layer_number = self.layer_number + self.config.num_layers
+            else:
+                layer_number = self.layer_number
+
             save_to_aux_losses_tracker(
-                "z_loss", z_loss / moe_z_loss_coeff, self.layer_number, num_layers
+                "z_loss", z_loss / moe_z_loss_coeff, layer_number, num_layers
             )
         return logits
 
@@ -582,7 +621,7 @@ def routing(self, logits: torch.Tensor, padding_mask: Optional[torch.Tensor] = N
                 score_function=self.score_function,
                 expert_bias=self.expert_bias,
                 fused=self.config.moe_router_fusion,
-                router_replay=self.router_replay,
+                is_mtp=self.is_mtp_layer,
             )
 
         # Apply token dropping to probs and routing_map.
diff --git a/megatron/core/transformer/moe/router_replay.py b/megatron/core/transformer/moe/router_replay.py
index b6b8e26a0a6..5430f75568f 100644
--- a/megatron/core/transformer/moe/router_replay.py
+++ b/megatron/core/transformer/moe/router_replay.py
@@ -74,6 +74,29 @@ def clear_global_router_replay_instances():
         """Clear the global list of router replay instances to prevent memory leaks."""
         RouterReplay.global_router_replay_instances.clear()
 
+    @staticmethod
+    def set_global_static_buffers(static_buffer: torch.Tensor):
+        """Sets static buffers for all router instances from a combined buffer.
+
+        Args:
+            static_buffer: Tensor of shape [max_tokens, num_layers, topk].
+                          Each layer's RouterReplay gets a slice [:, layer_idx, :].
+        """
+        num_layers = len(RouterReplay.global_router_replay_instances)
+        assert static_buffer.shape[1] == num_layers, (
+            f"Buffer has {static_buffer.shape[1]} layers but there are "
+            f"{num_layers} RouterReplay instances."
+        )
+        for layer_idx, router_instance in enumerate(RouterReplay.global_router_replay_instances):
+            # Each layer gets a view of shape [max_tokens, topk]
+            router_instance.set_static_buffer(static_buffer[:, layer_idx, :])
+
+    @staticmethod
+    def clear_global_static_buffers():
+        """Clears static buffers from all router instances."""
+        for router in RouterReplay.global_router_replay_instances:
+            router.clear_static_buffer()
+
     def __init__(self):
         """Initializes a RouterReplay instance for a specific layer."""
         self.target_topk_idx: Optional[torch.Tensor] = None  # Target topk indices for replay
@@ -84,6 +107,7 @@ def __init__(self):
         self.replay_backward_list: List[torch.Tensor] = (
             []
         )  # List of tensors for backward pass replay
+        self.static_buffer: Optional[torch.Tensor] = None  # Static buffer for CUDA graph
         RouterReplay.global_router_replay_instances.append(self)
 
     def set_target_indices(self, topk_indices: torch.Tensor):
@@ -95,10 +119,6 @@ def get_recorded_indices(self) -> Optional[torch.Tensor]:
         """Returns the recorded topk indices."""
         return self.recorded_topk_idx
 
-    def record_indices(self, topk_indices: torch.Tensor):
-        """Records the topk indices."""
-        self.recorded_topk_idx = topk_indices
-
     def clear_indices(self):
         """Clears the recorded and target topk indices."""
         self.recorded_topk_idx = None
@@ -159,3 +179,29 @@ def get_replay_topk(
             return probs, top_indices
         else:
             return default_compute_topk(scores, topk, num_groups, group_topk)
+
+    def set_static_buffer(self, buffer: torch.Tensor):
+        """Sets a static buffer for CUDA graph compatible recording.
+
+        Args:
+            buffer: Tensor of shape [max_tokens, topk] to copy routing indices into.
+        """
+        self.static_buffer = buffer
+
+    def clear_static_buffer(self):
+        """Clears the static buffer."""
+        self.static_buffer = None
+
+    def record_indices(self, topk_indices: torch.Tensor):
+        """Records the topk indices.
+
+        If a static buffer is set (for CUDA graph compatibility), copies into it.
+        Otherwise, just stores the tensor reference.
+        """
+        if self.static_buffer is not None:
+            # Copy into static buffer for CUDA graph compatibility.
+            num_tokens = topk_indices.shape[0]
+            self.static_buffer[:num_tokens].copy_(topk_indices)
+            self.recorded_topk_idx = self.static_buffer[:num_tokens]
+        else:
+            self.recorded_topk_idx = topk_indices
diff --git a/megatron/core/transformer/moe/shared_experts.py b/megatron/core/transformer/moe/shared_experts.py
index 35066b1a8b0..2ad2f125163 100644
--- a/megatron/core/transformer/moe/shared_experts.py
+++ b/megatron/core/transformer/moe/shared_experts.py
@@ -20,6 +20,7 @@
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
 from megatron.core.transformer.moe.moe_utils import ProcessGroupCollection
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.typed_torch import apply_module
 from megatron.core.utils import (
     is_te_min_version,
     is_torch_min_version,
@@ -184,7 +185,9 @@ def linear_fc1_forward_and_act(self, overlapped_comm_output=None):
             set_tensor_grad_fn_sequence_sr(overlapped_comm_output, torch.iinfo(torch.int).max)
         with torch.cuda.stream(self.stream):
             # [s, b, 4 * h/p]
-            intermediate_parallel, bias_parallel = self.linear_fc1(self.cached_fc1_input)
+            intermediate_parallel, bias_parallel = apply_module(self.linear_fc1)(
+                self.cached_fc1_input
+            )
             self.cached_fc1_input = None
 
             if self.config.use_te_activation_func:
@@ -235,7 +238,7 @@ def linear_fc2_forward(self, overlapped_comm_output=None):
             set_tensor_grad_fn_sequence_sr(overlapped_comm_output, torch.iinfo(torch.int).max)
         with torch.cuda.stream(self.stream):
             # [s, b, h]
-            self.cached_fc2_output, _ = self.linear_fc2(self.cached_fc2_input)
+            self.cached_fc2_output, _ = apply_module(self.linear_fc2)(self.cached_fc2_input)
             self.cached_fc2_input = None
 
     def post_forward_comm(self):
diff --git a/megatron/core/transformer/moe/token_dispatcher.py b/megatron/core/transformer/moe/token_dispatcher.py
index 327dbc8a382..e82882a66f9 100644
--- a/megatron/core/transformer/moe/token_dispatcher.py
+++ b/megatron/core/transformer/moe/token_dispatcher.py
@@ -296,11 +296,13 @@ def dispatch_postprocess(self, hidden_states, probs):
 
         tokens_per_expert = self.local_map.sum(dim=0).long().cpu()
 
-        (permuted_local_hidden_states, _, self.reversed_local_input_permutation_mapping) = permute(
-            hidden_states,
-            self.local_map,
-            num_out_tokens=tokens_per_expert.sum(),
-            fused=self.config.moe_permute_fusion,
+        permuted_local_hidden_states, _, self.reversed_local_input_permutation_mapping, _, _ = (
+            permute(
+                hidden_states,
+                self.local_map,
+                num_out_tokens=tokens_per_expert.sum().item(),
+                fused=self.config.moe_permute_fusion,
+            )
         )
 
         self.local_probs = self.local_probs.T.contiguous().masked_select(
@@ -636,6 +638,8 @@ def dispatch_preprocess(
             permutated_local_input_tokens,
             permuted_probs,
             self.reversed_local_input_permutation_mapping,
+            _,
+            _,
         ) = permute(
             hidden_states,
             self.routing_map,
@@ -1297,12 +1301,20 @@ def get_permuted_hidden_states_by_experts(self, hidden_states: torch.Tensor) ->
 
         self.hidden_shape_before_permute = hidden_states.shape
         assert self.dispatched_probs.dtype == torch.float32, "DeepEP only supports float32 probs"
-        hidden_states, permuted_probs, self.reversed_mapping_for_combine = permute(
+        (
+            hidden_states,
+            permuted_probs,
+            self.reversed_mapping_for_combine,
+            self.pad_offsets,
+            self.tokens_per_expert,
+        ) = permute(
             hidden_states,
             self.dispatched_routing_map,
             probs=self.dispatched_probs,
             num_out_tokens=self.tokens_per_expert.sum().item(),
             fused=self.permute_fusion,
+            tokens_per_expert=self.tokens_per_expert,
+            align_size=get_align_size_for_quantization(self.config),
         )
         if self.router_dtype == "fp64":
             permuted_probs = permuted_probs.to(torch.float64)
@@ -1315,6 +1327,7 @@ def get_restored_hidden_states_by_experts(self, hidden_states: torch.Tensor) ->
             restore_shape=self.hidden_shape_before_permute,
             routing_map=self.dispatched_routing_map,
             fused=self.permute_fusion,
+            pad_offsets=self.pad_offsets,
         )
         return hidden_states
 
diff --git a/megatron/core/transformer/multi_latent_attention.py b/megatron/core/transformer/multi_latent_attention.py
index cd3db50a35b..a9cdc697cc8 100644
--- a/megatron/core/transformer/multi_latent_attention.py
+++ b/megatron/core/transformer/multi_latent_attention.py
@@ -16,6 +16,7 @@
 
 
 from megatron.core import tensor_parallel
+from megatron.core.extensions.transformer_engine import split_te_layernorm_column_parallel_linear
 from megatron.core.models.common.embeddings import (
     RotaryEmbedding,
     YarnRotaryEmbedding,
@@ -33,12 +34,11 @@
     scatter_to_sequence_parallel_region,
 )
 from megatron.core.transformer.attention import Attention
-from megatron.core.transformer.custom_layers.transformer_engine import (
-    split_te_layernorm_column_parallel_linear,
-)
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.transformer.transformer_config import MLATransformerConfig
+from megatron.core.typed_torch import apply_module
 from megatron.core.utils import deprecate_inference_params, get_pg_size, is_te_min_version
 
 try:
@@ -69,6 +69,10 @@
 class MLASelfAttentionSubmodules:
     """Submodules for the MLA self-attention layer."""
 
+    # TODO(nschank): Move layernorms back to the bottom once all other layers have defaults removed.
+    q_layernorm: LayerNormBuilder
+    kv_layernorm: LayerNormBuilder
+
     linear_q_proj: Union[ModuleSpec, type] = None
     linear_q_down_proj: Union[ModuleSpec, type] = None
     linear_q_up_proj: Union[ModuleSpec, type] = None
@@ -76,8 +80,6 @@ class MLASelfAttentionSubmodules:
     linear_kv_up_proj: Union[ModuleSpec, type] = None
     core_attention: Union[ModuleSpec, type] = None
     linear_proj: Union[ModuleSpec, type] = None
-    q_layernorm: Union[ModuleSpec, type] = None
-    kv_layernorm: Union[ModuleSpec, type] = None
 
 
 class MultiLatentAttention(Attention):
@@ -495,15 +497,13 @@ def __init__(
         )
 
         if self.config.q_lora_rank is not None:
-            self.q_layernorm = build_module(
-                submodules.q_layernorm,
+            self.q_layernorm = submodules.q_layernorm(
                 hidden_size=self.config.q_lora_rank,
                 config=self.config,
                 eps=self.config.layernorm_epsilon,
             )
 
-        self.kv_layernorm = build_module(
-            submodules.kv_layernorm,
+        self.kv_layernorm = submodules.kv_layernorm(
             hidden_size=self.config.kv_lora_rank,
             config=self.config,
             eps=self.config.layernorm_epsilon,
@@ -635,9 +635,9 @@ def get_query_key_value_tensors(
 
         if self.config.q_lora_rank is not None:
             # q_compressed: [num_tokens, q_lora_rank]
-            q_compressed = self.q_layernorm(q_compressed)
+            q_compressed = apply_module(self.q_layernorm)(q_compressed)
 
-        kv_compressed = self.kv_layernorm(kv_compressed)
+        kv_compressed = apply_module(self.kv_layernorm)(kv_compressed)
 
         # =========================================
         # QKV up projection and RoPE apply
diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py
index b0476155ad9..60ea1067ce0 100755
--- a/megatron/core/transformer/multi_token_prediction.py
+++ b/megatron/core/transformer/multi_token_prediction.py
@@ -1,19 +1,22 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from __future__ import annotations
 
 import warnings
 from contextlib import nullcontext
 from dataclasses import dataclass
-from typing import Callable, List, Optional, Union
+from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
 import torch
 from torch import Tensor
+import warnings
 
 from megatron.core import InferenceParams, parallel_state, tensor_parallel
 from megatron.core.dist_checkpointing.mapping import ShardedStateDict
-from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding
+from megatron.core.dist_checkpointing.utils import apply_prefix_mapping, replace_prefix_for_sharding
 from megatron.core.fp8_utils import get_fp8_context
 from megatron.core.models.backends import BackendSpecProvider, LocalSpecProvider
 from megatron.core.packed_seq_params import PackedSeqParams
+from megatron.core.pipeline_parallel.utils import is_vp_last_stage
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.tensor_parallel import (
     gather_from_tensor_model_parallel_region,
@@ -22,9 +25,10 @@
 from megatron.core.transformer.enums import AttnMaskType, LayerType
 from megatron.core.transformer.module import MegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.transformer.transformer_block import TransformerBlockSubmodules
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.transformer.transformer_layer import get_transformer_layer_offset
+from megatron.core.typed_torch import apply_module
 from megatron.core.utils import (
     get_pg_rank,
     is_torch_min_version,
@@ -32,6 +36,9 @@
     make_viewless_tensor,
 )
 
+if TYPE_CHECKING:
+    from megatron.core.ssm.mamba_block import MambaStackSubmodules
+
 if is_torch_min_version("1.13.0"):
     dist_all_gather_func = torch.distributed.all_gather_into_tensor
 else:
@@ -265,6 +272,13 @@ def _roll_tensor_packed_seq(tensor, shifts, dims, packed_seq_params, cp_group=No
         # the idx has been multiplied by cp_size, need to divide it by cp_size to get the local idx
         local_start_idx = start_idx // cp_size
         local_end_idx = end_idx // cp_size
+
+        # Skip empty sequences - this can happen when a sequence is very short and
+        # after dividing by cp_size, the local slice has zero length
+        local_seq_len = local_end_idx - local_start_idx
+        if local_seq_len == 0:
+            continue
+
         tensor_slice = rolled_tensor[..., local_start_idx:local_end_idx].clone()
 
         # The following code is very similar as the code in roll_tensor function
@@ -274,6 +288,15 @@ def _roll_tensor_packed_seq(tensor, shifts, dims, packed_seq_params, cp_group=No
         tensor_send_list = []
         tensor_recv_list = []
         for chunk in rolled_chunks:
+            # Skip empty chunks that can occur when the sequence slice is very small
+            if chunk.size(dims) == 0:
+                tensor_send_list.append(
+                    torch.empty(chunk.shape[:-1], dtype=chunk.dtype, device=chunk.device)
+                )
+                tensor_recv_list.append(
+                    torch.empty(chunk.shape[:-1], dtype=chunk.dtype, device=chunk.device)
+                )
+                continue
             boundary = chunk.select(dims, shifts).contiguous().clone()
             tensor_send_list.append(boundary)
             tensor_recv_list.append(torch.empty_like(boundary))
@@ -297,6 +320,9 @@ def _roll_tensor_packed_seq(tensor, shifts, dims, packed_seq_params, cp_group=No
         index = [slice(None)] * rolled_chunks[0].dim()
         index[dims] = shifts
         for chunk, recv in zip(rolled_chunks, tensor_recv_list):
+            # Skip empty chunks
+            if chunk.size(dims) == 0:
+                continue
             chunk[tuple(index)] = recv
 
         seq_result = torch.cat(rolled_chunks, dim=dims)
@@ -369,7 +395,7 @@ def track_mtp_metrics(loss_scale, iteration, writer, wandb_writer=None, total_lo
         mtp_losses = tracker["values"] * loss_scale
         mtp_num_layers = mtp_losses.shape[0]
         for i in range(mtp_num_layers):
-            name = f"mtp_{i+1} loss"
+            name = f"mtp_{i + 1} loss"
             loss = mtp_losses[i]
             if total_loss_dict is not None:
                 if name in total_loss_dict:
@@ -390,25 +416,26 @@ class MultiTokenPredictionLayerSubmodules:
     Dataclass for specifying the submodules of a MultiTokenPrediction module.
 
     Args:
-        hnorm (Union[ModuleSpec, type]): Specification or instance of the
-             hidden states normalization to be applied.
-        enorm (Union[ModuleSpec, type]): Specification or instance of the
-            embedding normalization to be applied.
+        hnorm: Specification or instance of the hidden states normalization to be applied.
+        enorm: Specification or instance of the embedding normalization to be applied.
         eh_proj (Union[ModuleSpec, type]): Specification or instance of the
             linear projection to be applied.
-        transformer_layer (Union[ModuleSpec, type]): Specification
-            or instance of the transformer block to be applied.
+        mtp_model_layer (Union[ModuleSpec, type]): Specification
+            or instance of the transformer or mamba block to be applied.
     """
 
-    enorm: Union[ModuleSpec, type] = None
-    hnorm: Union[ModuleSpec, type] = None
+    enorm: LayerNormBuilder
+    hnorm: LayerNormBuilder
+    # TODO(nschank): Move this back below transformer_layer once eh_proj and transformer_layer have
+    # their defaults removed.
+    layer_norm: LayerNormBuilder
+
     eh_proj: Union[ModuleSpec, type] = None
-    transformer_layer: Union[ModuleSpec, type] = None
-    layer_norm: Union[ModuleSpec, type] = None
+    mtp_model_layer: Union[ModuleSpec, type] = None
 
 
 def get_mtp_layer_spec(
-    transformer_layer_spec: ModuleSpec, use_transformer_engine: bool
+    mtp_model_layer_spec: ModuleSpec, use_transformer_engine: bool
 ) -> ModuleSpec:
     """Get the MTP layer spec.
 
@@ -416,13 +443,13 @@ def get_mtp_layer_spec(
         ModuleSpec: Module specification with TE modules
     """
     return get_mtp_layer_spec_for_backend(
-        transformer_layer_spec,
+        mtp_model_layer_spec,
         backend=TESpecProvider() if use_transformer_engine else LocalSpecProvider(),
     )
 
 
 def get_mtp_layer_spec_for_backend(
-    transformer_layer_spec: ModuleSpec, backend: BackendSpecProvider
+    mtp_model_layer_spec: ModuleSpec, backend: BackendSpecProvider
 ) -> ModuleSpec:
     """Get the MTP layer spec.
 
@@ -430,14 +457,14 @@ def get_mtp_layer_spec_for_backend(
         ModuleSpec: Module specification with modules from the backend.
     """
     column_parallel_linear_impl: type = backend.column_parallel_linear()
-    layer_norm_impl: type = backend.layer_norm()
+    layer_norm_impl = backend.layer_norm()
     mtp_layer_spec = ModuleSpec(
         module=MultiTokenPredictionLayer,
         submodules=MultiTokenPredictionLayerSubmodules(
             enorm=layer_norm_impl,
             hnorm=layer_norm_impl,
             eh_proj=column_parallel_linear_impl,
-            transformer_layer=transformer_layer_spec,
+            mtp_model_layer=mtp_model_layer_spec,
             layer_norm=layer_norm_impl,
         ),
     )
@@ -586,6 +613,117 @@ def set_loss_scale(scale: torch.Tensor):
         MTPLossAutoScaler.main_loss_backward_scale = scale
 
 
+def process_mtp_loss(
+    hidden_states: Tensor,
+    labels: Tensor,
+    loss_mask: Optional[Tensor],
+    output_layer: Callable,
+    output_weight: Optional[Tensor],
+    runtime_gather_output: Optional[bool],
+    is_training: bool,
+    compute_language_model_loss: Callable,
+    config: TransformerConfig,
+    cp_group: Optional[torch.distributed.ProcessGroup] = None,
+    packed_seq_params: Optional[PackedSeqParams] = None,
+    detach_output_weight: bool = False,
+) -> Tensor:
+    """Process Multi-Token Prediction (MTP) loss computation.
+
+    This is a standalone function that handles MTP loss computation. It's used on the
+    post_process rank to split concatenated hidden states and compute MTP losses.
+
+    Args:
+        hidden_states (Tensor): Hidden states tensor (concatenated with MTP outputs).
+        labels (Tensor): Ground truth labels.
+        loss_mask (Optional[Tensor]): Mask for loss computation. If None, uses all ones.
+        output_layer (Callable): Output layer method to compute logits.
+        output_weight (Optional[Tensor]): Optional output weight for shared embeddings.
+        runtime_gather_output (Optional[bool]): Whether to gather output at runtime.
+        is_training (bool): Whether the model is in training mode.
+        compute_language_model_loss (Callable): Method to compute language model loss.
+        config (TransformerConfig): Model configuration containing mtp_num_layers etc.
+        cp_group (Optional[ProcessGroup]): Context parallelism process group.
+        packed_seq_params (Optional[PackedSeqParams]): Packed sequence parameters.
+        detach_output_weight (bool): If True, detach output_weight to prevent MTP gradient
+            flowing to the output layer (useful for RL training). Defaults to False.
+
+    Returns:
+        Tensor: Updated hidden states after MTP loss processing (first chunk only).
+    """
+    hidden_states_list = torch.chunk(hidden_states, 1 + config.mtp_num_layers, dim=0)
+    hidden_states = hidden_states_list[0]
+
+    if labels is None:
+        return hidden_states
+
+    mtp_labels = labels.clone()
+    if loss_mask is None:
+        loss_mask = torch.ones_like(mtp_labels)
+
+    # Store the original number of tokens before rolling for proper normalization
+    # when calculate_per_token_loss is enabled. This ensures MTP gradients are
+    # correctly scaled relative to the main loss gradients in finalize_model_grads.
+    original_num_tokens = loss_mask.sum()
+
+    fuse_linear_cross_entropy = (
+        config.cross_entropy_loss_fusion and config.cross_entropy_fusion_impl == "linear"
+    )
+    for mtp_layer_number in range(config.mtp_num_layers):
+        mtp_labels, _ = roll_tensor(
+            mtp_labels, shifts=-1, dims=-1, cp_group=cp_group, packed_seq_params=packed_seq_params
+        )
+        loss_mask, num_tokens = roll_tensor(
+            loss_mask, shifts=-1, dims=-1, cp_group=cp_group, packed_seq_params=packed_seq_params
+        )
+        mtp_output_weight = (
+            output_weight.detach() if detach_output_weight and output_weight is not None
+            else output_weight
+        )
+        output_layer_kwargs = dict(
+            input_=hidden_states_list[mtp_layer_number + 1],
+            weight=mtp_output_weight,
+            runtime_gather_output=runtime_gather_output,
+        )
+        if fuse_linear_cross_entropy:
+            mtp_loss = output_layer(
+                output_cross_entropy_loss=True, labels=mtp_labels, **output_layer_kwargs
+            )
+        else:
+            mtp_logits, _ = output_layer(**output_layer_kwargs)
+            mtp_loss = compute_language_model_loss(mtp_labels, mtp_logits)
+        mtp_loss = loss_mask * mtp_loss
+        if is_training:
+            mtp_loss_for_log = (
+                torch.sum(mtp_loss) / num_tokens if num_tokens > 0 else mtp_loss.new_tensor(0.0)
+            )
+            MTPLossLoggingHelper.save_loss_to_tracker(
+                mtp_loss_for_log,
+                mtp_layer_number,
+                config.mtp_num_layers,
+                avg_group=parallel_state.get_data_parallel_group(with_context_parallel=True),
+            )
+        mtp_loss_scale = config.mtp_loss_scaling_factor / config.mtp_num_layers
+        if config.calculate_per_token_loss:
+            # When calculate_per_token_loss is enabled, finalize_model_grads will
+            # divide all gradients by total_num_tokens (from main loss).
+            # However, MTP has fewer valid tokens due to rolling. To ensure correct
+            # per-token gradient weighting, we normalize by the rolled token count
+            # and re-scale by the original token count.
+            # Avoid division by zero
+            num_tokens_safe = torch.clamp(num_tokens, min=1)
+            mtp_loss_normalized = (
+                mtp_loss_scale * mtp_loss * (original_num_tokens / num_tokens_safe)
+            )
+            hidden_states = MTPLossAutoScaler.apply(hidden_states, mtp_loss_normalized)
+        else:
+            safe_num_tokens = num_tokens.clamp(min=1)
+            hidden_states = MTPLossAutoScaler.apply(
+                hidden_states, mtp_loss_scale * mtp_loss / safe_num_tokens
+            )
+
+    return hidden_states
+
+
 class MultiTokenPredictionLayer(MegatronModule):
     """The implementation for Multi-Token Prediction (MTP) which extends
     the prediction scope to multiple future tokens at each position.
@@ -602,8 +740,8 @@ class MultiTokenPredictionLayer(MegatronModule):
     the linear projection. The combined serves as the input of the Transformer block at
     the k-th depth to produce the output representation.
 
-    for more information, please refer to DeepSeek-V3 Technical Report
-    https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf
+    For more information, refer to DeepSeek-V3 Technical Report
+    https://arxiv.org/pdf/2412.19437.pdf
     """
 
     def __init__(
@@ -613,6 +751,9 @@ def __init__(
         layer_number: int = 1,
         vp_stage: Optional[int] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        # For Mamba path - pattern and submodules to build inner layers directly
+        mtp_layer_pattern: Optional[str] = None,
+        mamba_submodules: Optional[MambaStackSubmodules] = None,
     ):
         super().__init__(config=config)
         self.sequence_parallel = config.sequence_parallel
@@ -620,24 +761,43 @@ def __init__(
         self.layer_number = layer_number + get_mtp_layer_offset(self.config, vp_stage)
         self.vp_stage = vp_stage
         self.cp_group = pg_collection.cp
+        self.mtp_layer_pattern = mtp_layer_pattern
 
-        self_attention_spec = self.submodules.transformer_layer.submodules.self_attention
-        attn_mask_type = self_attention_spec.params.get('attn_mask_type', '')
-        assert attn_mask_type in SUPPORTED_ATTN_MASK, (
-            f"Multi-Token Prediction (MTP) is not jet supported with "
-            + f"{attn_mask_type} attention mask type."
-            + f"The supported attention mask types are {SUPPORTED_ATTN_MASK}."
-        )
+        # Validate attention mask type if using transformer-based inner layers
+        if self.submodules.mtp_model_layer is not None and hasattr(
+            self.submodules.mtp_model_layer, 'submodules'
+        ):
+            from megatron.core.ssm.mamba_block import MambaStackSubmodules
+            from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
+
+            layer_submodules = None
+            if isinstance(self.submodules.mtp_model_layer.submodules, MambaStackSubmodules):
+                attention_layer_spec = self.submodules.mtp_model_layer.submodules.attention_layer
+                if hasattr(attention_layer_spec, 'submodules'):
+                    assert isinstance(attention_layer_spec.submodules, TransformerLayerSubmodules)
+                    layer_submodules = attention_layer_spec.submodules
+            elif isinstance(self.submodules.mtp_model_layer.submodules, TransformerLayerSubmodules):
+                layer_submodules = self.submodules.mtp_model_layer.submodules
+            else:
+                raise ValueError(
+                    "Unsupported mtp_model_layer submodules type for attention mask validation."
+                )
+            if layer_submodules:
+                self_attention_spec = layer_submodules.self_attention
+                attn_mask_type = self_attention_spec.params.get('attn_mask_type', '')
+                assert attn_mask_type in SUPPORTED_ATTN_MASK, (
+                    f"Multi-Token Prediction (MTP) is not yet supported with "
+                    f"{attn_mask_type} attention mask type. "
+                    f"The supported attention mask types are {SUPPORTED_ATTN_MASK}."
+                )
 
-        self.enorm = build_module(
-            self.submodules.enorm,
+        self.enorm = self.submodules.enorm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
         )
 
-        self.hnorm = build_module(
-            self.submodules.hnorm,
+        self.hnorm = self.submodules.hnorm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
@@ -658,20 +818,39 @@ def __init__(
             bias=False,
             skip_bias_add=False,
             is_expert=False,
+            tp_comm_buffer_name="mtp_eh_proj",
         )
 
-        diff_transformer_layer_offset = self.config.num_layers - get_transformer_layer_offset(
-            self.config, vp_stage
-        )
-        self.transformer_layer = build_module(
-            self.submodules.transformer_layer,
-            config=self.config,
-            vp_stage=vp_stage,
-            layer_number=self.layer_number + diff_transformer_layer_offset,
-        )
+        # Build inner layers: two possible paths
+        # 1. Mamba path: use MambaStack for hybrid pattern support
+        # 2. GPT path: single TransformerLayer
+        if mtp_layer_pattern is not None and mamba_submodules is not None:
+            from megatron.core.ssm.mamba_block import MambaStack
+
+            self.mtp_model_layer = MambaStack(
+                config=self.config,
+                submodules=mamba_submodules,
+                hybrid_override_pattern=mtp_layer_pattern,
+                pre_process=True,  # Always receives input from eh_proj
+                post_layer_norm=False,  # MTP has its own final_layernorm
+                post_process=True,  # MTP layer is self-contained
+                pg_collection=pg_collection,
+                is_mtp_layer=True,
+            )
+        elif self.config.mtp_num_layers is not None:
+            # GPT path: Uses the transformer block spec for MTP layer
+            # MTP inner layers use their own layer numbering (self.layer_number = 1, 2, etc.)
+            # rather than continuing from decoder layer numbers. This is consistent with the
+            # Mamba path and ensures proper aux loss tracking in router.py.
+            self.mtp_model_layer = build_module(
+                self.submodules.mtp_model_layer,
+                config=self.config,
+                vp_stage=self.vp_stage,
+                layer_number=self.layer_number,
+                is_mtp_layer=True,
+            )
 
-        self.final_layernorm = build_module(
-            self.submodules.layer_norm,
+        self.final_layernorm = self.submodules.layer_norm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
@@ -709,17 +888,19 @@ def _get_embeddings(
             cp_group=self.cp_group,
             packed_seq_params=packed_seq_params,
         )
-        position_ids, _ = roll_tensor(
-            position_ids,
-            shifts=-1,
-            dims=-1,
-            cp_group=self.cp_group,
-            packed_seq_params=packed_seq_params,
-        )
+        if position_ids is not None:
+            position_ids, _ = roll_tensor(
+                position_ids,
+                shifts=-1,
+                dims=-1,
+                cp_group=self.cp_group,
+                packed_seq_params=packed_seq_params,
+            )
         # embedding
         decoder_input = embedding(input_ids=input_ids, position_ids=position_ids)
+        decoder_input = decoder_input.detach()
 
-        hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True)
+        hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=False)
 
         return input_ids, position_ids, decoder_input, hidden_states
 
@@ -727,9 +908,9 @@ def _concat_embeddings(self, hidden_states: torch.Tensor, decoder_input: torch.T
         """
         Concatenate the tokens before sending to transformer layer.
         """
-        decoder_input = self.enorm(decoder_input)
+        decoder_input = apply_module(self.enorm)(decoder_input)
         decoder_input = make_viewless_tensor(inp=decoder_input, requires_grad=True, keep_graph=True)
-        hidden_states = self.hnorm(hidden_states)
+        hidden_states = apply_module(self.hnorm)(hidden_states)
         hidden_states = make_viewless_tensor(inp=hidden_states, requires_grad=True, keep_graph=True)
         # At the (k - 1)-th MTP module, concatenates the i-th token's hidden_states
         # and the (i + K)-th token's embedding, and combine them with linear projection.
@@ -779,7 +960,6 @@ def _proj_and_transformer_layer(
             transformer_layer_fp8_context = nullcontext()
 
         # TODO: currently ignoring FP4 in MTP layers because we need more numerical validation
-
         with rng_context:
             with fp8_context:
                 hidden_states = self._concat_embeddings(hidden_states, decoder_input)
@@ -788,19 +968,29 @@ def _proj_and_transformer_layer(
             # transformer layer is cudagraphed, the FP8GlobalStateManager.is_first_fp8_module() is
             # True so that the fp8 weight caching can be triggered correctly.
             with transformer_layer_fp8_context:
-                hidden_states, _ = self.transformer_layer(
-                    hidden_states=hidden_states,
-                    attention_mask=attention_mask,
-                    context=context,
-                    context_mask=context_mask,
-                    rotary_pos_emb=rotary_pos_emb,
-                    rotary_pos_cos=rotary_pos_cos,
-                    rotary_pos_sin=rotary_pos_sin,
-                    attention_bias=attention_bias,
-                    inference_params=inference_params,
-                    packed_seq_params=packed_seq_params,
-                    sequence_len_offset=sequence_len_offset,
-                )
+                if self.mtp_layer_pattern is not None:
+                    hidden_states = self.mtp_model_layer(
+                        hidden_states=hidden_states,
+                        attention_mask=attention_mask,
+                        rotary_pos_emb=rotary_pos_emb,
+                        inference_context=inference_params,
+                        packed_seq_params=packed_seq_params,
+                    )
+                else:
+                    # GPT path: single TransformerLayer
+                    hidden_states, _ = self.mtp_model_layer(
+                        hidden_states=hidden_states,
+                        attention_mask=attention_mask,
+                        context=context,
+                        context_mask=context_mask,
+                        rotary_pos_emb=rotary_pos_emb,
+                        rotary_pos_cos=rotary_pos_cos,
+                        rotary_pos_sin=rotary_pos_sin,
+                        attention_bias=attention_bias,
+                        inference_params=inference_params,
+                        packed_seq_params=packed_seq_params,
+                        sequence_len_offset=sequence_len_offset,
+                    )
 
         hidden_states = self._postprocess(hidden_states)
 
@@ -812,7 +1002,7 @@ def _postprocess(self, hidden_states: torch.Tensor):
         """
 
         # Layer norm before shared head layer.
-        hidden_states = self.final_layernorm(hidden_states)
+        hidden_states = apply_module(self.final_layernorm)(hidden_states)
         # TENorm produces a "viewed" tensor. This will result in schedule.py's
         # deallocate_output_tensor() throwing an error, so a viewless tensor is
         # created to prevent this.
@@ -821,6 +1011,51 @@ def _postprocess(self, hidden_states: torch.Tensor):
         return hidden_states
 
     def _checkpointed_forward(self, forward_func, *args, **kwargs):
+        """Wrap `forward_func` with activation checkpointing while only passing tensors.
+
+        Non-tensor arguments (e.g., configuration objects, None) are captured via closure so
+        that checkpoint implementations never receive them directly, avoiding save_for_backward
+        issues with non-tensor inputs.
+        """
+
+        # TODO(jiajun): Is there any better implementation here?
+        positional_specs = []
+        kw_specs = []
+        tensor_args: List[torch.Tensor] = []
+
+        for arg in args:
+            if torch.is_tensor(arg):
+                positional_specs.append(('tensor', len(tensor_args)))
+                tensor_args.append(arg)
+            else:
+                positional_specs.append(('const', arg))
+
+        for key, value in kwargs.items():
+            if torch.is_tensor(value):
+                kw_specs.append((key, ('tensor', len(tensor_args))))
+                tensor_args.append(value)
+            else:
+                kw_specs.append((key, ('const', value)))
+
+        def run(*flat_tensor_args):
+            rebuilt_args = []
+            for spec_type, payload in positional_specs:
+                if spec_type == 'tensor':
+                    rebuilt_args.append(flat_tensor_args[payload])
+                else:
+                    rebuilt_args.append(payload)
+
+            rebuilt_kwargs = {}
+            for key, (spec_type, payload) in kw_specs:
+                if spec_type == 'tensor':
+                    rebuilt_kwargs[key] = flat_tensor_args[payload]
+                else:
+                    rebuilt_kwargs[key] = payload
+
+            return forward_func(*rebuilt_args, **rebuilt_kwargs)
+
+        tensor_args_tuple = tuple(tensor_args)
+
         def checkpoint_handler():
             """Determines whether to use the `te_checkpoint` or `tensor_parallel.checkpoint`"""
             if self.config.fp8:
@@ -831,12 +1066,11 @@ def checkpoint_handler():
                     self.config.distribute_saved_activations,
                     tensor_parallel.random.get_cuda_rng_tracker,
                     parallel_state.get_tensor_model_parallel_group(),
-                    *args,
-                    **kwargs,
+                    *tensor_args_tuple,
                 )
             else:
                 return tensor_parallel.checkpoint(
-                    forward_func, self.config.distribute_saved_activations, *args, *kwargs.values()
+                    run, self.config.distribute_saved_activations, *tensor_args_tuple
                 )
 
         if self.config.recompute_method == 'uniform':
@@ -897,8 +1131,7 @@ def forward(
             Union[Tensor, Tuple[Tensor, Tensor]]: The output hidden states tensor of shape
             [s, b, h], and optionally the updated context tensor if cross-attention is used.
         """
-        assert context is None, f"multi token prediction + cross attention is not yet supported."
-
+        assert context is None, "multi token prediction + cross attention is not yet supported."
         input_ids, position_ids, decoder_input, hidden_states = self._get_embeddings(
             input_ids=input_ids,
             position_ids=position_ids,
@@ -957,6 +1190,16 @@ def sharded_state_dict(
             token prediction layer.
         """
         sharded_state_dict = super().sharded_state_dict(prefix, sharded_offsets, metadata)
+
+        # Backward compatibility: GPT MTP checkpoints were saved with the submodule
+        # named 'transformer_layer'. Remap checkpoint keys so old checkpoints load
+        # correctly. Mamba MTP models keep 'mtp_model_layer' as their native format
+        # since no older checkpoints exist for them.
+        if self.mtp_layer_pattern is None:
+            apply_prefix_mapping(
+                sharded_state_dict, {f'{prefix}mtp_model_layer.': f'{prefix}transformer_layer.'}
+            )
+
         return sharded_state_dict
 
 
@@ -1022,8 +1265,11 @@ class MultiTokenPredictionBlock(MegatronModule):
     the linear projection. The combined serves as the input of the Transformer block at
     the k-th depth to produce the output representation.
 
-    for more information, please refer to DeepSeek-V3 Technical Report
-    https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf
+    When `mtp_use_repeated_layer=True` in config, instead of creating N separate MTP layers,
+    only 1 layer is created and applied mtp_num_layers times.
+
+    For more information, please refer to DeepSeek-V3 Technical Report
+    https://arxiv.org/pdf/2412.19437.pdf
     """
 
     def __init__(
@@ -1032,11 +1278,26 @@ def __init__(
         spec: Union[TransformerBlockSubmodules, ModuleSpec],
         vp_stage: Optional[int] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
+        # New: For Mamba path with unified pattern syntax
+        mtp_layer_pattern: Optional[str] = None,
+        mtp_num_depths: int = 0,
+        mamba_submodules: Optional["MambaStackSubmodules"] = None,
     ):
         super().__init__(config=config)
         self.submodules = _get_mtp_block_submodules(config, spec)
         self.mtp_loss_scaling_factor = config.mtp_loss_scaling_factor
         self.vp_stage = vp_stage
+        self.mtp_layer_pattern = mtp_layer_pattern
+        self.mtp_num_depths = mtp_num_depths
+        self.mamba_submodules = mamba_submodules
+        self.mtp_use_repeated_layer = self.config.mtp_use_repeated_layer
+
+        vp_size = config.virtual_pipeline_model_parallel_size
+        assert is_vp_last_stage(vp_stage=vp_stage, vp_size=vp_size), (
+            f"MTP layers must be placed on the last virtual pipeline stage. "
+            f"Got vp_stage={vp_stage} with vp_size={vp_size}. "
+            f"Placing MTP layers on different VPP stages is not currently supported."
+        )
 
         # Initialize Context Parallelism (CP) support for MTP
         # This enables MTP to work with CP > 1 by providing the CP process group
@@ -1055,7 +1316,14 @@ def __init__(
         self.cp_group = pg_collection.cp
 
     def _build_layers(self, pg_collection):
-        def build_layer(layer_spec, layer_number):
+        # Determine number of depths to build
+        if self.mtp_num_depths > 0:
+            num_depths = self.mtp_num_depths
+        else:
+            num_depths = self.config.mtp_num_layers or len(self.submodules.layer_specs)
+
+        def build_layer_legacy(layer_spec, layer_number):
+            """Build layer using legacy spec-based approach."""
             fp8_init_context = get_fp8_context(self.config, is_init=True)
             with fp8_init_context:
                 module = build_module(
@@ -1064,15 +1332,71 @@ def build_layer(layer_spec, layer_number):
                     layer_number=layer_number,
                     vp_stage=self.vp_stage,
                     pg_collection=pg_collection,
+                    mtp_layer_pattern=self.mtp_layer_pattern,
                 )
             return module
 
-        self.layers = torch.nn.ModuleList(
-            [
-                build_layer(layer_spec, i + 1)
-                for i, layer_spec in enumerate(self.submodules.layer_specs)
-            ]
-        )
+        def build_layer_with_pattern(layer_spec, layer_number, mtp_layer_pattern, mamba_submodules):
+            """Build layer using pattern-based approach (new Mamba path)."""
+            fp8_init_context = get_fp8_context(self.config, is_init=True)
+            with fp8_init_context:
+                module = build_module(
+                    layer_spec,
+                    config=self.config,
+                    layer_number=layer_number,
+                    vp_stage=self.vp_stage,
+                    pg_collection=pg_collection,
+                    mtp_layer_pattern=mtp_layer_pattern,
+                    mamba_submodules=mamba_submodules,
+                )
+            return module
+
+        # New Mamba path: use mtp_layer_pattern and mamba_submodules
+        if self.mtp_layer_pattern is not None and self.mamba_submodules is not None:
+            if self.mtp_use_repeated_layer:
+                # Shared/repeated layer: build one layer, use it for all depths
+                layer_spec = self.submodules.layer_specs[0]
+                shared_layer = build_layer_with_pattern(
+                    layer_spec,
+                    layer_number=1,
+                    mtp_layer_pattern=self.mtp_layer_pattern,
+                    mamba_submodules=self.mamba_submodules,
+                )
+                self.layers = torch.nn.ModuleList([shared_layer])
+            else:
+                # Non-shared: each depth gets its own layers
+                self.layers = torch.nn.ModuleList(
+                    [
+                        build_layer_with_pattern(
+                            self.submodules.layer_specs[
+                                min(i, len(self.submodules.layer_specs) - 1)
+                            ],
+                            layer_number=i + 1,
+                            mtp_layer_pattern=self.mtp_layer_pattern,
+                            mamba_submodules=self.mamba_submodules,
+                        )
+                        for i in range(num_depths)
+                    ]
+                )
+        elif self.mtp_use_repeated_layer:
+            # Legacy repeated layer mode
+            if len(self.submodules.layer_specs) != 1:
+                warnings.warn(
+                    "Repeated MTP mode expects exactly 1 layer spec, got "
+                    f"{len(self.submodules.layer_specs)} instead. "
+                    f"The first layer will be applied {self.config.mtp_num_layers} times."
+                )
+            self.layers = torch.nn.ModuleList(
+                [build_layer_legacy(self.submodules.layer_specs[0], layer_number=1)]
+            )
+        else:
+            # Legacy mode: build from layer_specs
+            self.layers = torch.nn.ModuleList(
+                [
+                    build_layer_legacy(layer_spec, i + 1)
+                    for i, layer_spec in enumerate(self.submodules.layer_specs)
+                ]
+            )
 
     def forward(
         self,
@@ -1108,8 +1432,9 @@ def forward(
         offset = get_mtp_layer_offset(self.config, self.vp_stage)
         hidden_states_list = list(torch.chunk(hidden_states, 1 + offset, dim=0))
         hidden_states = hidden_states_list[offset]
-        for layer_number in range(len(self.layers)):
-            (hidden_states, input_ids, position_ids) = self.layers[layer_number](
+        for iteration in range(self.config.mtp_num_layers):
+            layer_idx = 0 if self.mtp_use_repeated_layer else iteration
+            (hidden_states, input_ids, position_ids) = self.layers[layer_idx](
                 input_ids=input_ids,
                 position_ids=position_ids,
                 hidden_states=hidden_states,
@@ -1151,7 +1476,7 @@ def sharded_state_dict(
         layer_prefix = f'{prefix}layers.'
         for layer in self.layers:
             offset = get_mtp_layer_offset(self.config, self.vp_stage)
-            sharded_prefix = f'{layer_prefix}{layer.layer_number - 1 }.'
+            sharded_prefix = f'{layer_prefix}{layer.layer_number - 1}.'
 
             state_dict_prefix = f'{layer_prefix}{layer.layer_number - 1 - offset}.'
             sharded_pp_offset = []
diff --git a/megatron/core/transformer/torch_norm.py b/megatron/core/transformer/torch_norm.py
index d0ceca7af41..5948ae600f9 100644
--- a/megatron/core/transformer/torch_norm.py
+++ b/megatron/core/transformer/torch_norm.py
@@ -1,4 +1,6 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+from typing import Protocol
+
 import torch
 
 from megatron.core.jit import jit_fuser
@@ -6,6 +8,22 @@
 from megatron.core.utils import is_torch_min_version
 
 
+class LayerNormInterface(Protocol):
+    """Interface that all LayerNorm implementations should follow."""
+
+    def forward(self, x: torch.Tensor, /) -> torch.Tensor:
+        """Forward method for a LayerNorm implementation."""
+        ...
+
+
+class LayerNormBuilder(Protocol):
+    """A protocol showing how Modules are expected to construct LayerNorms."""
+
+    def __call__(
+        self, *, config: TransformerConfig, hidden_size: int, eps: float
+    ) -> LayerNormInterface: ...
+
+
 class WrappedTorchNorm:
     """
     A conditional wrapper to initialize an instance of PyTorch's
@@ -22,7 +40,7 @@ def __new__(
         persist_layer_norm: bool = False,
         zero_centered_gamma: bool = False,
         normalization: str = "LayerNorm",
-    ):
+    ) -> LayerNormInterface:
         assert (
             not config.layernorm_zero_centered_gamma
         ), f"zero_centered_gamma not supported by torch LayerNorm"
@@ -51,7 +69,7 @@ def __new__(
         return norm_cls(normalized_shape=hidden_size, eps=eps)
 
 
-class L2Norm(torch.nn.Module):
+class L2Norm(torch.nn.Module, LayerNormInterface):
     """
     Applies L2 normalization to the input tensor along the last dimension.
 
@@ -70,7 +88,7 @@ def __init__(self, hidden_size: int, eps: float = 1e-6, **kwargs):
         self.eps = eps
 
     @jit_fuser
-    def _norm(self, x):
+    def _norm(self, x: torch.Tensor) -> torch.Tensor:
         """
         Performs the actual L2 normalization.
 
@@ -83,7 +101,7 @@ def _norm(self, x):
         x_float = x.float()
         return (x_float * torch.rsqrt(x_float.pow(2).mean(-1, keepdim=True) + self.eps)).type_as(x)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
         Forward pass of the L2Norm module.
 
diff --git a/megatron/core/transformer/transformer_block.py b/megatron/core/transformer/transformer_block.py
index f222a2c3a6b..5559b2536a9 100755
--- a/megatron/core/transformer/transformer_block.py
+++ b/megatron/core/transformer/transformer_block.py
@@ -2,7 +2,7 @@
 import logging
 from contextlib import nullcontext
 from dataclasses import dataclass
-from typing import List, Optional, Union
+from typing import List, Optional, Set, Union, cast
 
 import torch
 from torch import Tensor
@@ -21,12 +21,14 @@
 from megatron.core.transformer.enums import CudaGraphScope, LayerType
 from megatron.core.transformer.module import GraphableMegatronModule, MegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.transformer_layer import (
     BaseTransformerLayer,
     get_transformer_layer_offset,
 )
 from megatron.core.transformer.utils import sharded_state_dict_default
+from megatron.core.typed_torch import apply_module, not_none
 from megatron.core.utils import (
     WrappedTensor,
     deprecate_inference_params,
@@ -219,7 +221,7 @@ class TransformerBlockSubmodules:
     """
 
     layer_specs: Optional[List[ModuleSpec]] = None
-    layer_norm: Optional[Union[ModuleSpec, torch.nn.Module]] = None
+    layer_norm: LayerNormBuilder | None = None
 
 
 def _get_block_submodules(
@@ -375,8 +377,7 @@ def build_layer(layer_spec, layer_number):
         # In pipeline parallelism, we want to add this LN only to the last stage of the pipeline
         # self.post_process and self.post_layer_norm guide this behavior
         if self.has_final_layernorm_in_this_stage():
-            self.final_layernorm = build_module(
-                self.submodules.layer_norm,
+            self.final_layernorm = not_none(self.submodules.layer_norm)(
                 config=self.config,
                 hidden_size=self.config.hidden_size,
                 eps=self.config.layernorm_epsilon,
@@ -453,8 +454,26 @@ def _checkpointed_forward(
         packed_seq_params: PackedSeqParams,
         use_inner_quantization_context: bool,
         padding_mask: Optional[Tensor] = None,
+        extract_layer_indices: Optional[Set[int]] = None,
+        layer_offset: int = 0,
     ):
-        """Forward method with activation checkpointing."""
+        """Forward method with activation checkpointing.
+
+        Args:
+            extract_layer_indices (Set[int], optional): Global layer
+                indices (across all pipeline stages) from which to
+                extract features.
+            layer_offset (int): The global layer offset for the current
+                pipeline stage. Used to convert local layer indices to
+                global indices when checking extract_layer_indices.
+
+        Returns:
+            If extract_layer_indices is empty: hidden_states tensor
+            If extract_layer_indices is non-empty: (hidden_states, intermediate_hidden_states) tuple
+        """
+        if extract_layer_indices is None:
+            extract_layer_indices = set()
+        intermediate_hidden_states: List[Tensor] = []
 
         def custom(start: int, end: int):
             def custom_forward(
@@ -538,6 +557,18 @@ def checkpoint_handler(forward_func):
                     custom(layer_idx, layer_idx + self.config.recompute_num_layers)
                 )
 
+                # Feature extraction for uniform recompute: collect at end of each chunk
+                # Note: Only the last layer of each chunk can have features collected
+                chunk_end = min(
+                    layer_idx + self.config.recompute_num_layers, self.num_layers_per_pipeline_rank
+                )
+                for idx in range(layer_idx, chunk_end):
+                    if (idx + layer_offset) in extract_layer_indices:
+                        # For uniform recompute, we can only get features at chunk boundaries
+                        # Limitation: for fine-grained extraction, use 'block'
+                        if idx == chunk_end - 1:
+                            intermediate_hidden_states.append(hidden_states)
+
                 layer_idx += self.config.recompute_num_layers
 
         elif self.config.recompute_method == 'block':
@@ -561,9 +592,17 @@ def checkpoint_handler(forward_func):
                     hidden_states, context = custom(layer_idx, layer_idx + 1)(
                         hidden_states, attention_mask, context, context_mask, rotary_pos_emb
                     )
+
+                # Feature extraction: collect hidden states at specified global layer indices
+                if (layer_idx + layer_offset) in extract_layer_indices:
+                    intermediate_hidden_states.append(hidden_states)
         else:
             raise ValueError("Invalid activation recompute method.")
 
+        # Return intermediate hidden states if feature extraction was requested
+        if len(extract_layer_indices) > 0:
+            return hidden_states, intermediate_hidden_states
+
         return hidden_states
 
     def set_input_tensor(self, input_tensor: Tensor):
@@ -580,14 +619,15 @@ def _should_call_local_cudagraph(self, *args, **kwargs):
         """
         Check if we should call the local cudagraph path.
         """
-        if not self.training and (
-            hasattr(self, 'cudagraph_manager')
+        if (
+            not self.training
+            and hasattr(self, 'cudagraph_manager')
             and kwargs['attention_mask'] is None
             and (
                 kwargs.get('inference_context') is not None
                 or kwargs.get('inference_params') is not None
             )
-            and CudaGraphScope.full_iteration in self.config.cuda_graph_scope
+            and CudaGraphScope.full_iteration_inference in self.config.cuda_graph_scope
         ):
             if kwargs['inference_context'].is_static_batching():
                 using_cuda_graph = kwargs['inference_context'].is_decode_only()
@@ -605,16 +645,7 @@ def __call__(self, *args, **kwargs):
                 if isinstance(kwargs['hidden_states'], WrappedTensor)
                 else kwargs['hidden_states']
             )
-            # dynamic_inference_decode_only is not a real argument to forward, it is only used
-            # to differentiate the cuda graph used for decode from the one used for non-decode
-            # inference.
-            dynamic_inference_decode_only = kwargs['inference_context'].is_decode_only()
-            # cudagraphmanager returns a singleton tuple, whereas the
-            # normal forward returns a tensor, therefore we need
-            # to extract the tensor from the tuple
-            return super().__call__(
-                *args, dynamic_inference_decode_only=dynamic_inference_decode_only, **kwargs
-            )[0]
+            return super().__call__(*args, **kwargs)[0]
         return super().__call__(*args, **kwargs)
 
     def forward(
@@ -632,6 +663,7 @@ def forward(
         packed_seq_params: Optional[PackedSeqParams] = None,
         sequence_len_offset: Optional[Tensor] = None,
         padding_mask: Optional[Tensor] = None,
+        extract_layer_indices: Optional[Set[int]] = None,
         *,
         inference_params: Optional[BaseInferenceContext] = None,
         dynamic_inference_decode_only: Optional[bool] = None,
@@ -663,13 +695,24 @@ def forward(
                 optimizations.
             packed_seq_params (PackedSeqParams, optional): Parameters for packed sequence
                 processing.
+            extract_layer_indices (Set[int], optional): A set of global
+                layer indices (0-based across all pipeline stages) from
+                which to extract intermediate hidden states. If
+                non-empty, the forward pass will collect hidden_states
+                after each specified layer.
             dynamic_inference_decode_only: Optional[bool]: If true, indicates that the current
                 inference context is for decode-only. This args is only used to uniquely
                 identify decode and non-decode cuda graph runners in the cuda graph manager.
 
         Returns:
-            Union[Tensor, Tuple[Tensor, Tensor]]: The output hidden states tensor of shape
-            [s, b, h], and optionally the updated context tensor if cross-attention is used.
+            Union[Tensor, Tuple[Tensor, List[Tensor]]]:
+                - If extract_layer_indices is None or empty: Returns the output hidden states tensor
+                  of shape [s, b, h].
+                - If extract_layer_indices is non-empty: Returns a tuple
+                  of (hidden_states, intermediate_hidden_states) where
+                  intermediate_hidden_states is a list of tensors
+                  corresponding to hidden states after each layer in
+                  extract_layer_indices.
         """
 
         inference_context = deprecate_inference_params(inference_context, inference_params)
@@ -677,6 +720,18 @@ def forward(
         # this is only used to uniquely identify decode and non-decode cuda graph
         # runners in the cuda graph manager
 
+        # Initialize feature collection (consistent with FastGen's Wan implementation)
+        if extract_layer_indices is None:
+            extract_layer_indices = set()
+        intermediate_hidden_states: List[Tensor] = []
+
+        # Calculate the global layer offset for this pipeline stage
+        # This is needed to convert local layer indices to global indices for feature extraction
+        pp_group = self.pg_collection.pp if hasattr(self.pg_collection, 'pp') else None
+        layer_offset = get_transformer_layer_offset(
+            self.config, self.vp_stage, get_pg_rank(pp_group)
+        )
+
         # Delete the obsolete reference to the initial input tensor if necessary
         if isinstance(hidden_states, WrappedTensor):
             hidden_states = hidden_states.unwrap()
@@ -732,7 +787,7 @@ def forward(
         with rng_context, outer_quantization_context:
             # Forward pass.
             if self.config.recompute_granularity == 'full' and self.training:
-                hidden_states = self._checkpointed_forward(
+                checkpointed_result = self._checkpointed_forward(
                     hidden_states=hidden_states,
                     attention_mask=attention_mask,
                     context=context,
@@ -742,7 +797,16 @@ def forward(
                     packed_seq_params=packed_seq_params,
                     use_inner_quantization_context=use_inner_quantization_context,
                     padding_mask=padding_mask,
+                    extract_layer_indices=extract_layer_indices,
+                    layer_offset=layer_offset,
                 )
+                # Handle return value from _checkpointed_forward
+                if len(extract_layer_indices) > 0:
+                    # (hidden_states, intermediate_hidden_states) tuple
+                    hidden_states, intermediate_hidden_states = checkpointed_result
+                else:
+                    # No intermediate_hidden_states requested: just hidden_states
+                    hidden_states = checkpointed_result
             else:
                 for l_no, layer in enumerate(self.layers):
                     # Get appropriate inner quantization context
@@ -784,9 +848,13 @@ def forward(
                     ):
                         hidden_states = self.group_prefetch_offload_commit_async(hidden_states)
 
+                    # Extract intermediate embeddings using global layer index
+                    if (l_no + layer_offset) in extract_layer_indices:
+                        intermediate_hidden_states.append(hidden_states)
+
         # Final layer norm.
         if self.final_layernorm is not None:
-            hidden_states = self.final_layernorm(hidden_states)
+            hidden_states = apply_module(self.final_layernorm)(cast(Tensor, hidden_states))
             # TENorm produces a "viewed" tensor. This will result in schedule.py's
             # deallocate_output_tensor() throwing an error, so a viewless tensor is
             # created to prevent this.
@@ -799,6 +867,9 @@ def forward(
         if not self.pre_process and len(self.layers) == 0 and not self.final_layernorm:
             hidden_states = hidden_states.clone()
 
+        if len(extract_layer_indices) > 0:
+            return hidden_states, intermediate_hidden_states
+
         return hidden_states
 
     def sharded_state_dict(
diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py
index dce438520aa..0a36bd4952c 100644
--- a/megatron/core/transformer/transformer_config.py
+++ b/megatron/core/transformer/transformer_config.py
@@ -1,5 +1,6 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
+import logging
 import warnings
 from dataclasses import dataclass, field
 from typing import Callable, List, Literal, Optional, Tuple, Union
@@ -13,6 +14,7 @@
 from megatron.core.transformer.pipeline_parallel_layer_layout import PipelineParallelLayerLayout
 from megatron.core.utils import experimental_api
 
+from .._rank_utils import log_single_rank
 from ..fusions.fused_bias_geglu import quick_gelu
 from ..model_parallel_config import ModelParallelConfig
 from ..utils import (
@@ -23,6 +25,8 @@
     scaled_init_method_normal,
 )
 
+logger = logging.getLogger(__name__)
+
 try:
     from packaging.version import Version as PkgVersion
 
@@ -61,6 +65,15 @@ class TransformerConfig(ModelParallelConfig):
     which serves as an additional training objective.
     """
 
+    mtp_use_repeated_layer: bool = False
+    """Use a single MTP layer repeatedly instead of multiple separate layers."""
+
+    mtp_hybrid_override_pattern: Optional[str] = None
+    """DEPRECATED: Use unified hybrid_override_pattern instead.
+    Legacy argument for loading old checkpoints.
+    Force a specific hybrid layer pattern for MTP layers.
+    """
+
     num_layers_in_first_pipeline_stage: Optional[int] = None
     """Number of transformer layers on first pipeline stage.
     None implies equal layer division across PP ranks."""
@@ -172,7 +185,7 @@ class TransformerConfig(ModelParallelConfig):
     gated_linear_unit: bool = False
     """Use a gated linear unit for the first linear layer in the MLP."""
 
-    activation_func: Callable = F.gelu
+    activation_func: Callable[[torch.Tensor], torch.Tensor] = F.gelu
     """Activation function to use for the non-linearity in the MLP."""
 
     activation_func_fp8_input_store: bool = False
@@ -229,6 +242,9 @@ class TransformerConfig(ModelParallelConfig):
     attention_output_gate: bool = False
     """Whether to apply output gate to the attention layers."""
 
+    post_self_attn_layernorm: bool = False
+    post_mlp_layernorm: bool = False
+
     test_mode: bool = False
     """Whether to run real-time tests."""
 
@@ -708,6 +724,12 @@ class TransformerConfig(ModelParallelConfig):
     the expert capacity length, effective only after the moe_expert_capacity_factor is set. The
     default setting is False."""
 
+    moe_pad_experts_for_cuda_graph_inference: bool = False
+    """moe_pad_experts_for_cuda_graph_inference (bool): If True, the router will switch to dropping
+    and padding during decode time which does not have a D2H sync. The capacity factor is set to the
+    max that an expert could see during inference so no tokens are actually dropped. The default
+    setting is False."""
+
     moe_token_drop_policy: Literal['probs', 'position'] = "probs"
     """The policy to drop tokens. Can be either "probs" or "position". If "probs", the tokens with
     the lowest probabilities will be dropped. If "position", tokens at the end of each batch will
@@ -842,6 +864,9 @@ class TransformerConfig(ModelParallelConfig):
     which is no use of symmetric memory.
     """
 
+    nccl_all_reduce_for_prefill: bool = False
+    """If True, use NCCL all-reduce kernels when symmetric all-reduce is enabled."""
+
     use_inference_optimized_layers: bool = False
     """If True, use inference optimized transformer layers during inference."""
 
@@ -927,6 +952,20 @@ def __post_init__(self):
         details.
         """
         super().__post_init__()
+
+        # When fp32 residual connections are enabled, pipeline parallel communication must
+        # use fp32 to match the dtype of the residual stream between pipeline stages.
+        if self.fp32_residual_connection and self.pipeline_dtype is not None:
+            if self.pipeline_dtype != torch.float:
+                log_single_rank(
+                    logger,
+                    logging.WARNING,
+                    f"fp32_residual_connection is enabled, overriding pipeline_dtype "
+                    f"from {self.pipeline_dtype} to torch.float to match the "
+                    f"residual stream dtype between pipeline stages.",
+                )
+            self.pipeline_dtype = torch.float
+
         if self.fp16 and self.bf16:
             raise ValueError(
                 f"Only one of self.fp16: {self.fp16} and self.bf16 {self.bf16} should be True."
diff --git a/megatron/core/transformer/transformer_layer.py b/megatron/core/transformer/transformer_layer.py
index 12c24868473..7420be7a99c 100644
--- a/megatron/core/transformer/transformer_layer.py
+++ b/megatron/core/transformer/transformer_layer.py
@@ -1,11 +1,12 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+from __future__ import annotations
 
 import functools
 import logging
 import warnings
 from abc import ABC
 from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
 
 import torch
 import torch.distributed
@@ -22,7 +23,9 @@
 from megatron.core.transformer.mlp import MLP
 from megatron.core.transformer.module import GraphableMegatronModule
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from megatron.core.transformer.torch_norm import LayerNormBuilder
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.typed_torch import apply_module, copy_signature
 from megatron.core.utils import (
     deprecate_inference_params,
     get_pg_rank,
@@ -33,6 +36,9 @@
     nvtx_range_push,
 )
 
+if TYPE_CHECKING:
+    from megatron.core.inference.contexts import BaseInferenceContext
+
 logger = logging.getLogger(__name__)
 
 
@@ -203,16 +209,16 @@ class TransformerLayerSubmodules:
     of the layer's architecture.
 
     Args:
-        input_layernorm (Union[ModuleSpec, type]): Specification for the input layer normalization.
+        input_layernorm: Specification for the input layer normalization.
         self_attention (Union[ModuleSpec, type]): Specification for the self-attention mechanism.
         self_attn_bda (Union[ModuleSpec, type]): Specification for the bias-dropout-add operation
             after self-attention.
-        pre_cross_attn_layernorm (Union[ModuleSpec, type]): Specification for the layer
+        pre_cross_attn_layernorm: Specification for the layer
             normalization before cross-attention.
         cross_attention (Union[ModuleSpec, type]): Specification for the cross-attention mechanism.
         cross_attn_bda (Union[ModuleSpec, type]): Specification for the bias-dropout-add operation
             after cross-attention.
-        pre_mlp_layernorm (Union[ModuleSpec, type]): Specification for the layer normalization
+        pre_mlp_layernorm: Specification for the layer normalization
             before the MLP.
         mlp (Union[ModuleSpec, type]): Specification for the MLP in Dense layer.
         mlp_bda (Union[ModuleSpec, type]): Specification for the bias-dropout-add operation
@@ -221,17 +227,19 @@ class TransformerLayerSubmodules:
             in the `sharded_state_dict` method.
     """
 
-    input_layernorm: Union[ModuleSpec, type] = IdentityOp
+    input_layernorm: LayerNormBuilder = IdentityOp
     self_attention: Union[ModuleSpec, type] = IdentityOp
     self_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp
+    post_self_attn_layernorm: Union[ModuleSpec, type] = IdentityOp
 
-    pre_cross_attn_layernorm: Union[ModuleSpec, type] = IdentityOp
+    pre_cross_attn_layernorm: LayerNormBuilder = IdentityOp
     cross_attention: Union[ModuleSpec, type] = IdentityOp
     cross_attn_bda: Union[ModuleSpec, type] = IdentityFuncOp
 
-    pre_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp
+    pre_mlp_layernorm: LayerNormBuilder = IdentityOp
     mlp: Union[ModuleSpec, type] = IdentityOp
     mlp_bda: Union[ModuleSpec, type] = IdentityFuncOp
+    post_mlp_layernorm: Union[ModuleSpec, type] = IdentityOp
 
     # Mapping for sharded tensor keys to be applied in `sharded_state_dict` method
     sharded_state_dict_keys_map: Dict[str, str] = field(default_factory=dict)
@@ -268,6 +276,7 @@ def __init__(
         hidden_dropout: Optional[float] = None,
         pg_collection: Optional[ProcessGroupCollection] = None,
         vp_stage: Optional[int] = None,
+        is_mtp_layer: bool = False,
     ):
         self.submodules_config = submodules
         super().__init__(config=config, vp_stage=vp_stage)
@@ -277,15 +286,22 @@ def __init__(
         self.pg_collection = pg_collection
         self.tp_group = pg_collection.tp
 
-        self.layer_number = layer_number + get_transformer_layer_offset(
-            self.config, vp_stage, get_pg_rank(pg_collection.pp)
-        )
+        # MTP inner layers use their own layer numbering (starting from 1 within each MTP depth),
+        # so they should NOT add the decoder layer offset. The router.py handles MTP layer
+        # numbering separately by adding config.num_layers to distinguish MTP layers from decoder
+        # layers in the aux loss tracker.
+        if is_mtp_layer:
+            self.layer_number = layer_number
+        else:
+            self.layer_number = layer_number + get_transformer_layer_offset(
+                self.config, vp_stage, get_pg_rank(pg_collection.pp)
+            )
         self.hidden_dropout = config.hidden_dropout if hidden_dropout is None else hidden_dropout
+        self.is_mtp_layer = is_mtp_layer
 
         # [Module 1: Input Layernorm] Optional Layernorm on the input data
         # TODO: add pytorch only layernorm
-        self.input_layernorm = build_module(
-            submodules.input_layernorm,
+        self.input_layernorm = submodules.input_layernorm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
@@ -311,9 +327,15 @@ def __init__(
         # [Module 3: BiasDropoutFusion]
         self.self_attn_bda = build_module(submodules.self_attn_bda)
 
+        self.post_self_attn_layernorm = build_module(
+            submodules.post_self_attn_layernorm,
+            config=self.config,
+            hidden_size=self.config.hidden_size,
+            eps=self.config.layernorm_epsilon,
+        )
+
         # [Module 4: Post SelfAttention] Optional Layernorm after self-attn
-        self.pre_cross_attn_layernorm = build_module(
-            submodules.pre_cross_attn_layernorm,
+        self.pre_cross_attn_layernorm = submodules.pre_cross_attn_layernorm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
@@ -331,8 +353,7 @@ def __init__(
         self.cross_attn_bda = build_module(submodules.cross_attn_bda, config=self.config)
 
         # [Module 7: Pre MLP] Optional Layernorm before MLP
-        self.pre_mlp_layernorm = build_module(
-            submodules.pre_mlp_layernorm,
+        self.pre_mlp_layernorm = submodules.pre_mlp_layernorm(
             config=self.config,
             hidden_size=self.config.hidden_size,
             eps=self.config.layernorm_epsilon,
@@ -351,6 +372,9 @@ def __init__(
         if isinstance(submodules.mlp, ModuleSpec):
             if submodules.mlp.module in (MoELayer, GroupedMLP, TEGroupedMLP, SequentialMLP):
                 additional_mlp_kwargs["pg_collection"] = pg_collection
+                # Pass is_mtp_layer flag to MoELayer to distinguish MTP MoE layers.
+                if submodules.mlp.module == MoELayer:
+                    additional_mlp_kwargs["is_mtp_layer"] = self.is_mtp_layer
             elif submodules.mlp.module == MLP:
                 assert hasattr(
                     pg_collection, 'tp'
@@ -376,10 +400,18 @@ def __init__(
 
         self.is_moe_layer = isinstance(self.mlp, MoELayer)
 
+        self.post_mlp_layernorm = build_module(
+            submodules.post_mlp_layernorm,
+            config=self.config,
+            hidden_size=self.config.hidden_size,
+            eps=self.config.layernorm_epsilon
+        )
+
         self.recompute_input_layernorm = False
         self.recompute_pre_mlp_layernorm = False
         self.recompute_mlp = False
         if self.config.recompute_granularity == 'selective':
+            assert self.config.recompute_modules is not None
             if "layernorm" in self.config.recompute_modules:
                 if not isinstance(self.input_layernorm, IdentityOp):
                     self.recompute_input_layernorm = True
@@ -499,25 +531,6 @@ def _get_layer_offset(config: TransformerConfig):
         )
         return get_transformer_layer_offset(config)
 
-    def forward(self, *args, **kwargs):
-        """
-        Perform a forward pass through the transformer layer.
-
-        This method calls the core computation of a transformer layer, including
-        self-attention, cross-attention (if applicable), and feed-forward operations.
-        """
-        # Remove 'dynamic_inference_decode_only' from kwargs if present
-        # this is only used to uniquely identify decode and non-decode cuda graph
-        # runners in the cuda graph manager
-        kwargs.pop("dynamic_inference_decode_only", None)
-        hidden_states, context = self._forward_attention(*args, **kwargs)
-        output = self._forward_mlp(
-            hidden_states,
-            kwargs.get("inference_context", None),
-            padding_mask=kwargs.get("padding_mask", None),
-        )
-        return output, context
-
     def _forward_attention(
         self,
         hidden_states: Tensor,
@@ -529,7 +542,7 @@ def _forward_attention(
         rotary_pos_sin: Optional[Tensor] = None,
         rotary_pos_cos_sin: Optional[Tensor] = None,
         attention_bias: Optional[Tensor] = None,
-        inference_context: Optional[Any] = None,
+        inference_context: Optional[BaseInferenceContext] = None,
         packed_seq_params: Optional[PackedSeqParams] = None,
         sequence_len_offset: Optional[Tensor] = None,
         padding_mask: Optional[Tensor] = None,
@@ -571,17 +584,19 @@ def _forward_attention(
 
         # Residual connection.
         residual = hidden_states
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
 
         # Optional Input Layer norm
         if self.recompute_input_layernorm:
             self.input_layernorm_checkpoint = tensor_parallel.CheckpointWithoutOutput()
             with off_interface(self.offload_attn_norm, hidden_states, "attn_norm") as hidden_states:
                 input_layernorm_output = self.input_layernorm_checkpoint.checkpoint(
-                    self.input_layernorm, hidden_states
+                    apply_module(self.input_layernorm), hidden_states
                 )
         else:
             with off_interface(self.offload_attn_norm, hidden_states, "attn_norm") as hidden_states:
-                input_layernorm_output = self.input_layernorm(hidden_states)
+                input_layernorm_output = apply_module(self.input_layernorm)(hidden_states)
 
         using_fused_tp_inference_kernel = (not self.training) and (
             self.config.inference_fuse_tp_communication
@@ -615,6 +630,10 @@ def _forward_attention(
                 attention_output_with_bias[0]
             )
 
+        attention_output, attention_output_bias = attention_output_with_bias
+        attention_output = self.post_self_attn_layernorm(attention_output)
+        attention_output_with_bias = (attention_output, attention_output_bias)
+
         # TODO: could we move `bias_dropout_add_exec_handler` itself
         # inside the module provided in the `bias_dropout_add_spec` module?
         nvtx_range_push(suffix="self_attn_bda")
@@ -639,9 +658,11 @@ def _forward_attention(
 
         # Residual connection.
         residual = hidden_states
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
 
         # Optional Layer norm after self-attention
-        pre_cross_attn_layernorm_output = self.pre_cross_attn_layernorm(hidden_states)
+        pre_cross_attn_layernorm_output = apply_module(self.pre_cross_attn_layernorm)(hidden_states)
 
         # Cross attention.
         attention_output_with_bias = self.cross_attention(
@@ -663,7 +684,25 @@ def _forward_attention(
 
         return hidden_states, context
 
-    def _forward_pre_mlp_layernorm(self, hidden_states):
+    @copy_signature(_forward_attention)
+    def forward(self, *args, **kwargs):
+        """
+        Perform a forward pass through the transformer layer.
+
+        This method calls the core computation of a transformer layer, including
+        self-attention, cross-attention (if applicable), and feed-forward operations.
+        """
+        # Injected by __call__ for cuda graph keying; not a real forward arg.
+        kwargs.pop("dynamic_inference_decode_only", None)
+        hidden_states, context = self._forward_attention(*args, **kwargs)
+        output = self._forward_mlp(
+            hidden_states,
+            kwargs.get("inference_context", None),
+            padding_mask=kwargs.get("padding_mask", None),
+        )
+        return output, context
+
+    def _forward_pre_mlp_layernorm(self, hidden_states: Tensor):
         from megatron.core.pipeline_parallel.fine_grained_activation_offload import (
             FineGrainedActivationOffloadingInterface as off_interface,
         )
@@ -672,15 +711,20 @@ def _forward_pre_mlp_layernorm(self, hidden_states):
             self.pre_mlp_norm_checkpoint = tensor_parallel.CheckpointWithoutOutput()
             with off_interface(self.offload_mlp_norm, hidden_states, "mlp_norm") as hidden_states:
                 pre_mlp_layernorm_output = self.pre_mlp_norm_checkpoint.checkpoint(
-                    self.pre_mlp_layernorm, hidden_states
+                    apply_module(self.pre_mlp_layernorm), hidden_states
                 )
         else:
             with off_interface(self.offload_mlp_norm, hidden_states, "mlp_norm") as hidden_states:
-                pre_mlp_layernorm_output = self.pre_mlp_layernorm(hidden_states)
+                pre_mlp_layernorm_output = apply_module(self.pre_mlp_layernorm)(hidden_states)
 
         return pre_mlp_layernorm_output
 
-    def _forward_mlp(self, hidden_states, inference_context=None, padding_mask=None):
+    def _forward_mlp(
+        self,
+        hidden_states: Tensor,
+        inference_context: BaseInferenceContext | None = None,
+        padding_mask: Tensor | None = None,
+    ) -> Tensor | list[Tensor | None]:
         """
         Perform a forward pass through the feed-forward layer.
 
@@ -698,6 +742,8 @@ def _forward_mlp(self, hidden_states, inference_context=None, padding_mask=None)
 
         # Residual connection.
         residual = hidden_states
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
 
         # Optional Layer norm post the cross-attention.
         pre_mlp_layernorm_output = self._forward_pre_mlp_layernorm(hidden_states)
@@ -755,6 +801,10 @@ def _forward_mlp(self, hidden_states, inference_context=None, padding_mask=None)
                 self._set_fc2_residual(residual)
             mlp_output_with_bias = self.mlp(pre_mlp_layernorm_output, padding_mask=padding_mask)
 
+        mlp_output, mlp_output_bias = mlp_output_with_bias
+        mlp_output = self.post_mlp_layernorm(mlp_output)
+        mlp_output_with_bias = (mlp_output, mlp_output_bias)
+
         nvtx_range_pop(suffix="mlp")
 
         if (
@@ -775,7 +825,9 @@ def _forward_mlp(self, hidden_states, inference_context=None, padding_mask=None)
         else:
             return self._forward_post_mlp(mlp_output_with_bias, residual)
 
-    def _forward_post_mlp(self, mlp_output_with_bias, residual):
+    def _forward_post_mlp(
+        self, mlp_output_with_bias: tuple[Tensor, Tensor | None], residual: Tensor
+    ) -> Tensor:
         """
         Perform operations after the MLP computation.
 
@@ -1110,7 +1162,7 @@ def _te_cuda_graph_replay(self, *args, **kwargs):
                 residual = cuda_graph_output.pop()
                 if not self.is_moe_layer:
                     return residual, None, None, None
-                hidden_states = self.pre_mlp_layernorm(residual)
+                hidden_states = apply_module(self.pre_mlp_layernorm)(residual)
                 shared_expert_output = self.mlp.shared_experts_compute(hidden_states)
                 probs, routing_map = self.mlp.route(hidden_states)
                 hidden_states, probs = self.mlp.preprocess(hidden_states, probs, routing_map)
@@ -1190,7 +1242,7 @@ def _should_call_local_cudagraph(self, *args, **kwargs):
                 (kwargs.get('inference_context') is not None)
                 or (kwargs.get('inference_params') is not None)
             )
-            and CudaGraphScope.full_iteration not in self.config.cuda_graph_scope
+            and not self.config.cuda_graph_scope  # empty-list = per-layer CUDA graphs
         ):
             if kwargs['inference_context'].is_static_batching():
                 using_cuda_graph = kwargs['inference_context'].is_decode_only()
@@ -1295,6 +1347,8 @@ def _forward_mlp_router(self, hidden_states, padding_mask=None):
         """
 
         residual = hidden_states
+        if self.config.fp32_residual_connection:
+            residual = residual.float()
         self.mlp.fwd_execution_map = "route"
         pre_mlp_layernorm_output = self._forward_pre_mlp_layernorm(hidden_states)
         router_outputs = self.mlp(
diff --git a/megatron/core/transformer/utils.py b/megatron/core/transformer/utils.py
index 880c5309933..3b2b8668252 100644
--- a/megatron/core/transformer/utils.py
+++ b/megatron/core/transformer/utils.py
@@ -391,14 +391,12 @@ def find_modules_with_attrs(module):
     find_modules_with_attrs(model_modules)
 
 
-def toggle_cuda_graphs(model, set_to="none", reset_cuda_graphs=True):
+def toggle_cuda_graphs(model, set_to="none"):
     """
     Toggle CUDA graph-related attributes for the model and its modules.
 
     Args:
         set_to (str): Value to set for CUDA graph-related attributes.
-        reset_cuda_graphs (bool): If True, remake the CUDA graph;
-            if False, use cached CUDA graph managers.
     """
     global cuda_graph_attr_cache
     model_id = id(model)
@@ -427,26 +425,14 @@ def toggle_cuda_graphs(model, set_to="none", reset_cuda_graphs=True):
         elif attribute == "cudagraph_manager":
             for module in modules:
                 if set_to == "local":
-                    if reset_cuda_graphs:
-                        from megatron.core.transformer.cuda_graphs import CudaGraphManager
-
-                        # If we are resetting cuda graphs we create a new cuda graph manager
-                        setattr(module[0], attribute, CudaGraphManager(model.config))
-                    else:
-                        # If we are not resetting cuda graphs we set it to its cached cuda graph
-                        setattr(module[0], attribute, module[1])
+                    # If we are not resetting cuda graphs we set it to its cached cuda graph
+                    setattr(module[0], attribute, module[1])
                 else:
                     for module in modules:
                         # If we are deleting the cuda graph, we delete its attribute
                         if hasattr(module[0], "cudagraph_manager"):
                             delattr(module[0], "cudagraph_manager")
 
-    from megatron.core.transformer.cuda_graphs import delete_cuda_graphs
-
-    # if we are resetting cuda graphs we need to reset all the state
-    if reset_cuda_graphs and set_to == "none":
-        delete_cuda_graphs()
-
 
 def is_layer_window_attention(
     window_size: Optional[Tuple[int, int]], window_attn_skip_freq: int | list, layer_number: int
diff --git a/megatron/core/typed_torch.py b/megatron/core/typed_torch.py
index bcbf388facc..57d31fc2867 100644
--- a/megatron/core/typed_torch.py
+++ b/megatron/core/typed_torch.py
@@ -2,8 +2,9 @@
 """Utilities for improved type hinting with torch interfaces."""
 from __future__ import annotations
 
+import inspect
 from collections.abc import Callable
-from typing import Generic, ParamSpec, Protocol, TypeVar
+from typing import Any, Concatenate, Generic, Literal, ParamSpec, Protocol, TypeVar, overload
 
 import torch
 
@@ -48,3 +49,174 @@ def not_none(value: T | None) -> T:
     if value is None:
         raise ValueError('Expected value to be not None')
     return value
+
+
+R_src = TypeVar('R_src')
+R_dst = TypeVar('R_dst')
+P_src = ParamSpec('P_src')
+P_dst = ParamSpec('P_dst')
+First_dst = TypeVar('First_dst')
+
+
+@overload
+def copy_signature(
+    source: Callable[P_src, Any],
+    /,
+    *,
+    handle_return_type: Literal['preserve'] = 'preserve',
+    handle_first_src_param: Literal['copy'] = 'copy',
+    handle_first_dst_param: Literal['drop'] = 'drop',
+) -> Callable[[Callable[..., R_dst]], Callable[P_src, R_dst]]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[P_src, R_src],
+    /,
+    *,
+    handle_return_type: Literal['overwrite'],
+    handle_first_src_param: Literal['copy'] = 'copy',
+    handle_first_dst_param: Literal['drop'] = 'drop',
+) -> Callable[[Callable[..., Any]], Callable[P_src, R_src]]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[Concatenate[Any, P_src], Any],
+    /,
+    *,
+    handle_return_type: Literal['preserve'] = 'preserve',
+    handle_first_src_param: Literal['skip'],
+    handle_first_dst_param: Literal['drop'] = 'drop',
+) -> Callable[[Callable[..., R_dst]], Callable[P_src, R_dst]]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[Concatenate[Any, P_src], R_src],
+    /,
+    *,
+    handle_return_type: Literal['overwrite'],
+    handle_first_src_param: Literal['skip'],
+    handle_first_dst_param: Literal['drop'] = 'drop',
+) -> Callable[[Callable[..., Any]], Callable[P_src, R_src]]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[P_src, Any],
+    /,
+    *,
+    handle_return_type: Literal['preserve'] = 'preserve',
+    handle_first_src_param: Literal['copy'] = 'copy',
+    handle_first_dst_param: Literal['preserve'],
+) -> Callable[
+    [Callable[Concatenate[First_dst, ...], R_dst]], Callable[Concatenate[First_dst, P_src], R_dst]
+]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[P_src, R_src],
+    /,
+    *,
+    handle_return_type: Literal['overwrite'],
+    handle_first_src_param: Literal['copy'] = 'copy',
+    handle_first_dst_param: Literal['preserve'],
+) -> Callable[
+    [Callable[Concatenate[First_dst, ...], Any]], Callable[Concatenate[First_dst, P_src], R_src]
+]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[Concatenate[Any, P_src], Any],
+    /,
+    *,
+    handle_return_type: Literal['preserve'] = 'preserve',
+    handle_first_src_param: Literal['skip'],
+    handle_first_dst_param: Literal['preserve'],
+) -> Callable[
+    [Callable[Concatenate[First_dst, ...], R_dst]], Callable[Concatenate[First_dst, P_src], R_dst]
+]: ...
+
+
+@overload
+def copy_signature(
+    source: Callable[Concatenate[Any, P_src], R_src],
+    /,
+    *,
+    handle_return_type: Literal['overwrite'],
+    handle_first_src_param: Literal['skip'],
+    handle_first_dst_param: Literal['preserve'],
+) -> Callable[
+    [Callable[Concatenate[First_dst, ...], Any]], Callable[Concatenate[First_dst, P_src], R_src]
+]: ...
+
+
+def copy_signature(
+    source: Callable[..., Any],
+    /,
+    *,
+    handle_return_type: Literal['preserve', 'overwrite'] = 'preserve',
+    handle_first_src_param: Literal['copy', 'skip'] = 'copy',
+    handle_first_dst_param: Literal['preserve', 'drop'] = 'drop',
+):
+    """Decorator to copy the signature from one function to another.
+
+      Similar to `functools.wraps`, but preserves the signature instead of the
+      metadata. Useful when writing adapter/wrapper functions that forward arguments
+      to another function, as in:
+
+          def function_with_lots_of_args(
+              a: int,
+              b: str,
+              c: float,
+              ...
+          ) -> BigObject:
+              ...
+
+          @copy_signature(function_with_lots_of_args)
+          def convenient_wrapper(*args: Any, **kwargs: Any) -> str:
+              return function_with_lots_of_args(*args, **kwargs).to_string()
+
+    Args:
+        source: The function or callable from which to copy the signature.
+        handle_return_type: How to handle the return type of the decorated
+          function. 'preserve' to keep the decorated function's return type
+          (the default, since many wrappers are specifically written to return a
+          different type), or 'overwrite' to copy the source function's return
+          type as well.
+        handle_first_src_param: Whether to include the first parameter of the
+          source function. 'copy' to include it in the decorated function's
+          signature (the default), 'skip' to exclude it (useful for removing
+          'self' or 'cls').
+        handle_first_dst_param: Whether to keep the first parameter of the
+          decorated function. 'drop' to overwrite it just like any other parameter
+          (the default), or 'preserve' to keep it in the decorated function's
+          signature (useful for preserving 'self' or 'cls').
+
+      Returns:
+          A decorator that copies the signature from `source` to the decorated function.
+    """
+    source_signature = inspect.signature(source)
+
+    def decorator(decorated: Callable[..., Any], /) -> Callable[..., Any]:
+        dest_signature = inspect.signature(decorated)
+        new_params = []
+        if handle_first_dst_param == 'preserve':
+            new_params.append(next(iter(dest_signature.parameters.values())))
+        src_params_iter = iter(source_signature.parameters.values())
+        if handle_first_src_param == 'skip':
+            next(src_params_iter)
+        new_params.extend(src_params_iter)
+        new_signature = dest_signature.replace(parameters=new_params)
+        if handle_return_type == 'overwrite':
+            new_signature = new_signature.replace(
+                return_annotation=source_signature.return_annotation
+            )
+
+        decorated.__signature__ = new_signature  # type: ignore
+        return decorated
+
+    return decorator
diff --git a/megatron/core/utils.py b/megatron/core/utils.py
index 036589b209a..7c60f2da457 100644
--- a/megatron/core/utils.py
+++ b/megatron/core/utils.py
@@ -92,6 +92,7 @@
     _torch_version = PkgVersion("0.0.0") if HAVE_PACKAGING else "0.0.0"
 _te_version = None
 _fa_version = None
+_flashinfer_version = None
 _mamba_ssm_version = None
 _causal_conv1d_version = None
 
@@ -485,6 +486,44 @@ def is_causal_conv1d_min_version(version, check_equality=True):
     return get_causal_conv1d_version() > PkgVersion(version)
 
 
+def get_flashinfer_version():
+    """Get flashinfer version from __version__; if not available use pip's. Use caching."""
+    if not HAVE_PACKAGING:
+        raise ImportError(
+            "packaging is not installed. Please install it with `pip install packaging`."
+        )
+
+    def get_flashinfer_version_str():
+        try:
+            import flashinfer
+        except ImportError:
+            return None
+
+        if hasattr(flashinfer, "__version__"):
+            return str(flashinfer.__version__)
+        else:
+            return version("flashinfer")
+
+    global _flashinfer_version
+    if _flashinfer_version is None:
+        if (flashinfer_version_str := get_flashinfer_version_str()) is not None:
+            _flashinfer_version = PkgVersion(flashinfer_version_str)
+    return _flashinfer_version
+
+
+def is_flashinfer_min_version(version, check_equality=True):
+    """Check if minimum version of `flashinfer` is installed."""
+    if not HAVE_PACKAGING:
+        raise ImportError(
+            "packaging is not installed. Please install it with `pip install packaging`."
+        )
+    if (flashinfer_version := get_flashinfer_version()) is None:
+        return False
+    if check_equality:
+        return flashinfer_version >= PkgVersion(version)
+    return flashinver_version > PkgVersion(version)
+
+
 def ensure_divisibility(numerator, denominator):
     """Ensure that numerator is divisible by the denominator."""
     assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator)
@@ -497,17 +536,6 @@ def divide(numerator, denominator):
     return numerator // denominator
 
 
-def deprecate_inference_params(inference_context, inference_params):
-    """Print warning for deprecated `inference_params`."""
-    if inference_context is None and inference_params is not None:
-        warnings.warn(
-            "`inference_params` renamed to `inference_context`, and will be "
-            "removed in `megatron-core` 0.13."
-        )
-        return inference_params
-    return inference_context
-
-
 def get_tensor_model_parallel_group_if_none(tp_group, is_expert=False, check_initialized=True):
     """Issue a deprecation warning if tp_group is None and return the default tp group."""
     # TODO(zijiey): remove this function later.
@@ -2418,25 +2446,6 @@ async def wrapper(*args, **kwargs):
     return _decorate if func is None else _decorate(func)
 
 
-def get_mamba_inference_state_config_from_model(model) -> Optional["MambaInferenceStateConfig"]:
-    """Returns Mamba inference state config from the model if it is a hybrid model."""
-    from megatron.core.inference.contexts.attention_context.mamba_metadata import (
-        MambaInferenceStateConfig,
-    )
-    from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
-
-    decoder = get_attr_wrapped_model(model, "decoder")
-    layer_type_list = getattr(decoder, "layer_type_list", None)
-    if layer_type_list is not None and Symbols.MAMBA in layer_type_list:
-        (mamba_conv_states_shape, mamba_ssm_states_shape) = decoder.mamba_state_shapes_per_request()
-        return MambaInferenceStateConfig(
-            layer_type_list=layer_type_list,
-            mamba_conv_states_shape=mamba_conv_states_shape,
-            mamba_ssm_states_shape=mamba_ssm_states_shape,
-        )
-    return None
-
-
 # ============================================================================
 # Backward Compatibility Decorators
 # ============================================================================
@@ -2571,3 +2580,43 @@ class ExperimentalModel:
     """
     func._experimental_api = True
     return func
+
+
+def deprecate_args(
+    *deprecated_keys, message="Argument '{name}' has been deprecated and should not be used."
+):
+    """
+    Intercepts specific keyword arguments to raise a custom TypeError.
+
+    Args:
+        *deprecated_keys: Strings representing the argument names to block.
+        message: Custom error message string. Use {name} as a placeholder.
+    """
+
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            # Check if any deprecated key is present in kwargs
+            found_deprecated = set(deprecated_keys) & set(kwargs.keys())
+
+            if found_deprecated:
+                bad_key = list(found_deprecated)[0]
+                raise TypeError(message.format(name=bad_key))
+
+            # Send args to the real function
+            return func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+def deprecate_inference_params(inference_context, inference_params):
+    """Print warning for deprecated `inference_params`."""
+    if inference_context is None and inference_params is not None:
+        warnings.warn(
+            "`inference_params` renamed to `inference_context`, and will be "
+            "removed in `megatron-core` 0.13."
+        )
+        return inference_params
+    return inference_context
diff --git a/megatron/inference/__init__.py b/megatron/inference/__init__.py
new file mode 100644
index 00000000000..26496bfed70
--- /dev/null
+++ b/megatron/inference/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
diff --git a/megatron/inference/utils.py b/megatron/inference/utils.py
new file mode 100644
index 00000000000..b8a277fb9e1
--- /dev/null
+++ b/megatron/inference/utils.py
@@ -0,0 +1,327 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import logging
+from argparse import ArgumentParser
+from functools import partial
+from typing import Optional
+import torch
+
+from gpt_builders import gpt_builder
+from mamba_builders import mamba_builder
+from megatron.core.inference.config import (
+    InferenceConfig,
+    KVCacheManagementMode,
+    MambaInferenceStateConfig,
+)
+from megatron.core.inference.contexts import DynamicInferenceContext
+from megatron.core.inference.engines import DynamicInferenceEngine
+from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
+    GPTInferenceWrapper,
+)
+from megatron.core.inference.quantization.utils import quantize_model_to_mxfp8
+from megatron.core.inference.text_generation_controllers.text_generation_controller import (
+    TextGenerationController,
+)
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
+from megatron.core.transformer.module import MegatronModule
+from megatron.core.utils import get_attr_wrapped_model, log_single_rank, unwrap_model
+from megatron.training import get_args
+from megatron.training import get_model as _get_model
+from megatron.training import get_tokenizer, get_wandb_writer
+from megatron.training.checkpointing import load_checkpoint
+from model_provider import model_provider
+
+logger = logging.getLogger(__name__)
+
+
+def get_model_for_inference() -> MegatronModule:
+    """Initialize model and load checkpoint for inference."""
+
+    args = get_args()
+
+    if args.model_provider == "gpt":
+        model_builder = gpt_builder
+    elif args.model_provider == "mamba":
+        model_builder = mamba_builder
+    else:
+        raise ValueError(f"Invalid model provider {args.model_provider}")
+
+    # Build model.
+    model = _get_model(partial(model_provider, model_builder), wrap_with_ddp=False)
+
+    # Load checkpoint.
+    assert args.load is not None
+    args.exit_on_missing_checkpoint = True
+    load_checkpoint(
+        ddp_model=model,
+        optimizer=None,
+        opt_param_scheduler=None,
+        strict=not args.inference_ckpt_non_strict,
+    )
+
+    # No virtual PP.
+    assert len(model) == 1, "Above condition should have caught this"
+    model = model[0]
+
+    # Eval mode.
+    model.eval()
+
+    if args.transformer_impl == "inference_optimized" and args.fp8_recipe == "mxfp8":
+        quantize_model_to_mxfp8(unwrap_model(model))
+
+    return model
+
+
+def add_inference_args(parser: ArgumentParser) -> ArgumentParser:
+    """Add inference command line arguments to the parser."""
+
+    group = parser.add_argument_group(title='Inference')
+
+    group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.')
+    group.add_argument("--top_k", type=int, default=1, help='Top k sampling.')
+    group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.')
+    group.add_argument(
+        "--return-log-probs",
+        action='store_true',
+        default=False,
+        help='Return the log probabilities of the final output tokens',
+    )
+    group.add_argument(
+        "--prompts",
+        metavar='N',
+        type=str,
+        nargs='+',
+        help='Input prompts with each prompt within quotes and separated by space',
+    )
+    group.add_argument(
+        "--num-tokens-to-prompt",
+        type=int,
+        nargs="+",
+        default=[64, 1024],
+        help='Number of tokens to use for simulated prompts. This should be a '
+        'space-separated pair of integers, and the generated prompt lengths will '
+        'be uniformly sampled within this range.',
+    )
+    group.add_argument(
+        "--num-tokens-to-generate",
+        type=int,
+        default=30,
+        help='Number of tokens to generate for each prompt',
+    )
+    group.add_argument(
+        "--num-tokens-from-file",
+        action='store_true',
+        default=False,
+        help='Use per-prompt num_tokens_to_generate from prompt file',
+    )
+    group.add_argument(
+        "--top-n-logprobs",
+        type=int,
+        default=0,
+        help=(
+            "Return the top n logprobs for the generated tokens and their "
+            "corresponding token as a dictionary"
+        ),
+    )
+    group.add_argument(
+        "--incoming-requests-per-step",
+        type=int,
+        default=None,
+        help="Add a deterministic number of requests per step. This arg is "
+        "prioritized over `--incoming-requests-per-sec` below (which is non-"
+        "deterministic). Note that the number of requests added per step is "
+        "additionally limited by the inference context's `max_requests`, "
+        "`max_tokens`, and KV buffer size.",
+    )
+    group.add_argument(
+        "--incoming-requests-per-sec",
+        type=float,
+        default=100.0,
+        help="Simulated number of requests per second. Set to -1 to add all requests together.",
+    )
+    group.add_argument(
+        "--incoming-requests-duration",
+        type=float,
+        default=10.0,
+        help="Total amount of time to simulate that requests are "
+        "arriving. Multiply this value with "
+        "`--incoming-requests-per-sec` to get the approximate "
+        "total number of requests. Set to -1 to add all requests together.",
+    )
+    group.add_argument(
+        "--model-provider", choices=["mamba", "gpt"], default="gpt", help="Model provider"
+    )
+    group.add_argument(
+        "--skip-prompt-log-probs", action='store_true', default=False, help='Skip prompt log probs.'
+    )
+    group.add_argument(
+        "--stop-words",
+        metavar='WORD',
+        type=str,
+        nargs='+',
+        default=None,
+        help='Stop words to terminate generation. Each word should be quoted and '
+        'separated by space. Example: --stop-words "\\n\\n" "END" "###"',
+    )
+    group.add_argument(
+        "--output-path", type=str, default=None, help="Path to save generations as JSON"
+    )
+    group.add_argument(
+        "--output-every-n-results",
+        type=int,
+        default=1,
+        help="To minimize the output file size of larger runs, only write the "
+        "results of every `n` requests.",
+    )
+    group.add_argument(
+        "--prompt-file",
+        help='Jsonl file containing input prompts, where each item (i.e., line) '
+        'contains the field \'text\' where the value is the prompt. All other '
+        'fields within each item are ignored, and may be customized for each '
+        'application.',
+    )
+    group.add_argument(
+        "--prompt-file-num-truncate",
+        type=int,
+        help='Number of samples to use from the loaded prompt file (see '
+        '`--prompt-file` above). The first `--prompt-file-num-truncate` samples '
+        'will be used, in order.',
+    )
+    group.add_argument(
+        "--use-flashinfer-fused-rope",
+        action='store_true',
+        default=False,
+        help='Use flashinfer fused rope implementation.',
+    )
+    group.add_argument(
+        "--no-record-throughput",
+        action='store_false',
+        dest="record_throughput",
+        help="Disable throughput recording in --output-file",
+    )
+    group.add_argument(
+        "--inference-ckpt-non-strict",
+        action="store_true",
+        help="Load checkpoint with `strict=False`.",
+    )
+    group.add_argument(
+        "--termination-id",
+        type=int,
+        default=None,
+        help="Termination ID that overrides `tokenizer.eod`.",
+    )
+    group.add_argument(
+        "--suspend-resume-interval",
+        type=int,
+        default=None,
+        help="Suspend and resume the dynamic engine every "
+        "`suspend_resume_interval` steps. This is used to tet the suspend/resume "
+        "system.",
+    )
+    group.add_argument(
+        "--inference-repeat-n",
+        type=int,
+        default=1,
+        help="Repeat inference iterations N times for benchmarking.",
+    )
+    group.add_argument(
+        "--throughput-check-only",
+        action='store_true',
+        default=False,
+        help="If true, only run throughput check without verifying outputs.",
+    )
+
+    return parser
+
+
+def get_inference_config_from_model_and_args(model: MegatronModule, args):
+    """Returns a `InferenceConfig` constructed from the model and command line arguments."""
+
+    # Max sequence length.
+    position_embedding_type = get_attr_wrapped_model(model, "position_embedding_type")
+    model_max_seq_len = get_attr_wrapped_model(model, "max_sequence_length")
+    inf_max_seq_len = args.inference_max_seq_length
+    max_batch_size = args.inference_dynamic_batching_max_requests
+
+    if position_embedding_type == "learned_absolute":
+        # When using absolute position embeddings, it is critical that the
+        # context's `max_sequence_length` is less than or equal to the model's
+        # `max_sequence_length`. Otherwise, the context's `position_ids` will
+        # contain ids greater than the dimension of the position embedding
+        # tensor, which will result in an index error.
+        if inf_max_seq_len:
+            max_sequence_length = min(model_max_seq_len, inf_max_seq_len)
+        else:
+            max_sequence_length = model_max_seq_len
+        assert max_batch_size is None or max_batch_size <= model_max_seq_len
+    else:
+        max_sequence_length = inf_max_seq_len
+    if args.inference_dynamic_batching_max_requests is not None:
+        max_sequence_length = max(max_sequence_length, max_batch_size)
+
+    mamba_inference_state_config = MambaInferenceStateConfig.from_model(model)
+    pg_collection = get_attr_wrapped_model(model, "pg_collection")
+
+    # Get inference logging configuration from args
+    log_inference_wandb = args.inference_wandb_logging
+    inference_logging_step_interval = args.inference_logging_step_interval
+
+    # Get metrics writer if logging is enabled and on the logging rank
+    # Use the same rank convention as training (last rank logs)
+    metrics_writer = None
+    if (
+        inference_logging_step_interval > 0
+        and log_inference_wandb
+        and args.rank == (args.world_size - 1)
+    ):
+        metrics_writer = get_wandb_writer()
+        if metrics_writer is None:
+            log_single_rank(
+                logger,
+                logging.WARNING,
+                "WARNING: --rl-inference-logging-step-interval is set but no metrics writer "
+                "wandb module is available. Inference logging will be disabled.",
+            )
+
+    return InferenceConfig(
+        block_size_tokens=args.inference_dynamic_batching_block_size,
+        buffer_size_gb=args.inference_dynamic_batching_buffer_size_gb,
+        paused_buffer_size_gb=args.inference_dynamic_batching_paused_buffer_size_gb,
+        mamba_memory_ratio=args.inference_dynamic_batching_mamba_memory_ratio,
+        num_cuda_graphs=(
+            args.inference_dynamic_batching_num_cuda_graphs
+            if args.cuda_graph_impl == "local"
+            else None
+        ),
+        max_requests=args.inference_dynamic_batching_max_requests,
+        max_tokens=args.inference_dynamic_batching_max_tokens,
+        unified_memory_level=args.inference_dynamic_batching_unified_memory_level,
+        kv_cache_management_mode=KVCacheManagementMode(args.rl_kv_cache_management_mode),
+        cuda_graph_mixed_prefill_count=args.inference_dynamic_batching_cuda_graph_mixed_prefill_count,  # pylint: disable=line-too-long
+        use_cuda_graphs_for_non_decode_steps=not args.decode_only_cuda_graphs,
+        static_kv_memory_pointers=args.rl_persist_cuda_graphs,
+        max_sequence_length=max_sequence_length,
+        mamba_inference_state_config=mamba_inference_state_config,
+        pg_collection=pg_collection,
+        use_flashinfer_fused_rope=args.use_flashinfer_fused_rope,
+        materialize_only_last_token_logits=not args.return_log_probs,
+        track_paused_request_events=args.inference_dynamic_batching_track_paused_request_events,
+        enable_chunked_prefill=args.enable_chunked_prefill,
+        metrics_writer=metrics_writer,
+        logging_step_interval=args.inference_logging_step_interval,
+    )
+
+
+def get_dynamic_inference_engine(model: Optional[MegatronModule] = None) -> DynamicInferenceEngine:
+    """Builds a `DynamicInferenceEngine`."""
+    args = get_args()
+    if model is None:
+        model = get_model_for_inference()
+    tokenizer = build_tokenizer(args)
+
+    inference_config = get_inference_config_from_model_and_args(model, args)
+    context = DynamicInferenceContext(model.config, inference_config)
+    inference_wrapped_model = GPTInferenceWrapper(model, context)
+    controller = TextGenerationController(inference_wrapped_model, tokenizer)
+    engine = DynamicInferenceEngine(controller, context)
+    return engine
diff --git a/megatron/legacy/model/utils.py b/megatron/legacy/model/utils.py
index 5762000d5df..1ea05bb96fd 100644
--- a/megatron/legacy/model/utils.py
+++ b/megatron/legacy/model/utils.py
@@ -69,7 +69,7 @@ def get_norm(config):
             sequence_parallel=config.sequence_parallel,
             apply_layernorm_1p=args.apply_layernorm_1p)
     elif args.normalization == "RMSNorm":
-        if args.apply_layernorm_1p:
+        if "apply_layernorm_1p" in args and args.apply_layernorm_1p:
             raise NotImplementedError('RMSNorm does not currently support the layernorm_1p formulation.')
 
         return RMSNorm(dim=config.hidden_size,
diff --git a/megatron/legacy/model/vision/esvit_swin_backbone.py b/megatron/legacy/model/vision/esvit_swin_backbone.py
index 87932040cb7..c7c67b40c64 100644
--- a/megatron/legacy/model/vision/esvit_swin_backbone.py
+++ b/megatron/legacy/model/vision/esvit_swin_backbone.py
@@ -726,7 +726,7 @@ def init_weights(self, pretrained='', pretrained_layers=[], verbose=True):
             for k, v in pretrained_dict.items():
                 need_init = (
                         k.split('.')[0] in pretrained_layers
-                        or pretrained_layers[0] is '*'
+                        or pretrained_layers[0] == '*'
                         or 'relative_position_index' not in k
                         or 'attn_mask' not in k
                 )
@@ -785,7 +785,7 @@ def freeze_pretrained_layers(self, frozen_layers=[]):
             if (
                     name.split('.')[0] in frozen_layers
                     or '.'.join(name.split('.')[0:2]) in frozen_layers
-                    or (len(frozen_layers) > 0 and frozen_layers[0] is '*')
+                    or (len(frozen_layers) > 0 and frozen_layers[0] == '*')
             ):
                 for _name, param in module.named_parameters():
                     param.requires_grad = False
@@ -796,7 +796,7 @@ def freeze_pretrained_layers(self, frozen_layers=[]):
         for name, param in self.named_parameters():
             if (
                     name.split('.')[0] in frozen_layers
-                    or (len(frozen_layers) > 0 and frozen_layers[0] is '*')
+                    or (len(frozen_layers) > 0 and frozen_layers[0] == '*')
                     and param.requires_grad is True
             ):
                 param.requires_grad = False
diff --git a/megatron/post_training/arguments.py b/megatron/post_training/arguments.py
index 1e988680142..dc98c6d28e4 100644
--- a/megatron/post_training/arguments.py
+++ b/megatron/post_training/arguments.py
@@ -21,7 +21,17 @@ def add_modelopt_args(parser):
     group.add_argument(
         "--export-te-mcore-model",
         action="store_true",
-        help="Export a megatron-core transformer-engine checkpoint.",
+        help="Indicate the source checkpoint uses the fused Transformer-Engine mcore layer spec "
+        "(where layernorms are fused into linear layers). Enables state_dict key remapping so the "
+        "TE checkpoint can be loaded into the local ModelOpt spec for PTQ/export, and saved back "
+        "in TE-compatible format. Mutually exclusive with --export-default-te-spec.",
+    )
+    group.add_argument(
+        "--export-default-te-spec",
+        action="store_true",
+        help="Use the full Transformer-Engine layer spec for model building. "
+        "This builds the model with TELayerNormColumnParallelLinear, TERowParallelLinear, "
+        "TEGroupedMLP, TEDotProductAttention, etc., matching the canonical TE specs.",
     )
     group.add_argument(
         "--export-force-local-attention",
diff --git a/megatron/post_training/generate.py b/megatron/post_training/generate.py
index 2a124734a30..c4bb07c353f 100644
--- a/megatron/post_training/generate.py
+++ b/megatron/post_training/generate.py
@@ -19,6 +19,7 @@ def simple_generate(
     osl: int = 32,
     eos_token_id: List[int] = [],
     disable_tqdm: bool = False,
+    calibration_mode: bool = False,
 ):
     """A simple generate function without using KV-cache."""
     model.eval()
@@ -64,20 +65,23 @@ def _forward_step_func(data, model):
         else:
             tokens = input_ids
 
-        list_of_logits = get_forward_backward_func()(
+        logits_and_extras = get_forward_backward_func()(
             forward_step_func=_forward_step_func,
             data_iterator=[{"tokens": tokens}],
             model=model,
             num_microbatches=1,
             seq_length=tokens.shape[-1],
-            micro_batch_size=1,
+            micro_batch_size=tokens.shape[0],
             decoder_seq_length=tokens.shape[-1],
             forward_only=True,
             collect_non_loss_data=True,
         )
 
+        if calibration_mode:
+            continue  # avoid unnecessary computation
+
         if mpu.is_pipeline_last_stage():
-            logits = gather_from_tensor_model_parallel_region(list_of_logits[0])
+            logits = gather_from_tensor_model_parallel_region(logits_and_extras[0])
             eager_ids = logits[:, input_ids.shape[-1] - 1, :].argmax(dim=-1, keepdim=True).detach()
         else:
             eager_ids = None
diff --git a/megatron/post_training/model_builder.py b/megatron/post_training/model_builder.py
index fea837c96c3..7d38041a7a3 100644
--- a/megatron/post_training/model_builder.py
+++ b/megatron/post_training/model_builder.py
@@ -2,6 +2,7 @@
 
 """ModelOpt GPT model provider."""
 
+import logging
 import os
 from argparse import Namespace
 from typing import Any, Dict
@@ -135,7 +136,7 @@ def _load_teacher_model(config, config_raw: Namespace, model_kwargs: Dict[str, A
                 local_core_attention=False if config.context_parallel_size > 1 else args.export_force_local_attention,
                 remap_te_layernorm=args.export_te_mcore_model,
                 real_quant_cfg=args.export_real_quant_cfg,
-                use_arbitrary_attention_mask=False if config.context_parallel_size > 1 else True,
+                use_arbitrary_attention_mask=False,
             )
         teacher = MCoreGPTModel(config=config, **model_kwargs)
     _add_load_convert_hooks(teacher)
@@ -226,21 +227,18 @@ def modelopt_gpt_mamba_builder(
                 use_te=args.transformer_impl == "transformer_engine",
             )
         else:
-            local_core_attention=args.export_force_local_attention
             if config.context_parallel_size > 1:
                 print_rank_0("context_parallel_size > 1! Force using TEDotProductAttention!")
                 local_core_attention=False
-                print_rank_0("context_parallel_size > 1! Force attention_mask_type to Causal. This can be wrong for EAGLE training!")
-                use_arbitrary_attention_mask = False
             else:
-                use_arbitrary_attention_mask = True
+                local_core_attention=args.export_force_local_attention
 
             transformer_layer_spec = get_gpt_modelopt_spec(
                 config=config,
                 local_core_attention=local_core_attention,
                 remap_te_layernorm=args.export_te_mcore_model,
                 real_quant_cfg=args.export_real_quant_cfg,
-                use_arbitrary_attention_mask=use_arbitrary_attention_mask,
+                use_arbitrary_attention_mask=False,
             )
 
         model_kwargs = {
@@ -262,8 +260,16 @@ def modelopt_gpt_mamba_builder(
     elif args.export_model_type == "MambaModel" or args.is_hybrid_model:
         from megatron.core.post_training.modelopt.mamba.model_specs import get_mamba_stack_modelopt_spec
 
+        if args.export_default_te_spec and args.export_te_mcore_model:
+            logging.getLogger(__name__).warning(
+                "--export-default-te-spec and --export-te-mcore-model are mutually exclusive. "
+                "Since --export-default-te-spec is given, --export-te-mcore-model will be disabled."
+            )
+            args.export_te_mcore_model = False
+
         mamba_stack_spec = get_mamba_stack_modelopt_spec(
-            remap_te_layernorm=args.export_te_mcore_model
+            remap_te_layernorm=args.export_te_mcore_model,
+            use_default_te_spec=args.export_default_te_spec,
         )
         model_kwargs = {
             "mamba_stack_spec": mamba_stack_spec,
@@ -317,6 +323,9 @@ def modelopt_gpt_mamba_builder(
         assert (
             not args.tp_comm_overlap
         ), "ModelOpt Distillation currently incompatible with `--tp-comm-overlap` option."
+        assert (
+            args.cross_entropy_fusion_impl != "te"
+        ), "ModelOpt Distillation currently incompatible with TransformerEngine Cross-Entropy implementation."
         if args.pipeline_model_parallel_size > 1:
             assert (
                 args.virtual_pipeline_model_parallel_size is None
diff --git a/megatron/rl/agent/api.py b/megatron/rl/agent/api.py
index 34efa68d85a..dd52c7277bf 100644
--- a/megatron/rl/agent/api.py
+++ b/megatron/rl/agent/api.py
@@ -12,10 +12,7 @@
 
 from ..__init__ import Request, TypeLookupable
 from ..inference import (
-    ChatInferenceInterface,
-    ChatInferenceRequest,
     InferenceInterface,
-    InferenceRequest,
     LLMChatMessage,
     ReturnsRaw,
 )
@@ -46,29 +43,29 @@ class GroupedRolloutRequest(Request):
 class Rollout(AgentBaseModel):
     """Data for language-based Rollout."""
 
-    trajectory: str
-    prompt_length: int | None = None
+    trajectory: list[str]
+    prompt_length: list[int] | None = None
     reward: float = None
-    env_id: str | None = None
+    env_id: str = ''
     problem_id: str | None = None
 
 
 class TokenRollout(AgentBaseModel):
     """Tokenized representation of a language-based Rollout."""
 
-    trajectory: list[int]
+    trajectory: list[list[int]]
     reward: list[float] | float
-    generation_mask: list[list[int]] | list[bool] | None = None
-    logprobs: list[float] | None = None
-    env_id: str | None = None
+    generation_mask: list[list[bool]] | None = None
+    logprobs: list[list[float]] | None = None
+    env_id: str = ''
     problem_id: str | None = None
 
 
 class ContrastiveRollout(AgentBaseModel):
     """Contrastive/Preference data for language-based Rollout."""
 
-    chosen_trajectory: str
-    rejected_trajectory: str
+    chosen_trajectory: list[str]
+    rejected_trajectory: list[str]
 
 
 class Head2HeadRolloutRequest(Request):
@@ -102,7 +99,7 @@ class RewardEvaluationResult(EvaluationResult):
 
 
 class EvaluationResponse(AgentBaseModel, TypeLookupable, Generic[T]):
-    env_id: str | None = None
+    env_id: str
     results: list[T]
 
     def metrics(self):
@@ -124,11 +121,6 @@ async def get_reward_rollouts(self, request: RolloutRequest) -> list[Rollout]:
             request.inference_interface, ReturnsRaw
         ), "InferenceInterface must support raw_text return to provide rollouts."
 
-        if isinstance(request.inference_interface, ChatInferenceInterface):
-            self.chat_mode = True
-        else:
-            self.chat_mode = False
-
         return await asyncio.gather(
             *[self.rollout(request=request) for _ in range(request.num_rollouts)]
         )
@@ -158,11 +150,6 @@ async def get_reward_rollouts(self, request: RolloutRequest) -> list[TokenRollou
             request.inference_interface, ReturnsRaw
         ), "InferenceInterface must support raw_text return to provide rollouts."
 
-        if isinstance(request.inference_interface, ChatInferenceInterface):
-            self.chat_mode = True
-        else:
-            self.chat_mode = False
-
         return await asyncio.gather(
             *[self.rollout(request=request) for _ in range(request.num_rollouts)]
         )
@@ -187,11 +174,6 @@ async def get_grouped_rollouts(self, request: GroupedRolloutRequest):
             request.inference_interface, ReturnsRaw
         ), "InferenceInterface must support raw_text return to provide rollouts."
 
-        if isinstance(request.inference_interface, ChatInferenceInterface):
-            self.chat_mode = True
-        else:
-            self.chat_mode = False
-
         # If num_groups is -1, we generate a stream of groups.
         # The buffer size is used to create backpressure for each agent in order to balance group generation in a multi-task setting.
         grouped_rollouts: asyncio.Queue[list[Rollout]] = asyncio.Queue(
diff --git a/megatron/rl/agent/pass_at_evaluation_agent.py b/megatron/rl/agent/pass_at_evaluation_agent.py
index b10e3b897c8..c04e1c2772f 100644
--- a/megatron/rl/agent/pass_at_evaluation_agent.py
+++ b/megatron/rl/agent/pass_at_evaluation_agent.py
@@ -7,7 +7,7 @@
 import numpy as np
 
 from ..__init__ import GenericGenerationArgs
-from ..inference import ChatInferenceResponse, LLMChatMessage
+from ..inference import LLMChatMessage
 from .api import EvaluationAgent, EvaluationRequest, EvaluationResponse, RewardEvaluationResult
 
 
diff --git a/megatron/rl/agent/reward_only_agent.py b/megatron/rl/agent/reward_only_agent.py
index 2e81674c74d..4099406a98a 100644
--- a/megatron/rl/agent/reward_only_agent.py
+++ b/megatron/rl/agent/reward_only_agent.py
@@ -7,8 +7,6 @@
 from tqdm.asyncio import tqdm
 
 from ..inference import (
-    ChatInferenceInterface,
-    ChatInferenceResponse,
     InferenceResponse,
     LLMChatMessage,
     ReturnsRaw,
@@ -91,11 +89,7 @@ async def rollout_from_response(
         ), "InferenceInterface must support raw_text return to provide rollouts."
         raw_text = response.raw_text
 
-        response_text = (
-            response.response.content
-            if isinstance(response, ChatInferenceResponse)
-            else response.response
-        )
+        response_text = response.response.content
 
         if isinstance(request.inference_interface, ReturnsTokens):
             logprobs = response.logprobs
@@ -104,16 +98,16 @@ async def rollout_from_response(
                 for x in range(len(response.token_ids))
             ]
             rollout = TokenRollout(
-                trajectory=response.token_ids,
+                trajectory=[response.token_ids],
                 reward=await self.get_reward(response_text, golden),
-                logprobs=logprobs,
-                generation_mask=generation_mask,
+                logprobs=[logprobs],
+                generation_mask=[generation_mask],
                 env_id=self.env_id,
                 problem_id=golden['problem_id'] if 'problem_id' in golden else None,
             )
         else:
             rollout = Rollout(
-                trajectory=raw_text,
+                trajectory=[raw_text],
                 reward=await self.get_reward(response_text, golden),
                 env_id=self.env_id,
                 problem_id=golden['problem_id'] if 'problem_id' in golden else None,
@@ -126,14 +120,10 @@ async def rollout(self, request: RolloutRequest) -> Rollout:
         prompt, golden = await self.get_prompt(validation=request.validation)
 
         inference_request = request.inference_interface.prepare_request(
-            [prompt], request.generation_args
+            prompt, request.generation_args
         )
 
-        responses = await request.inference_interface.agenerate(inference_request)
-        assert (
-            len(responses) == 1
-        ), "get_reward_rollouts only requested a single response but got multiple responses"
-        response = responses[0]
+        response = await request.inference_interface.agenerate(inference_request)
 
         return await self.rollout_from_response(request, response, golden)
 
@@ -142,41 +132,22 @@ async def group_rollout(self, request: GroupedRolloutRequest) -> list[Rollout]:
         prompt, golden = await self.get_prompt(validation=request.validation)
 
         inference_request = request.inference_interface.prepare_request(
-            [prompt], request.generation_args
-        )
-        inference_request.n = request.rollouts_per_group
-
-        groups = await request.inference_interface.agenerate(inference_request)
-        assert (
-            len(groups) == 1
-        ), "get_grouped_rollouts only requested a single group but got multiple groups"
-        responses = groups[0].responses
-
-        rollouts = await asyncio.gather(
-            *[self.rollout_from_response(request, response, golden) for response in responses]
+            prompt, request.generation_args
         )
 
-        return rollouts
+        responses = await asyncio.gather(*[request.inference_interface.agenerate(inference_request) for _ in range(request.rollouts_per_group)])
+        return [await self.rollout_from_response(request, response, golden) for response in responses]
 
     async def _evaluation(
         self, prompt: str, golden: Any, request: EvaluationRequest
     ) -> RewardOnlyEvaluationResponse:
 
         inference_request = request.inference_interface.prepare_request(
-            [prompt], request.generation_args
+            prompt, request.generation_args
         )
 
-        responses = await request.inference_interface.agenerate(inference_request)
-        assert (
-            len(responses) == 1
-        ), "evaluation only requested a single response but got multiple responses"
-        response = responses[0]
-
-        response_text = (
-            response.response.content
-            if isinstance(response, ChatInferenceResponse)
-            else response.response
-        )
+        response = await request.inference_interface.agenerate(inference_request)
+        response_text = response.response.content
 
         result = RewardEvaluationResult(
             env_id=self.env_id,
@@ -190,11 +161,6 @@ async def _evaluation(
 
     async def run_evaluation(self, request: EvaluationRequest):
 
-        if isinstance(request.inference_interface, ChatInferenceInterface):
-            self.chat_mode = True
-        else:
-            self.chat_mode = False
-
         # Get all prompts first
         all_prompts = list(
             await self.evaluation_prompts(
diff --git a/megatron/rl/inference/api.py b/megatron/rl/inference/api.py
index ae19380842e..87f6b87f908 100644
--- a/megatron/rl/inference/api.py
+++ b/megatron/rl/inference/api.py
@@ -11,42 +11,15 @@ class LLMChatMessage(BaseModel):
 
 
 class InferenceRequest(Request):
-    prompt: list[str]
-    n: int | None = None
-
-
-class ChatInferenceRequest(InferenceRequest):
-    prompt: list[list[LLMChatMessage]]
+    prompt: list[LLMChatMessage]
     tools: list[dict] | None = None
 
 
-class GroupedInferenceRequest(InferenceRequest):
-    group_size: int = 1
-
-
 class InferenceResponse(BaseModel):
     """The minimum required response for an inference interface."""
 
-    response: str
+    response: LLMChatMessage
     raw_text: str | None = None
     token_ids: list[int] | None = None
     prompt_length: int | None = None
     logprobs: list[float] | None = None
-
-
-class GroupedInferenceResponse(BaseModel):
-    """An inference response which includes a list of responses."""
-
-    responses: list[InferenceResponse]
-
-
-class ChatInferenceResponse(InferenceResponse):
-    """The minimum required response for a chat inference interface."""
-
-    response: LLMChatMessage
-
-
-class GroupedChatInferenceResponse(GroupedInferenceResponse):
-    """A chat inference response which includes a list of responses."""
-
-    responses: list[ChatInferenceResponse]
diff --git a/megatron/rl/inference/chat_templates.py b/megatron/rl/inference/chat_templates.py
deleted file mode 100644
index 3e464842859..00000000000
--- a/megatron/rl/inference/chat_templates.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-
-import warnings
-
-from pydantic import BaseModel, ConfigDict, Field
-from transformers import AutoTokenizer
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-
-from .api import InferenceResponse, LLMChatMessage
-
-
-class ConversationTemplate(BaseModel):
-    """Transformers tokenizer based template."""
-
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast = Field(repr=False)
-    stop_words: list[str] = []
-
-    def format(self, messages: list[LLMChatMessage], tools: list[dict] | None = None) -> str:
-        return self.tokenizer.apply_chat_template(
-            messages, add_generation_prompt=True, tokenize=False, tools=tools
-        )
-
-    def parse_response(self, responses: list[InferenceResponse]) -> list[LLMChatMessage]:
-        return [
-            LLMChatMessage(role="assistant", content=response.response) for response in responses
-        ]
-
-    @classmethod
-    def from_string(cls, tokenizer_name: str) -> 'ConversationTemplate':
-        if tokenizer_name == "null":
-            warnings.warn(
-                "Using NullConversationTemplate. This provides no chat templating to Chat requests."
-            )
-            return NullConversationTemplate()
-        return cls(tokenizer=AutoTokenizer.from_pretrained(tokenizer_name))
-
-
-class NullConversationTemplate(ConversationTemplate):
-
-    tokenizer: None = None
-
-    def format(self, messages: list[LLMChatMessage], tools: list[dict] | None = None) -> str:
-        return "\n".join([f"{message.content}" for message in messages]) + "\n"
-
-    def parse_response(self, responses: list[InferenceResponse]) -> list[LLMChatMessage]:
-        return [
-            LLMChatMessage(role="assistant", content=response.response) for response in responses
-        ]
diff --git a/megatron/rl/inference/inference_interface.py b/megatron/rl/inference/inference_interface.py
index e950792e72b..715f5ada9d4 100644
--- a/megatron/rl/inference/inference_interface.py
+++ b/megatron/rl/inference/inference_interface.py
@@ -1,83 +1,40 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import asyncio
-from abc import abstractmethod
-from itertools import zip_longest
-from typing import Annotated, Any, ClassVar
 
-from pydantic import BaseModel, BeforeValidator, ValidationError
+from pydantic import BaseModel
 
 from ..__init__ import GenericGenerationArgs
 from ..inference.api import (
-    ChatInferenceRequest,
-    ChatInferenceResponse,
-    GroupedChatInferenceResponse,
-    GroupedInferenceResponse,
     InferenceRequest,
     InferenceResponse,
     LLMChatMessage,
 )
-from ..inference.chat_templates import ConversationTemplate
-
-
-# Used when generating n resposnes for a single prompt
-def grouper(iterable, n, fillvalue=None):
-    """Fold an iterable into a list of lists of size n."""
-    args = [iter(iterable)] * n
-    return zip_longest(*args, fillvalue=fillvalue)
 
 
 class InferenceInterface(BaseModel):
-    """Inference interface that for base language models."""
+    """Inference interface for chat models."""
 
     class Config:
         arbitrary_types_allowed = True
 
-    supports_n: ClassVar[bool] = False
-
     def prepare_request(
-        self, prompts: list[str], generation_args: GenericGenerationArgs
+        self, prompt: str | list[LLMChatMessage], generation_args: GenericGenerationArgs
     ) -> InferenceRequest:
-        assert all(isinstance(p, str) for p in prompts), "Prompt must be a list of strings"
-        return InferenceRequest(prompt=prompts, generation_args=generation_args)
-
-    async def base_generate(self, request: InferenceRequest) -> list[InferenceResponse]:
-        raise NotImplementedError(
-            "Direct Inference Classes must implement the base_generate method."
-        )
-
-    def duplicate_requests(self, request: InferenceRequest, n: int) -> list[InferenceRequest]:
-        return request.model_copy(update={'prompt': request.prompt * n})
+        prompt = [LLMChatMessage(role='user', content=prompt)] if isinstance(prompt, str) else prompt
+        return InferenceRequest(prompt=prompt, generation_args=generation_args)
 
-    def fold_responses(
-        self, responses: list[InferenceResponse], n: int
-    ) -> list[GroupedInferenceResponse]:
-        return [GroupedInferenceResponse(responses=x) for x in list(grouper(responses, n))]
+    async def base_generate(self, request: InferenceRequest) -> InferenceResponse:
+        assert NotImplementedError("Direct Inference Classes must implement the base_generate method.")
 
     async def agenerate(
         self, request: InferenceRequest
-    ) -> list[InferenceResponse] | list[GroupedInferenceResponse]:
-        if not self.supports_n and request.n is not None:
-            request = self.duplicate_requests(request, request.n)
-
-        generations = await self.base_generate(request)
-
-        if request.n is not None:
-            if self.supports_n:
-                assert (
-                    len(generations) == len(request.prompt) * request.n
-                ), f"Number of generations ({len(generations)}) does not match number of prompts ({len(request.prompt)} * {request.n})."
-            else:
-                assert len(generations) == len(
-                    request.prompt
-                ), f"Number of generations ({len(generations)}) does not match number of prompts ({len(request.prompt)})."
-            generations = self.fold_responses(generations, request.n)
-
-        return generations
+    ) -> InferenceResponse:
+        return await self.base_generate(request)
 
     def generate(
         self, request: InferenceRequest
-    ) -> list[InferenceResponse] | list[GroupedInferenceResponse]:
+    ) -> InferenceResponse:
         try:
             loop = asyncio.get_running_loop()
         except RuntimeError:
@@ -85,58 +42,6 @@ def generate(
         else:
             return loop.run_until_complete(self.agenerate(request))
 
-
-def ensure_template(value: Any) -> ConversationTemplate:
-    if isinstance(value, ConversationTemplate):
-        return value
-    elif isinstance(value, str):
-        return ConversationTemplate.from_string(value)
-    else:
-        raise ValueError(f"Invalid conversation template: {value}")
-
-
-class ChatInferenceInterface(InferenceInterface):
-    """Inference interface for chat models."""
-
-    conversation_template: Annotated[ConversationTemplate, BeforeValidator(ensure_template)]
-
-    def prepare_request(
-        self, prompts: list[str | list[LLMChatMessage]], generation_args: GenericGenerationArgs
-    ) -> ChatInferenceRequest:
-        prompt = [
-            [LLMChatMessage(role='user', content=p)] if isinstance(p, str) else p for p in prompts
-        ]
-        return ChatInferenceRequest(prompt=prompt, generation_args=generation_args)
-
-    async def base_generate(self, request: ChatInferenceRequest) -> list[ChatInferenceResponse]:
-        base_generate_results = await super().base_generate(
-            InferenceRequest(
-                prompt=[
-                    self.conversation_template.format(messages, request.tools)
-                    for messages in request.prompt
-                ],
-                generation_args=request.generation_args,
-            )
-        )
-        chat_message_results = self.conversation_template.parse_response(base_generate_results)
-        return [
-            ChatInferenceResponse(
-                response=chat_message, **response.model_dump(exclude={'response'})
-            )
-            for chat_message, response in zip(chat_message_results, base_generate_results)
-        ]
-
-    def generate(
-        self, request: ChatInferenceRequest
-    ) -> list[ChatInferenceResponse] | list[GroupedChatInferenceResponse]:
-        return super().generate(request)
-
-    async def agenerate(
-        self, request: ChatInferenceRequest
-    ) -> list[ChatInferenceResponse] | list[GroupedChatInferenceResponse]:
-        return await super().agenerate(request)
-
-
 class ReturnsRaw(InferenceInterface):
     """Mix-In for interface that supports returning complete string fed to the LLM."""
 
diff --git a/megatron/rl/inference/megatron.py b/megatron/rl/inference/megatron.py
index 4e9364b3ae9..ee31a16eeaa 100644
--- a/megatron/rl/inference/megatron.py
+++ b/megatron/rl/inference/megatron.py
@@ -2,42 +2,18 @@
 
 import asyncio
 import logging
-from argparse import Namespace
 
 import torch.distributed as dist
 from pydantic import PrivateAttr
 
-from megatron.core import parallel_state
-from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
-from megatron.core.inference.engines.abstract_engine import AbstractEngine
+from megatron.core.inference.config import KVCacheManagementMode
 from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine
-from megatron.core.inference.engines.mcore_engine import MCoreEngine
 from megatron.core.inference.inference_client import InferenceClient
-from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
-    GPTInferenceWrapper,
-)
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
-from megatron.core.inference.sampling_params import SamplingParams
-from megatron.core.inference.text_generation_controllers.simple_text_generation_controller import (
-    SimpleTextGenerationController,
-)
 from megatron.core.models.gpt.gpt_model import GPTModel
-from megatron.core.pipeline_parallel.utils import is_pp_first_stage, is_pp_last_stage
-from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
-from megatron.core.transformer.module import MegatronModule
-from megatron.core.utils import (
-    get_attr_wrapped_model,
-    get_mamba_inference_state_config_from_model,
-    get_pg_size,
-    log_single_rank,
-)
-from megatron.training import get_wandb_writer
+from megatron.core.utils import log_single_rank
 from megatron.training.global_vars import get_args, get_tokenizer
 
 from ..inference.inference_interface import (
-    ChatInferenceInterface,
     InferenceRequest,
     InferenceResponse,
     LLMChatMessage,
@@ -49,203 +25,58 @@
 logger = logging.getLogger(__name__)
 
 
-## This code is copied from tools/run_text_generation_server.py
-def get_static_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngine:
-    """Get the relevant backend for running inference.
-
-    This function will automatically choose the TRTLLMBackend when possible,
-    and default to Mcore backend if the user does not specify any backends.
-    TRTLLMBackend is not implmented yet.
-
-    Args:
-        args (Namespace): The user arguments parsed from command line
-        model (MegatronModule): The megatron model.
-
-    Returns:
-        AbstractBackend: The chosen backend
-    """
-    tokenizer = get_tokenizer()
-
-    inference_wrapper_config = InferenceWrapperConfig(
-        hidden_size=args.hidden_size,
-        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
-        fp32_residual_connection=args.fp32_residual_connection,
-        params_dtype=args.params_dtype,
-        padded_vocab_size=args.padded_vocab_size,
-        inference_max_seq_length=args.inference_max_seq_length,
-        inference_max_requests=(
-            args.inference_max_batch_size if args.inference_max_batch_size is not None else 1
-        ),
-        nccl_all_reduce_for_prefill=args.nccl_all_reduce_for_prefill,
-    )
-
-    inference_wrapped_model = GPTInferenceWrapper(model, inference_wrapper_config)
-    pg_collection = get_attr_wrapped_model(model, "pg_collection")
-    pp_group = pg_collection.pp
-    text_generation_controller = SimpleTextGenerationController(
-        inference_wrapped_model=inference_wrapped_model,
-        tokenizer=tokenizer,
-        pp_group=pp_group,
-    )
-    return MCoreEngine(
-        text_generation_controller=text_generation_controller,
-        max_batch_size=(
-            args.inference_max_batch_size if args.inference_max_batch_size is not None else 1
-        ),
-    )
-
-
-## This code is copied from tools/run_text_generation_server.py
-def get_dynamic_inference_engine(
-    args: Namespace,
-    model: MegatronModule,
-    inference_logging_step_interval: int = 0,
-    metrics_writer = None
-) -> AbstractEngine:
-    """Get the relevant backend for running inference.
-
-    This function will automatically choose the TRTLLMBackend when possible,
-    and default to Mcore backend if the user does not specify any backends.
-    TRTLLMBackend is not implmented yet.
-
-    Args:
-        args (Namespace): The user arguments parsed from command line
-        model (MegatronModule): The megatron model.
-        inference_logging_step_interval (int): Step interval for logging inference metrics.
-        metrics_writer: Metrics writer (wandb module) for logging.
-
-    Returns:
-        AbstractBackend: The chosen backend
-    """
-    tokenizer = get_tokenizer()
-
-    enable_cuda_graph = args.cuda_graph_impl == "local"
-
-    mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
-
-    # DynamicInferenceContext must use the inference model's TP / PP size, not the
-    # training TP / PP size from global args. The inference model may have a custom
-    # ProcessGroupCollection with a different TP / PP size.
-    pg_collection = get_attr_wrapped_model(model, "pg_collection")
-    tp_group = getattr(pg_collection, 'tp', None) if pg_collection is not None else None
-    if tp_group is not None:
-        inference_tp_size = get_pg_size(tp_group)
-    else:
-        inference_tp_size = args.tensor_model_parallel_size
-    pp_group = getattr(pg_collection, 'pp', None) if pg_collection is not None else None
-    if pp_group is not None:
-        inference_pp_size = get_pg_size(pp_group)
-    else:
-        inference_pp_size = args.pipeline_model_parallel_size
-
-    # Inference context.
-    inference_context = DynamicInferenceContext(
-        params_dtype=args.params_dtype,
-        num_layers=args.num_layers // inference_pp_size,
-        kv_channels=args.kv_channels,
-        num_attention_heads=(
-            args.num_query_groups if args.group_query_attention else args.num_attention_heads
-        ),
-        max_sequence_length=args.inference_max_seq_length,
-        num_cuda_graphs=(
-            args.inference_dynamic_batching_num_cuda_graphs if enable_cuda_graph else None
-        ),
-        block_size_tokens=args.inference_dynamic_batching_block_size,
-        buffer_size_gb=args.inference_dynamic_batching_buffer_size_gb,
-        max_requests=args.inference_dynamic_batching_max_requests,
-        max_tokens=args.inference_dynamic_batching_max_tokens,
-        pg_collection=pg_collection,  # TP/PP sizes are derived from the model's pg_collection.
-        materialize_only_last_token_logits=True,
-        mamba_inference_state_config=mamba_inference_state_config,
-        cache_mla_latent=args.multi_latent_attention and args.cache_mla_latents,
-        kv_lora_rank=args.kv_lora_rank if args.multi_latent_attention else None,
-        qk_pos_emb_head_dim=args.qk_pos_emb_head_dim,
-        use_cuda_graphs_for_non_decode_steps=not args.decode_only_cuda_graphs,
-        use_flashinfer_fused_rope=None,
-        unified_memory_level=args.inference_dynamic_batching_unified_memory_level,
-        cuda_graph_max_tokens=args.inference_dynamic_batching_cuda_graph_max_tokens,
-        cuda_graph_mixed_prefill_count=args.inference_dynamic_batching_cuda_graph_mixed_prefill_count,
-        metrics_writer=metrics_writer,
-        persist_cuda_graphs=args.rl_training_cuda_graphs,
-        offload_kv_cache=args.rl_offload_kv_cache_during_training
-    )
-
-    inference_wrapped_model = GPTInferenceWrapper(model, args, inference_context, pg_collection=pg_collection)
-
-    inference_wrapped_model.model_is_pipeline_parallel = not (
-        is_pp_first_stage(pg_collection.pp) and is_pp_last_stage(pg_collection.pp)
-    )
-
-    pp_group = getattr(pg_collection, "pp", None)
-    text_generation_controller = SimpleTextGenerationController(
-        inference_wrapped_model=inference_wrapped_model,
-        tokenizer=tokenizer,
-        pp_group=pp_group,
-    )
-
-    return DynamicInferenceEngine(
-        controller=text_generation_controller,
-        context=inference_context,
-        random_seed=args.seed,
-        track_paused_request_events=args.inference_dynamic_batching_track_paused_request_events,
-        enable_chunked_prefill=not args.disable_chunked_prefill,
-        inference_logging_step_interval=inference_logging_step_interval,
-        pg_collection=pg_collection,
-    )
-
-
 class MegatronLocal(InferenceServer, ReturnsTokens, ReturnsRaw):
     """Interface to use MCoreEngine directly as an inference engine."""
 
+    host: str
+    port: int
+
+    _server_task: asyncio.Task = PrivateAttr(None)
     _client: InferenceClient = PrivateAttr(None)
     _inference_engine: DynamicInferenceEngine = PrivateAttr(None)
+    _rl_kv_cache_management_mode: KVCacheManagementMode = PrivateAttr(None)
 
-    async def base_generate(self, request: InferenceRequest):
-
-        if any(isinstance(p, LLMChatMessage) for p in request.prompt):
-            raise ValueError(
-                "MegatronLocal does not support chat requests."
-                "Use MegatronChatLocal to apply chat templating."
-            )
-        assert all(
-            isinstance(p, str) for p in request.prompt
-        ), "MegatronLocal only supports string prompts."
-
-        assert self._client is not None, "Client is not initialized"
+    async def base_generate(self, request: InferenceRequest) -> InferenceResponse:
 
+        assert self._server_task is not None, "Inference server is not initialized"
         tokenizer = get_tokenizer()
         args = get_args()
 
-        sampling_params = SamplingParams(
-            num_tokens_to_generate=None,
-            num_tokens_total=request.generation_args.max_tokens,
+        from openai import AsyncOpenAI
+        client = AsyncOpenAI(base_url=f"http://{self.host}:{self.port}", api_key="NONE")
+
+        # Things that may be problematic when doign this switch
+        # - Add BOS token
+        # - Skip prompt logprobs
+        response = await client.chat.completions.create(
+            model="",
+            messages=[message.model_dump() for message in request.prompt],
             temperature=request.generation_args.temperature or 1.0,
-            top_k=request.generation_args.top_k or 0,
             top_p=request.generation_args.top_p or 0.0,
-            termination_id=self._inference_engine.controller.tokenizer.eod,
-            return_log_probs=True,
-            skip_prompt_log_probs=True,
-            add_BOS=(not args.rl_skip_bos_token and tokenizer.bos is not None),
+            n=1,
+            logprobs=True,
+            extra_body={
+                "skip_prompt_log_probs": True,
+                "add_BOS": (not args.rl_skip_bos_token and tokenizer.bos is not None),
+            },
+        )
+
+        choice = response.choices[0]
+
+        return InferenceResponse(
+            # TODO: Handle tool calls and reasoning in LLMChatMessage
+            response=LLMChatMessage(**choice.message.model_dump(include={'role', 'content'})),
+            raw_text=choice.raw_text,
+            token_ids=choice.prompt_token_ids + choice.generation_token_ids,
+            logprobs=choice.generation_log_probs,
+            prompt_length=len(choice.prompt_token_ids),
         )
-        requests = [
-            self._client.add_request(prompt=prompt, sampling_params=sampling_params)
-            for prompt in request.prompt
-        ]
-        records = await asyncio.gather(*requests)
-        responses = [record[-1] for record in records]
-        return [
-            InferenceResponse(
-                response=r.generated_text,
-                raw_text=p + r.generated_text,
-                token_ids=r.prompt_tokens.tolist() + r.generated_tokens,
-                logprobs=r.generated_log_probs,
-                prompt_length=len(r.prompt_tokens),
-            )
-            for p, r in zip(request.prompt, responses)
-        ]
 
     @classmethod
     async def launch(cls, model: GPTModel, **kwargs):
+        # Import here to avoid circular imports
+        from megatron.inference.utils import get_dynamic_inference_engine
+
         args = get_args()
         tokenizer = get_tokenizer()
 
@@ -256,42 +87,34 @@ async def launch(cls, model: GPTModel, **kwargs):
                 "WARNING: Tokenizer has no BOS token so prompt will not have BOS token",
             )
 
-        # Get inference logging configuration from args
-        log_inference_wandb = args.inference_wandb_logging
-        inference_logging_step_interval = args.inference_logging_step_interval
-
-        # Get metrics writer if logging is enabled and on the logging rank
-        # Use the same rank convention as training (last rank logs)
-        metrics_writer = None
-        if (
-            inference_logging_step_interval > 0
-            and log_inference_wandb
-            and args.rank == (args.world_size - 1)
-        ):
-            metrics_writer = get_wandb_writer()
-            if metrics_writer is None:
-                log_single_rank(
-                    logger,
-                    logging.WARNING,
-                    "WARNING: --rl-inference-logging-step-interval is set but no metrics writer "
-                    "wandb module is available. Inference logging will be disabled.",
-                )
-
-        inference_engine: DynamicInferenceEngine = get_dynamic_inference_engine(
-            args, model, inference_logging_step_interval, metrics_writer
-        )
+        inference_engine: DynamicInferenceEngine = get_dynamic_inference_engine(model=model)
         dp_addr = await inference_engine.start_listening_to_data_parallel_coordinator(
             inference_coordinator_port=41521, launch_inference_coordinator=True,
         )
+
         if dist.get_rank() == 0:
-            # TODO: We have to do this only on the rank 0 process, should be fixed in the future when we have support for multiple inference clients. !2278
+            from megatron.core.inference.text_generation_server.dynamic_text_gen_server.flask_server import run_flask_server_on_client
+            loop = asyncio.get_event_loop()
             client = InferenceClient(inference_coordinator_address=dp_addr)
             await client.start()
+            server_task = loop.create_task(run_flask_server_on_client(
+                client=client,
+                tokenizer=inference_engine.controller.tokenizer,
+                flask_port=kwargs.get('port', 8294),
+                parsers=[],
+                verbose=kwargs.get('verbose', False),
+            ))
         else:
             client = None
+            server_task = None
+            
         launched_server = cls(**kwargs)
         launched_server._client = client
+        launched_server._server_task = server_task
         launched_server._inference_engine = inference_engine
+        launched_server._rl_kv_cache_management_mode = KVCacheManagementMode(
+            args.rl_kv_cache_management_mode
+        )
 
         return launched_server
 
@@ -302,13 +125,12 @@ async def kill(self):
 
     async def suspend(self):
         if dist.get_rank() == 0:
-            await self._client.pause_engines()
+            self._client.suspend_engines()
         await self._inference_engine.paused.wait()
+        self._inference_engine.suspend()
 
     async def resume(self):
         if dist.get_rank() == 0:
-            self._client.unpause_engines()
+            self._client.resume_engines()
         await self._inference_engine.running.wait()
-
-
-class MegatronChatLocal(ChatInferenceInterface, MegatronLocal): ...
+        self._inference_engine.resume()
diff --git a/megatron/rl/parallel_utils.py b/megatron/rl/parallel_utils.py
index 9cab73daba9..da4ee8aa4cf 100644
--- a/megatron/rl/parallel_utils.py
+++ b/megatron/rl/parallel_utils.py
@@ -21,6 +21,7 @@ def build_inference_pg_collection(
     ep_size: Optional[int] = None,
     expt_tp_size: Optional[int] = None,
     use_tp_pp_dp_mapping: bool = False,
+    rank_offset: int = 0,
 ) -> ProcessGroupCollection:
     """
     Build a ProcessGroupCollection for an RL inference model with custom parallelism.
@@ -37,6 +38,8 @@ def build_inference_pg_collection(
         ep_size: Expert parallel size. Defaults to training's EP size.
         expt_tp_size: Expert tensor parallel size. Defaults to training's expert TP size.
         use_tp_pp_dp_mapping: If True, use 'tp-pp-dp' order; otherwise 'tp-dp-pp'.
+        rank_offset: Starting rank when the grid doesn't span the entire communication world.
+                     Used in non-collocated mode where model ranks don't start from 0.
 
     Returns:
         ProcessGroupCollection configured for the inference model.
@@ -78,13 +81,15 @@ def build_inference_pg_collection(
         # Order: tp-cp-pp-dp
         decoder_grid = HyperCommGrid(
             [tp_size, cp_size, pp_size, dp_size],
-            ["tp", "cp", "pp", "dp"]
+            ["tp", "cp", "pp", "dp"],
+            rank_offset=rank_offset
         )
     else:
         # Order: tp-cp-dp-pp (default)
         decoder_grid = HyperCommGrid(
             [tp_size, cp_size, dp_size, pp_size],
-            ["tp", "cp", "dp", "pp"]
+            ["tp", "cp", "dp", "pp"],
+            rank_offset=rank_offset
         )
 
     # Create dense layer groups from decoder_grid
@@ -105,13 +110,15 @@ def build_inference_pg_collection(
         # Order: tp-ep-pp-dp
         expert_grid = HyperCommGrid(
             [expt_tp_size, ep_size, pp_size, expt_dp_size],
-            ["tp", "ep", "pp", "dp"]
+            ["tp", "ep", "pp", "dp"],
+            rank_offset=rank_offset
         )
     else:
         # Order: tp-ep-dp-pp (default)
         expert_grid = HyperCommGrid(
             [expt_tp_size, ep_size, expt_dp_size, pp_size],
-            ["tp", "ep", "dp", "pp"]
+            ["tp", "ep", "dp", "pp"],
+            rank_offset=rank_offset
         )
 
     # Verify PP groups match between decoder and expert grids (required by mpu)
diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py
index 973a396b909..46436add1da 100644
--- a/megatron/rl/rl_utils.py
+++ b/megatron/rl/rl_utils.py
@@ -2,18 +2,19 @@
 
 import gc
 
+import copy
+from functools import partial
 # Keep this to make the env registered.
 import itertools
-import json
-import logging
 import math
-import pickle
+import logging
+import json
+import os
 from collections import Counter, defaultdict
 from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
-from difflib import SequenceMatcher
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Optional, Tuple
+from typing import Any, Dict, Iterator, List, Optional 
 
 import numpy as np
 import torch
@@ -23,13 +24,14 @@
 from torch.utils.tensorboard import SummaryWriter
 
 from megatron.core import mpu
-from megatron.core.datasets.megatron_tokenizer import MegatronLegacyTokenizer
 from megatron.core.full_cuda_graph import FullCudaGraphWrapper
 from megatron.core.models.common.language_module.language_module import LanguageModule
+from megatron.core.num_microbatches_calculator import reconfigure_num_microbatches_calculator
 from megatron.core.optimizer import MegatronOptimizer
 from megatron.core.pipeline_parallel import get_forward_backward_func
 from megatron.core.pipeline_parallel.utils import is_pp_last_stage, get_pp_last_rank
 from megatron.core.rerun_state_machine import RerunDataIterator
+from megatron.core.tokenizers import MegatronTokenizer
 from megatron.core.transformer.cuda_graphs import _CudagraphGlobalRecord
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.utils import toggle_cuda_graphs
@@ -45,10 +47,10 @@
     compute_packed_inference_logprobs_stats,
     pack_all_trajectories,
     load_packed_data_by_index,
-    update_sequence_packing_metrics,
     get_sequence_packing_tensorboard_metrics,
     get_sequence_packing_log_info,
     get_default_packed_seq_params,
+    update_microbatch_calculator,
 )
 from megatron.rl.agent.api import (
     EvaluationRequest,
@@ -59,18 +61,16 @@
     TokenRollout,
 )
 from megatron.rl.agent.weighted_multi_task import WeightedMultiTask
-from megatron.rl.inference.megatron import MegatronChatLocal, MegatronLocal
+from megatron.rl.inference.megatron import MegatronLocal
 from megatron.rl.logging import LOG_DIR as lang_rl_log_dir
 from megatron.rl.logging import log as lang_rl_log
 from megatron.rl.server.inference.inference_interface_server import InferenceInterfaceServer
 from megatron.training.global_vars import (
     get_args,
     get_tensorboard_writer,
-    get_timers,
     get_tokenizer,
     get_wandb_writer,
 )
-from megatron.training.tokenizer.tokenizer import CustomTikTokenizer, _HuggingFaceTokenizer
 from megatron.training.utils import (
     get_ltor_masks_and_position_ids,
     get_nvtx_range,
@@ -94,16 +94,61 @@
 _GLOBAL_PACKING_CONTEXT = None
 
 
+# Track whether the inference model is currently paused (offloaded to CPU).
+# Model starts on GPU after creation and is used immediately, so starts as False.
+_INFERENCE_MODEL_IS_PAUSED = False
+
+
+def _torch_saver_swap_inference_model(*, to_cpu: bool) -> None:
+    """Swap RL inference model weights between CPU and GPU using torch_memory_saver.
+
+    Uses torch_memory_saver.pause()/resume() to transfer inference model weights
+    that were allocated within a torch_memory_saver.region() context.
+
+    Args:
+        to_cpu: If True, move weights to CPU (pause). If False, restore weights to GPU (resume).
+    """
+    global _INFERENCE_MODEL_IS_PAUSED
+
+    if not HAVE_TORCH_MEMORY_SAVER:
+        raise RuntimeError(
+            "torch_memory_saver is required for inference model offloading when not using UVM. "
+            "Please install it: pip install torch_memory_saver "
+            "(see https://github.com/fzyzcjy/torch_memory_saver)"
+        )
+
+    if to_cpu:
+        if not _INFERENCE_MODEL_IS_PAUSED:
+            torch_memory_saver.pause("rl_inference_model")
+            _INFERENCE_MODEL_IS_PAUSED = True
+            print_rank_0("[Rank 0] offloaded RL inference model weights to CPU using torch_memory_saver")
+    else:
+        if _INFERENCE_MODEL_IS_PAUSED:
+            torch_memory_saver.resume("rl_inference_model")
+            _INFERENCE_MODEL_IS_PAUSED = False
+            print_rank_0("[Rank 0] restored RL inference model weights to GPU using torch_memory_saver")
+
+
 def _maybe_prefetch_separate_inference_model_weights(model_core, *, to_cpu: bool) -> None:
-    """Prefetch RL *separate inference model* weights to CPU/GPU (UVM-only path).
+    """Prefetch RL *separate inference model* weights to CPU/GPU.
+
+    Supports two modes:
+    1. UVM-based offloading (when --rl-inference-model-unified-memory-level=1)
+    2. torch_memory_saver-based offloading (when offloading is enabled but UVM is not)
 
-    Gated only by user args; this assumes the separate inference model was allocated with UVM when enabled.
+    Gated by user args; this assumes the separate inference model was allocated
+    with UVM or torch_memory_saver when enabled.
     """
     args = get_args()
     if not args.rl_offload_inference_model_weights_when_idle:
         return
+
+    # Check for torch_memory_saver path (when offloading is enabled but UVM is not)
     if args.rl_inference_model_unified_memory_level != 1:
+        _torch_saver_swap_inference_model(to_cpu=to_cpu)
         return
+
+    # UVM-based path (when UVM level is 1)
     device = -1 if to_cpu else int(torch.cuda.current_device())
     # Note: include_buffers=False because buffers created with explicit device= in register_buffer()
     # are not allocated via the UVM mempool and will fail UVM operations. Only parameters are UVM-allocated.
@@ -209,27 +254,24 @@ def verify_model_weights_swap(
         if inf_was_training:
             inf_core.train()
 
-GroupedRollouts = list[list[TokenRollout | Rollout]]
+Rollouts = list[TokenRollout | Rollout]
+GroupedRollouts = list[Rollouts]
 
 
 @dataclass(slots=True)
 class RolloutStats:
-    mean_reward: float
-    mean_sim: None | float
-    mean_length: float
-    mean_length_std: float
-    max_length: float
-    min_length: float
-    reward_means: list[float]
-    reward_stds: list[float]
-    rewards: list[float]
+    rewards: list[list[float]] # inner list is for a group
+    env_ids: list[str] # same length as len(rewards)
+    turn_lens: list[list[int]] # token lengths of turns, grouped.
+    traj_lens: list[list[int]] # all turns comprise one trajectory.
+    num_turns: None | list[list[int]] # num_turns per traj
+    advantages: None | list[list[float]]
     min_piold_to_inf_prob: None | float
     max_piold_to_inf_prob: None | float
     mean_piold_to_inf_prob: None | float
     min_inf_train_prob_abs_diff: None | float
     max_inf_train_prob_abs_diff: None | float
     mean_inf_train_prob_abs_diff: None | float
-    advantages: None | list[list[float]]
     min_inf_prob: None | float
     max_inf_prob: None | float
     mean_inf_prob: None | float
@@ -242,7 +284,6 @@ class RLRuntimeState:
     def __init__(self):
         self.packing_context = None
         self.last_collection_iteration = 0
-        self.global_batches_per_collection = 0
         self.sequences_this_iteration_on_rank = 0
         self.latest_batch_num_sequences = 0
 
@@ -386,36 +427,13 @@ def get_agent(args, parallel_generation_tasks: int | None = None):
 def get_inference_interface(args, loop, model):
     global _INFERENCE_INTERFACE
     if _INFERENCE_INTERFACE is None:
-        rank = torch.distributed.get_rank()
-        if rank == 0 and args.langrl_external_server:
-            if args.langrl_inference_server_type == 'inplace_megatron':
-                _INFERENCE_INTERFACE = loop.run_until_complete(
-                    InferenceInterfaceServer.launch(MegatronLocal, model=model[0])
-                )
-            elif args.langrl_inference_server_type == 'inplace_megatron_chat':
-                _INFERENCE_INTERFACE = loop.run_until_complete(
-                    InferenceInterfaceServer.launch(
-                        MegatronChatLocal,
-                        model=model[0],
-                        conversation_template=args.langrl_inference_server_conversation_template,
-                    )
-                )
-            else:
-                raise ValueError(f"Unknown inference_server_type {args.inference_server_type}")
-        else:
-            if args.langrl_inference_server_type == 'inplace_megatron':
-                _INFERENCE_INTERFACE = loop.run_until_complete(MegatronLocal.launch(model[0]))
-            elif args.langrl_inference_server_type == 'inplace_megatron_chat':
-                _INFERENCE_INTERFACE = loop.run_until_complete(
-                    MegatronChatLocal.launch(
-                        model[0],
-                        conversation_template=args.langrl_inference_server_conversation_template,
-                    )
-                )
-            else:
-                raise ValueError(
-                    f"Unknown inference_server_type {args.langrl_inference_server_type}"
-                )
+        _INFERENCE_INTERFACE = loop.run_until_complete(
+            MegatronLocal.launch(
+                model[0],
+                host='0.0.0.0',
+                port=8294,
+                verbose=args.inference_flask_server_logging)
+        )
     return _INFERENCE_INTERFACE
 
 
@@ -462,11 +480,17 @@ def get_environment_rollouts(
 
     if args.rl_offload_optimizer_during_inference:
         with nvtx_range("offload-optimizer-state-and-grad-buffers-during-inference"):
-            model[0].offload_grad_buffers()
+            if not args.rl_training_cuda_graphs:
+                model[0].offload_grad_buffers()
+            else:
+                logger.warning(
+                    "Gradient buffers will not be offloaded when training cudagraphs are used!"
+                )
             optimizer.offload_to_cpu()
-             
-    # If we have seperate training and inference models we to refit weights from the training model to the inference model.
-    if inference_model is not None:
+
+    # If we have separate training and inference models we to refit weights from the training model to the inference model.
+    has_separate_inference_model = inference_model is not None
+    if has_separate_inference_model:
         # If the separate inference model weights were prefetched to CPU while idle, bring them
         # back to GPU before refit/copy and before any CUDA-graph'd inference.
         with nvtx_range("prefetch-inference-model-weights-to-gpu"):
@@ -494,10 +518,8 @@ def get_environment_rollouts(
             inference_model,
             optimizer,
             args.cuda_graph_impl,
-            args.rl_reset_cuda_graphs,
             False, # offload optimizer during rollout collection is handled above
-            args.rl_offload_kv_cache_during_training,
-            args.rl_remove_kv_cache_during_training,
+            training_model=model if has_separate_inference_model else None,
         ) as inference_interface:
 
             with nvtx_range("inference-setup"):
@@ -518,6 +540,10 @@ def get_environment_rollouts(
                     rollouts = [
                         loop.run_until_complete(anext(rollout_generator)) for _ in range(n_prompts)
                     ]
+                    # In deterministic mode, sort rollouts by problem_id for consistent ordering
+                    # regardless of completion order due to system timing jitter.
+                    if torch.are_deterministic_algorithms_enabled():
+                        rollouts.sort(key=lambda group: group[0].problem_id if group and group[0].problem_id else "")
                     if not args.rl_partial_rollouts:
                         while True:
                             try:
@@ -544,10 +570,10 @@ def get_environment_rollouts(
         with open(
             lang_rl_log_dir
             + f'/rollouts_rank{rank}_iteration{args.curr_iteration}_'
-            + f'{Path(args.langrl_env_config).stem}.pkl',
-            'wb',
+            + f'{Path(args.langrl_env_config).stem}.json',
+            'w',
         ) as f:
-            pickle.dump(rollouts, f)
+            json.dump([[r.model_dump() for r in group] for group in rollouts], f)
 
     return rollouts
 
@@ -613,20 +639,20 @@ def get_logprobs(model, tokens, position_ids, no_grad=False, sequence_packing=Fa
 
     """
 
+    args = get_args()
     # Ensure packed_seq_params is always provided for CUDA graph signature consistency
     if packed_seq_params is None and sequence_packing:
         packed_seq_params = get_default_packed_seq_params(
             seq_length=tokens.shape[1],
+            max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
             device=tokens.device,
         )
 
     nvtx_range = get_nvtx_range()
 
     with nvtx_range("get-logprobs", time=False):
-
         with nvtx_range("forward-pass", time=False):
             # TODO(vitalyk): use fp16/bf16 as a function argument. Do not use args.
-            args = get_args()
 
             attention_mask_for_forward = None
 
@@ -658,80 +684,98 @@ def get_logprobs(model, tokens, position_ids, no_grad=False, sequence_packing=Fa
             return logprobs
 
 
+def calculate_grpo_advantages(rewards: list[list[float]], num_turns: list[list[int]]) -> np.ndarray:
+    """Calculate GRPO advantages from rewards/num_turns.
+
+    For multiturn rollouts, the logic is a bit more involved.
+    # For training, we'll be turning each turn into a trajectory with the same reward
+    # within a trajectory, e.g. if [[a,b],[c,d,e]] trajectory has reward 1.0, we will
+    # get [a,b] with 1.0 and [c,d,e] with 1.0 when doing updates.
+    """
+
+    rewards = np.array(rewards)
+
+    num_turns = np.array(num_turns)
+    # Each outer dimension of num_turns is a group. Sum of those gives total num_turns per group.
+    # Let's use this to calculate advantage.
+    # mean/std should be repeated based on group lens
+    group_turns = num_turns.sum(axis=-1)
+    reward_means = rewards.mean(axis=1, keepdims=True).repeat(group_turns)
+    reward_stds = rewards.std(axis=1, keepdims=True).repeat(group_turns)
+
+    # rewards are originally [g, group_size]
+    # Making an assumption that all groups are of the same size!
+    # @vitalyk: this will go away when we start sending env-based sample reqs.
+    rewards = rewards.flatten().repeat(num_turns.flatten())
+
+    return ((rewards - reward_means) / (1e-4 + reward_stds)).tolist()
+
+
 def compute_group_stats(
-    rollouts: GroupedRollouts, tokenizer: MegatronLegacyTokenizer
+    rollouts: GroupedRollouts, tokenizer: MegatronTokenizer, seq_len: int,
 ) -> RolloutStats:
     """Add group-based rollout stats for logging.
 
     Args:
         rollouts: Rollouts to generate the stats for. Each inner list is a group (as in GRPO group), i.e. all rollouts are for the same prompt.
         tokenizer: Tokenizer to tokenize the rollouts in case they are raw strings.
+        seq_len: Maximum sequence length.
 
     Returns:
        RolloutStats object containing all the stats.
     """
-    args = get_args()
     # TODO (rkirby) Maybe do some of this after the tensor building
     group_reward_means = []
     group_reward_stds = []
-    group_length_means = []
-    group_length_stds = []
-    group_length_maxs = []
-    group_length_mins = []
-    group_rollout_similarities = []
+    turn_lens = []
+    traj_lens = []
+    rewards = []
+    env_ids = []
+    group_reward_ids = []
+    num_turns = [] # num_turns per traj
     for group in rollouts:
         group_rewards = []
-        group_lengths = []
+        group_traj_lengths = []
+        group_turn_lengths = []
+        group_num_turns = []
         for rollout in group:
             if isinstance(rollout, TokenRollout):
-                lang_rl_log(
-                    f"Rollout: [{rollout.env_id}] [{rollout.reward} : {len(rollout.trajectory)} tokens] {tokenizer.detokenize(rollout.trajectory)}"
-                )
-                assert (len(rollout.trajectory) == args.seq_length) or (
-                    rollout.trajectory[-1] == tokenizer.eod
-                ), f"Rollout is not the correct length: {len(rollout.trajectory)} {rollout.trajectory[-1]}\n{tokenizer.detokenize(rollout.trajectory)}"
+                for turn_traj in rollout.trajectory:
+                    detokenized_traj = tokenizer.detokenize(turn_traj)
+                    lang_rl_log(
+                        f"Rollout: [{rollout.env_id}] [{rollout.reward} : {len(rollout.trajectory)} tokens] {detokenized_traj}"
+                    )
+                    # TODO(vitalyk): how does multiturn change EOD/EOT?
+                    assert (len(turn_traj) == seq_len) or (
+                        turn_traj[-1] == tokenizer.eod
+                    ), f"Rollout is not the correct length: {len(turn_traj)} {turn_traj[-1]}\n{detokenized_traj}"
             else:
                 lang_rl_log(
                     f"Rollout: [{rollout.env_id}] [{rollout.reward} : {len(rollout.trajectory)} chars] {rollout.trajectory}"
                 )
+            group_num_turns.append(len(rollout.trajectory))
             group_rewards.append(rollout.reward)
-            group_lengths.append(len(rollout.trajectory))
-        if args.rl_calculate_intra_group_similarity:
-            # We can probably compute this outside, but in case we switch to different group sizes for different envs, let's keep it here.
-            combos = itertools.combinations(range(len(group)), 2)
-            # For every pair (excluding ourselves), check the sequence similarity and log.
-            # Use this to track the diversity of generated rollouts within a group.
-            intra_group_sim = np.mean(
-                list(
-                    map(
-                        lambda idx_pair: SequenceMatcher(
-                            None, group[idx_pair[0]].trajectory, group[idx_pair[1]].trajectory
-                        ).ratio(),
-                        combos,
-                    )
-                )
-            )
-            group_rollout_similarities.append(intra_group_sim)
-        else:
-            group_rollout_similarities = None
-
-        group_length_maxs.append(max(group_lengths))
-        group_length_mins.append(min(group_lengths))
-        group_reward_means.append(np.mean(group_rewards))
-        group_reward_stds.append(np.std(group_rewards))
-        group_length_means.append(np.mean(group_lengths))
-        # https://arxiv.org/abs/2504.21233 reports that lens variants hurts.
+            roll_turn_lens = [len(t) for t in rollout.trajectory]
+            group_turn_lengths.extend(roll_turn_lens)
+            group_traj_lengths.append(sum(roll_turn_lens))
+        traj_lens.append(group_traj_lengths)
+        turn_lens.append(group_turn_lengths)
+        env_ids.append(group[0].env_id) # All rollouts in a group share the env_id by design.
+        rewards.append(group_rewards)
+        # https://arxiv.org/abs/2504.21233 reports that lens variance hurts.
         # Let's track this.
-        group_length_stds.append(np.std(group_lengths))
+        num_turns.append(group_num_turns)
+
     stats = RolloutStats(
-        mean_reward=np.mean(group_reward_means),
-        mean_sim=np.mean(group_rollout_similarities) if group_rollout_similarities else None,
-        mean_length=np.mean(group_length_means),
-        mean_length_std=np.mean(group_length_stds),
-        max_length=np.max(group_length_maxs),
-        min_length=np.min(group_length_mins),
-        reward_means=group_reward_means,
-        reward_stds=group_reward_stds,
+        traj_lens=traj_lens,
+        turn_lens=turn_lens,
+        rewards=rewards,
+        # --------
+        # Everything above is per-group, i.e. it is a list of lists,
+        # with the inner list being the group data.
+        env_ids=env_ids,
+        num_turns=num_turns,
+        advantages=calculate_grpo_advantages(rewards, num_turns),
         min_piold_to_inf_prob=None,
         max_piold_to_inf_prob=None,
         mean_piold_to_inf_prob=None,
@@ -741,19 +785,96 @@ def compute_group_stats(
         min_inf_prob=None,
         max_inf_prob=None,
         mean_inf_prob=None,
-        rewards=None,  # We will fill those in later in prepare_data_for_update.
-        advantages=None,  # We will fill those in later in prepare_data_for_update.
     )
     return stats
 
 
+def prep_wandb_metrics(
+        wandb_writer: wandb_run.Run,
+        traj_lens: List[List[int]],
+        turn_lens: List[List[int]],
+        rewards: List[List[float]],
+        num_turns: List[List[int]],
+        advantages: List[float],
+        example_group: list[TokenRollout | Rollout] | None = None,
+        tokenizer: MegatronTokenizer | None = None
+    ):
+
+    """Make a wandb-parseable dictionary of metrics for logging.
+
+    Args:
+        wandb_writer: Wandb run to log to.
+        traj_lens: Grouped list of trajectory lengths.
+        turn_lens: Grouped list of turn lengths.
+        rewards: Grouped list of rewards.
+        num_turns: Grouped list of number of turns in the trajectories.
+        advantages: Flattened list of advantages.
+        tokenizer: Tokenizer to untokenize trajectories for logging.
+        example_groups: A list of rollouts of one group to log examples of trajectories.
+    """
+
+    group_table = wandb_writer.Table(
+        columns=['group_means', 'group_stds'],
+        data=[[np.mean(g), np.std(g)] for g in rewards],
+    )
+
+    metrics = {
+            'group_means_hist': wandb_writer.plot.histogram(
+                group_table, 'group_means', 'Group Means'
+            ),
+            'group_stds_hist': wandb_writer.plot.histogram(
+                group_table, 'group_stds', 'Group STDs'
+            ),
+            'rewards_hist': wandb_writer.plot.histogram(
+                wandb_writer.Table(
+                    columns=['reward'], data=[[r] for g in rewards for r in g]
+                ),
+                'reward', 'All Rewards'
+            ),
+            'advantages_hist': wandb_writer.plot.histogram(
+                wandb_writer.Table(
+                    columns=['advantages'], data=[[x] for x in advantages]
+                ),
+                'advantages', 'Advantages'
+            ),
+            'mean_turn_length': np.mean([np.mean(g) for g in turn_lens]),
+            'mean_turn_length_std': np.mean([np.std(g) for g in turn_lens]),
+            'max_turn_length': max([max(g) for g in turn_lens]),
+            'min_turn_length': min([min(g) for g in turn_lens]),
+            'mean_traj_length': np.mean([np.mean(g) for g in traj_lens]),
+            'mean_traj_length_std': np.mean([np.std(g) for g in traj_lens]),
+            'max_traj_length': max([max(g) for g in traj_lens]),
+            'min_traj_length': min([min(g) for g in traj_lens]),
+            'mean_num_turns': np.mean([np.mean(g) for g in num_turns]),
+            'max_num_turns': max([max(g) for g in num_turns]),
+            'min_num_turns': min([min(g) for g in num_turns]),
+            'mean_reward': np.mean([np.mean(g) for g in rewards]),
+            'mean_advantage': np.mean(advantages),
+            'nonzero_groups_ratio': np.count_nonzero(advantages)
+            / len(advantages),
+    }
+    if example_group:
+        if tokenizer is None:
+            raise ValueError("If you provide an example group to log, you need to provide a tokenizer too.")
+        metrics['rollouts'] = wandb_writer.Table(
+            columns=['Trajectories', 'Tokens', 'Rewards'],
+            rows=[
+                [
+                    tokenizer.detokenize(turn) if isinstance(r, TokenRollout) else turn,
+                    r.trajectory,
+                    r.reward,
+                ]
+                for r in example_group for turn in r.trajectory
+            ],
+        )
+    return metrics
+
+
 def maybe_log_training_metrics(
     group_stats: RolloutStats,
     current_iteration: int,
-    tokenizer: MegatronLegacyTokenizer,
-    example_group: list[TokenRollout | Rollout],
-    wandb_writer: wandb_run.Run | None = None,
-    tb_writer: SummaryWriter | None = None,
+    tokenizer: MegatronTokenizer,
+    example_groups: dict[str, list[TokenRollout | Rollout]],
 ):
     """Log training metrics if writers are available.
 
@@ -761,87 +882,67 @@ def maybe_log_training_metrics(
         group_stats: RolloutStats object to pass to writers.
         current_iteration: Current training iteration.
         tokenizer: Tokenizer to untokenize trajectories for logging.
-        example_group: A list of rollouts of one group to log examples of trajectories.
-        wandb_writer: W&B writer object.
-        tb_writer:  Tensorboard writer object.
+        example_groups: A dict with values as list of rollouts of one group to log examples of trajectories. Keys are env names.
     """
-    if wandb_writer:
-        group_table = wandb_writer.Table(
-            columns=['group_means', 'group_stds'],
-            data=list(zip(group_stats.reward_means, group_stats.reward_stds)),
-        )
-        rollout_table = wandb_writer.Table(
-            columns=['reward'], data=[[r] for r in group_stats.rewards]
-        )
-        advantages = wandb_writer.Table(
-            columns=['advantages'], data=[[x] for x in group_stats.advantages]
-        )
-        wandb_writer.log(
-            {
-                **{
-                    'group_means_hist': wandb_writer.plot.histogram(
-                        group_table, 'group_means', 'Group Means'
-                    ),
-                    'group_stds_hist': wandb_writer.plot.histogram(
-                        group_table, 'group_stds', 'Group STDs'
-                    ),
-                    'rewards_hist': wandb_writer.plot.histogram(
-                        rollout_table, 'reward', 'All Rewards'
-                    ),
-                    'mean_length': group_stats.mean_length,
-                    'mean_length_std': group_stats.mean_length_std,
-                    'max_length': group_stats.max_length,
-                    'min_length': group_stats.min_length,
-                    'mean_reward': group_stats.mean_reward,
-                    'mean_advantage': np.mean(group_stats.advantages),
-                    'advantages_hist': wandb_writer.plot.histogram(
-                        advantages, 'advantages', 'Advantages'
-                    ),
-                    'nonzero_groups_ratio': np.count_nonzero(group_stats.advantages)
-                    / len(group_stats.advantages),
-                    'min_piold_to_inf_prob': group_stats.min_piold_to_inf_prob,
-                    'max_piold_to_inf_prob': group_stats.max_piold_to_inf_prob,
-                    'mean_piold_to_inf_prob': group_stats.mean_piold_to_inf_prob,
-                    'min_inf_train_prob_abs_diff': group_stats.min_inf_train_prob_abs_diff,
-                    'max_inf_train_prob_abs_diff': group_stats.max_inf_train_prob_abs_diff,
-                    'mean_inf_train_prob_abs_diff': group_stats.mean_inf_train_prob_abs_diff,
-                    'min_inf_prob': group_stats.min_inf_prob,
-                    'max_inf_prob': group_stats.max_inf_prob,
-                    'mean_inf_prob': group_stats.mean_inf_prob,
-                    # For now only log the first group
-                    'rollouts': wandb_writer.Table(
-                        columns=['Trajectories', 'Tokens', 'Rewards'],
-                        rows=[
-                            [
-                                (
-                                    tokenizer.detokenize(r.trajectory)
-                                    if isinstance(r, TokenRollout)
-                                    else r.trajectory
-                                ),
-                                r.trajectory,
-                                r.reward,
-                            ]
-                            for r in example_group
-                        ],
-                    ),
-                },
-                **(
-                    {'mean_intra_group_similarity': group_stats.mean_sim}
-                    if group_stats.mean_sim
-                    else {}
-                ),
-            },
-            step=current_iteration,
-        )
+
+    wandb_writer = get_wandb_writer()
+    tb_writer = get_tensorboard_writer()
     if tb_writer:
-        tb_writer.add_scalar('mean_reward', group_stats.mean_reward, current_iteration)
+        tb_writer.add_scalar('mean_reward', np.mean([np.mean(g) for g in group_stats.rewards]), current_iteration)
+    if not wandb_writer:
+        return
+
+    # We log these metrics for the aggregated data, no split per env.
+    metrics = {
+        'min_piold_to_inf_prob': group_stats.min_piold_to_inf_prob,
+        'max_piold_to_inf_prob': group_stats.max_piold_to_inf_prob,
+        'mean_piold_to_inf_prob': group_stats.mean_piold_to_inf_prob,
+        'min_inf_train_prob_abs_diff': group_stats.min_inf_train_prob_abs_diff,
+        'max_inf_train_prob_abs_diff': group_stats.max_inf_train_prob_abs_diff,
+        'mean_inf_train_prob_abs_diff': group_stats.mean_inf_train_prob_abs_diff,
+        'min_inf_prob': group_stats.min_inf_prob,
+        'max_inf_prob': group_stats.max_inf_prob,
+        'mean_inf_prob': group_stats.mean_inf_prob,
+    }
+
+    traj_lens = group_stats.traj_lens
+    turn_lens = group_stats.turn_lens
+    rewards = group_stats.rewards
+    num_turns = group_stats.num_turns
+    advantages = group_stats.advantages
+    metrics = metrics | prep_wandb_metrics(wandb_writer=wandb_writer, 
+        traj_lens=traj_lens, turn_lens=turn_lens, rewards=rewards, num_turns=num_turns, advantages=advantages)  
+    env_stats = lambda cont, idx: [cont[i] for i in idx]
+    group_turn_counts = [sum(nt) for nt in num_turns]
+
+    for env_id in set(group_stats.env_ids):
+        env_idx = [i for i, eidx in enumerate(group_stats.env_ids) if eidx == env_id]
+
+        # Advantages are flattened, we need to be more careful with those.
+        env_advantages = []
+        for i in env_idx:
+            st = sum(group_turn_counts[:i])
+            end = st + group_turn_counts[i]
+            env_advantages.extend(advantages[st:end])
+
+        env_metrics = prep_wandb_metrics(wandb_writer=wandb_writer, traj_lens=env_stats(traj_lens, env_idx), 
+            turn_lens=env_stats(turn_lens, env_idx), 
+            rewards=env_stats(rewards, env_idx),
+            num_turns=env_stats(num_turns, env_idx),
+            advantages=env_advantages,
+            example_group=example_groups[env_id],
+            tokenizer=tokenizer,
+        )
+        for k, v in env_metrics.items():
+            metrics[f"{env_id}_{k}"] = v
+
+    wandb_writer.log(metrics, step=current_iteration)
 
 
 def prepare_trajectories(
-    rollouts: GroupedRollouts, tokenizer: MegatronLegacyTokenizer, seq_length: int
+    rollouts: Rollouts, tokenizer: MegatronTokenizer, seq_length: int, sequence_packing: bool, skip_bos_token: bool
 ):
     """Pad trajectories and extract the generation masks.
-
     Args:
         rollouts: Rollouts to extract trajectories from.
         tokenizer: Tokenizer to get the padding token and potentially tokenize.
@@ -858,7 +959,7 @@ def prepare_trajectories(
 
     DEFAULT_PAD_TOKENS = ['<|finetune_right_pad_id|>']
 
-    if isinstance(tokenizer, _HuggingFaceTokenizer):
+    if tokenizer.library == "huggingface":
         if not tokenizer.pad:
             for pad_token in DEFAULT_PAD_TOKENS:
                 if pad_token in tokenizer.vocab:
@@ -869,7 +970,7 @@ def prepare_trajectories(
                     break
             else:
                 raise ValueError("No pad token found in tokenizer vocabulary")
-    elif isinstance(tokenizer, CustomTikTokenizer):
+    elif tokenizer.library == "tiktoken":
         assert "<SPECIAL_233>" in tokenizer.vocab, "Pad token is NOT in the tokenizer"
         tokenizer._pad_id = tokenizer.vocab["<SPECIAL_233>"]
 
@@ -888,17 +989,19 @@ def prepare_trajectories(
     trajs = []
     generation_masks = []
     inference_logprobs = []
-    for group in rollouts:
-        for rollout in group:
-            generation_mask = rollout.generation_mask if isinstance(rollout, TokenRollout) else None
-
-            trajectory = (
-                rollout.trajectory.copy()
-                if isinstance(rollout, TokenRollout)
-                else tokenizer.tokenize(rollout.trajectory)
-            )
-            inf_logprobs = rollout.logprobs
-
+    for rollout in rollouts:
+        # traj, gen mask and logprobs are lists now.
+        # each list entry is a turn, single-turn environments just have a single-element list.
+        # We assume that all lengths of the structs above have the same lengths (number of turns).
+
+        all_turns_trajectories = (
+            copy.deepcopy(rollout.trajectory)
+            if isinstance(rollout, TokenRollout)
+            else tokenizer.tokenize(rollout.trajectory)
+        )
+        for turn_idx, trajectory in enumerate(all_turns_trajectories):
+            inf_logprobs = rollout.logprobs[turn_idx]
+            generation_mask = rollout.generation_mask[turn_idx] if isinstance(rollout, TokenRollout) else None
             length = len(trajectory)
             assert length <= seq_length, "Rollout too long, how did this happen?"
             if len(trajectory) < seq_length:
@@ -920,8 +1023,7 @@ def prepare_trajectories(
             else:
                 inference_logprobs.append(None)
 
-            env_id = rollout.env_id
-            env_id_counts[env_id] += 1
+        env_id_counts[rollout.env_id] += 1
 
     if torch.distributed.is_initialized():
         logger.info(f"[{dist.get_rank()}] Rollout counts:")
@@ -931,34 +1033,18 @@ def prepare_trajectories(
     generation_masks = torch.tensor(generation_masks, dtype=torch.bool, device='cpu')
     trajs = torch.tensor(trajs, device='cpu')
 
-    args = get_args()
     # Only process if we have inference_logprobs
     if inference_logprobs and any(lp is not None for lp in inference_logprobs):
-        if args.rl_use_sequence_packing:
-            # For sequence packing, we need to pad all logprobs to the same size
-            padded_logprobs = []
-            for logprobs in inference_logprobs:
-                if logprobs is not None:
-                    if len(logprobs) < seq_length:
-                        # Pad with zeros (these positions will be masked anyway)
-                        padding_size = seq_length - len(logprobs)
-                        padded = torch.nn.functional.pad(logprobs, (0, padding_size), value=0.0)
-                        padded_logprobs.append(padded)
-                    else:
-                        padded_logprobs.append(logprobs)
-                else:
-                    # Create zero tensor for None logprobs
-                    padded_logprobs.append(torch.zeros(seq_length))
-            inference_logprobs = torch.stack(padded_logprobs)
-        else:
-            # For non-packing mode, keep as list of tensors (unpadded)
-            # This preserves the original behavior where each sequence can have different lengths
-            pass
+        # We need to pad all logprobs to the same size for sequence packing.
+        # For non-packing mode, keep as list of tensors (unpadded)
+        # This preserves the original behavior where each sequence can have different lengths
+        if sequence_packing:
+            inference_logprobs = _pad_nonnull_with_zeros(inference_logprobs, seq_length)
     else:
         inference_logprobs = None
 
     # Some sanity checks regarding the tokenization
-    if not args.rl_skip_bos_token:
+    if not skip_bos_token:
         assert (
             tokenizer.bos is None or (trajs[:, 0] == tokenizer.bos).all()
         ), "First token should be bos"
@@ -979,11 +1065,94 @@ def prepare_trajectories(
     return trajs, generation_masks, inference_logprobs
 
 
+def logprobs_forward_step(data_iterator, model, is_correction, packing_context=None):
+    # Avoid self.training checks which will trigger cudagraph capture; this path reuses
+    # the forward pass from training after it has been captured on the 1st iteration.
+    model.eval()
+
+    if packing_context is not None:
+        # When using sequence packing, the data iterator returns a tuple with a single element, the bin index.
+        bin_tensor = next(data_iterator)[0]
+        #TODO(jalbericiola): change for named tuple
+        (b_trajs, _, _, _, b_posids, _, _, _, _, _, b_packed_seq_params) = (
+            load_packed_data_by_index(bin_tensor.item(), packing_context, is_correction)
+        )
+    else:
+        b_trajs, b_posids = next(data_iterator)
+        b_packed_seq_params = None
+
+    logprobs = (
+        get_logprobs(
+            model,
+            b_trajs.cuda(),
+            b_posids.cuda(),
+            no_grad=True,
+            sequence_packing=packing_context is not None,
+            packed_seq_params=b_packed_seq_params,
+        ),
+        None,
+    )
+    model.train()
+    return logprobs
+
+
+def compute_logprobs_batch(
+    model,
+    data_loader,
+    forward_backward_func,
+    packing_context,
+    trajs_batch_size, # n_bins for seq packing, and batch_size for non seq packing
+    seq_length,
+    logprobs_batch_size,
+    decoder_seq_length,
+    dtype,
+    pp_group,
+    is_correction,
+    collect_non_loss_data=False,
+):
+    """Compute logprobs for all batches in the data loader."""
+    logprobs_list = []
+    data_iterator = iter(data_loader)
+    for i in range(len(data_loader)):
+        output_tensor = forward_backward_func(
+            forward_step_func=partial(logprobs_forward_step, is_correction=is_correction, packing_context=packing_context),
+            data_iterator=data_iterator,
+            model=model,
+            num_microbatches=1,
+            seq_length=seq_length,
+            micro_batch_size=logprobs_batch_size,
+            decoder_seq_length=decoder_seq_length,
+            forward_only=True,
+            adjust_tensor_shapes_fn=None,
+            collect_non_loss_data=collect_non_loss_data,
+        )
+        if is_pp_last_stage(pp_group):
+            logprobs_list.append(output_tensor[0].detach())
+
+    if is_pp_last_stage(pp_group):
+        logprobs = torch.concat(logprobs_list, dim=0)
+        assert logprobs.dtype == dtype
+    else:
+        logprobs = torch.empty(
+            trajs_batch_size,
+            seq_length-1,
+            dtype=dtype,
+            device=torch.cuda.current_device(),
+        )
+
+    # Only PP>1 needs a broadcast from the last stage; for PP=1 the output is already local.
+    if get_pg_size(pp_group) > 1:
+        dist.broadcast(logprobs, src=get_pp_last_rank(pp_group), group=pp_group)
+    return logprobs.cpu()
+
+
 def prepare_data_for_update(
     model: list[LanguageModule],
     ref_state_dict: Dict[str, Any],
     rollouts: GroupedRollouts,
-    tokenizer: MegatronLegacyTokenizer,
+    tokenizer: MegatronTokenizer,
+    sequence_packing: bool,
+    is_correction: bool,
 ) -> RerunDataIterator:
     """Extract data for the update from raw rollouts.
 
@@ -992,13 +1161,13 @@ def prepare_data_for_update(
         ref_state_dict: Reference policy state dict.
         rollouts: Rollouts to extract the data from.
         tokenizer: Tokenizer to pad/tokenize data.
+        sequence_packing: Use sequence packing if True.
+        is_correction: Prepare data for IS correction if True.
 
     Returns:
         Cycled iterator over dataset batches. In GRPO we might want to go over the same data multiple times.
     """
     args = get_args()
-    wandb_writer = get_wandb_writer()
-    tb_writer = get_tensorboard_writer()
     nvtx_range = get_nvtx_range()
     runtime_state = get_rl_runtime_state()
 
@@ -1006,42 +1175,39 @@ def prepare_data_for_update(
         lang_module = (
             model[0].module.module if hasattr(model[0].module, "module") else model[0].module
         )
-        toggle_cuda_graphs(lang_module, "none", reset_cuda_graphs=False)
+        toggle_cuda_graphs(lang_module, "none")
 
     model = model[0]
     dtype = torch.bfloat16 if args.bf16 else (torch.float16 if args.fp16 else torch.float32)
 
     with nvtx_range("prepare-data-for-update"):
         with nvtx_range("compute-group-stats"):
-            # These are computed on all rollouts for reporting purposes
-            group_stats = compute_group_stats(rollouts, tokenizer)
-            rewards = np.array([[rollout.reward for rollout in group] for group in rollouts])
-            group_stats.rewards = rewards.flatten().tolist()
-            group_stats.advantages = (
-                (
-                    (rewards - rewards.mean(axis=1, keepdims=True))
-                    / (1e-4 + rewards.std(axis=1, keepdims=True))
-                )
-                .flatten()
-                .tolist()
-            )
-            global_rollout_count = len(group_stats.rewards)
-
-        with nvtx_range("prepare_advantages", time=True):        
-            # [g, group_size]
-            # Making an assumption that all groups are of the same size!
-            rewards = torch.tensor(rewards, device='cpu')
-            advantages = (rewards - rewards.mean(axis=1, keepdim=True)) / (
-                1e-4 + rewards.std(axis=1, keepdim=True)
-            )
-
-            # Flatten advantages for training and move to GPU
-            advantages = global_advantages = advantages.view(-1).cuda()
+            group_stats = compute_group_stats(rollouts, tokenizer, args.seq_length)
+            # TODO(vitalyk): why do we need global_advantages here? go inside packing
+            advantages = global_advantages = torch.tensor(group_stats.advantages, dtype=dtype).cuda()
 
         # Now split the rollouts across the data parallel ranks for training
         # This needs to be done at this point because we are about to calculate logprobs
         # Note :- For EP, do not use the expert data parallel group here. Always 
         # use the regular data parallel group. 
+
+        # Get example group per environment to log their rollouts.
+        example_groups = {}
+        for g in rollouts:
+            if g[0].env_id not in example_groups:
+                example_groups[g[0].env_id] = g
+
+        # Let's expand rollouts getting rid of the groups.
+        # We need this to correctly split the rollouts across dp groups.
+        # And we do not actually need them grouped in anything below anyways.
+        rollouts = [r for g in rollouts for r in g]
+        num_turns = [nt for g in group_stats.num_turns for nt in g]
+        total_turns_sampled = len(rollouts)
+
+        # We might sample more than we consume in one step.
+        samples_ratio_per_step = args.global_batch_size / (args.grpo_prompts_per_step * args.grpo_group_size)
+        assert samples_ratio_per_step <= 1, "You cannot use more data than you sampled."
+
         if (data_parallel_world_size := mpu.get_data_parallel_world_size()) > 0:
             data_split_size = len(rollouts) // data_parallel_world_size
             data_split_range = (
@@ -1049,23 +1215,20 @@ def prepare_data_for_update(
                 (mpu.get_data_parallel_rank() + 1) * data_split_size,
             )
             rollouts = rollouts[data_split_range[0] : data_split_range[1]]
+            local_num_turns = sum(num_turns[data_split_range[0] : data_split_range[1]])
+            steps_before = sum(num_turns[:data_split_range[0]])
+            advantages = advantages[steps_before:steps_before+local_num_turns]
             # First we calculate them on a global level and then we split and recalculate on a local level.
             # Sequence packing and reporting needs it global but non-packing wants it local.
-            rewards = torch.tensor([[r.reward for r in group] for group in rollouts], device='cpu')
-            advantages = (rewards - rewards.mean(axis=1, keepdim=True)) / (
-                1e-4 + rewards.std(axis=1, keepdim=True)
-            )
-
-            # Flatten advantages for training and move to GPU
-            advantages = advantages.view(-1).cuda()
 
         with nvtx_range("prepare_trajectories"):
             trajs, generation_masks, inference_logprobs = prepare_trajectories(
-                rollouts, tokenizer, args.seq_length
+                rollouts, tokenizer, args.seq_length, sequence_packing, args.rl_skip_bos_token
             )
 
+        packing_context = None
         # Build trajectories based on sequence packing or standard processing
-        if args.rl_use_sequence_packing:
+        if sequence_packing:
             with nvtx_range("sequence_packing", time=True):
                 runtime_state.packing_context = packing_context = pack_all_trajectories(
                     trajs, 
@@ -1105,7 +1268,6 @@ def prepare_data_for_update(
                 )
                 logprobs_batch_size = args.micro_batch_size
 
-
         with torch.no_grad(), nvtx_range("compute_logprobs", time=True):
             # Before we can update the model, we need to get the logprobs for the \pi_{old} model.
 
@@ -1116,41 +1278,6 @@ def prepare_data_for_update(
                     forward_backward_func, cuda_graph_warmup_steps=args.cuda_graph_warmup_steps
                 )
 
-            def logprobs_forward_step(data_iterator, model):
-
-                # Avoid self.training checks which will trigger cudagraph capture; this path reuses
-                # the forward pass from training after it has been captured on the 1st iteration.
-                model.eval()
-
-                if args.rl_use_sequence_packing:
-                    # When using sequence packing, the data iterator returns a tuple with a single element, the bin index.
-                    bin_tensor = next(data_iterator)[0]
-                    #TODO(jalbericiola): change for named tuple
-                    (b_trajs, _, _, _, b_posids, _, _, _, _, _, b_packed_seq_params) = (
-                        load_packed_data_by_index(bin_tensor.item(), packing_context, args.rl_inference_logprobs_is_correction)
-                    )
-                else:
-                    batch_data = next(data_iterator)
-                    b_trajs, b_posids = batch_data
-                    b_packed_seq_params = None
-
-                b_trajs = b_trajs.cuda()
-                b_posids = b_posids.cuda()
-
-                logprobs = (
-                    get_logprobs(
-                        model,
-                        b_trajs,
-                        b_posids,
-                        no_grad=True,
-                        sequence_packing=args.rl_use_sequence_packing,                       
-                        packed_seq_params=b_packed_seq_params,
-                    ),
-                    None,
-                )
-
-                model.train()
-                return logprobs
 
             dtype = (
                 torch.bfloat16 if args.bf16 else (torch.float16 if args.fp16 else torch.float32)
@@ -1159,43 +1286,20 @@ def logprobs_forward_step(data_iterator, model):
             pg_collection = get_attr_wrapped_model(model, "pg_collection")
             pp_group = pg_collection.pp
 
-            def _compute_logprobs_batch():
-                """Compute logprobs for all batches in the data loader."""
-                logprobs_list = []
-                data_iterator = iter(data_loader)
-                for i in range(len(data_loader)):
-                    output_tensor = forward_backward_func(
-                        forward_step_func=logprobs_forward_step,
-                        data_iterator=data_iterator,
-                        model=model,
-                        num_microbatches=1,
-                        seq_length=args.seq_length,
-                        micro_batch_size=logprobs_batch_size,
-                        decoder_seq_length=args.decoder_seq_length,
-                        forward_only=True,
-                        adjust_tensor_shapes_fn=None,
-                    )
-                    if is_pp_last_stage(pp_group):
-                        logprobs_list.append(output_tensor[0].detach())
-
-                if is_pp_last_stage(pp_group):
-                    logprobs = torch.concat(logprobs_list, dim=0)
-                    assert logprobs.dtype == dtype
-                else:
-                    logprobs = torch.empty(
-                        len(compute_trajs),
-                        args.seq_length - 1,
-                        dtype=dtype,
-                        device=torch.cuda.current_device(),
-                    )
-
-                # Only PP>1 needs a broadcast from the last stage; for PP=1 the output is already local.
-                if get_pg_size(pp_group) > 1:
-                    dist.broadcast(logprobs, src=get_pp_last_rank(pp_group), group=pp_group)
-                return logprobs.cpu()
-
             with torch.no_grad(), nvtx_range("compute_old_logprobs", time=True):
-                old_logprobs = _compute_logprobs_batch()
+                old_logprobs = compute_logprobs_batch(
+                    model=model,
+                    data_loader=data_loader,
+                    forward_backward_func=forward_backward_func,
+                    packing_context=packing_context,
+                    trajs_batch_size=len(compute_trajs),
+                    seq_length=args.seq_length,
+                    logprobs_batch_size=logprobs_batch_size,
+                    decoder_seq_length=args.decoder_seq_length,
+                    dtype=dtype,
+                    pp_group=pp_group,
+                    is_correction=args.rl_inference_logprobs_is_correction,
+                )
 
             with torch.no_grad(), nvtx_range("compute_ref_logprobs", time=True):
                 # We need to load the ref model state dict and compute the logprobs for the ref model
@@ -1203,8 +1307,19 @@ def _compute_logprobs_batch():
                     k: (v.cpu() if v is not None else v) for k, v in model.state_dict().items()
                 }
                 model.load_state_dict(ref_state_dict)
-
-                ref_logprobs = _compute_logprobs_batch()
+                ref_logprobs = compute_logprobs_batch(
+                    model=model,
+                    data_loader=data_loader,
+                    forward_backward_func=forward_backward_func,
+                    packing_context=packing_context,
+                    trajs_batch_size=len(compute_trajs),
+                    seq_length=args.seq_length,
+                    logprobs_batch_size=logprobs_batch_size,
+                    decoder_seq_length=args.decoder_seq_length,
+                    dtype=dtype,
+                    pp_group=pp_group,
+                    is_correction=args.rl_inference_logprobs_is_correction,
+                )
 
                 # logprobs are [b, seq, h] now.
                 model.load_state_dict(cur_st_dict)
@@ -1214,7 +1329,7 @@ def _compute_logprobs_batch():
             torch.cuda.empty_cache()
 
 
-        if args.rl_use_sequence_packing:
+        if sequence_packing:
             with nvtx_range("pack_logprobs", time=True):
                 # Store logprobs on gpu in packing context
                 # Since PackingContext is a dataclass, we add these as new attributes
@@ -1243,6 +1358,22 @@ def _compute_logprobs_batch():
                     packing_context.packed_inference_logprobs = packed_inference_logprobs.cuda()
                     # Only mark as having inference logprobs for IS correction if enabled
                     packing_context.has_inference_logprobs = args.rl_inference_logprobs_is_correction
+            with nvtx_range("create_dataloader"):
+                # @vitalyk: This function also reconfigures the data loader to count the
+                # global_batch_size in the bins frame of reference.
+                # I think it will be a better design if we split the data loader creating and logic
+                # that reconfigures the microbatch calculator.
+
+                update_microbatch_calculator(
+                    samples_ratio_per_step=samples_ratio_per_step,
+                    num_bins_this_rank = len(packing_context.packed_trajs),
+                    bin_seq_indices = packing_context.packing_info.bin_seq_indices,
+                    global_batch_size=args.global_batch_size, 
+                    rampup_batch_size=args.rampup_batch_size, 
+                    micro_batch_size=args.micro_batch_size, 
+                    decrease_batch_size_if_needed=args.decrease_batch_size_if_needed,
+               )
+                loader = get_microbatch_dataloader(len(packing_context.packed_trajs), args.micro_batch_size)
         else:
             with nvtx_range("align_inference_logprobs", time=True):
                 if inference_logprobs is not None:
@@ -1257,14 +1388,20 @@ def _compute_logprobs_batch():
                     # Nullify logprobs if not used in IS correction,
                     if not args.rl_inference_logprobs_is_correction:
                         inference_logprobs = None
-
-        with nvtx_range("create_dataloader"):
-            if args.rl_use_sequence_packing:
-               loader, optimizer_steps = get_microbatch_dataloader(packing_context)
-               runtime_state.global_batches_per_collection = optimizer_steps
-            else:
+            with nvtx_range("create_dataloader"):
+                # Because of multiturn, our batch sizes for non-sequence packed trajectories are not fixed anymore.
+                # As in sequence packing above, we need to reconfigure it too.
                 runtime_state.packing_context = None
-                runtime_state.global_batches_per_collection = global_rollout_count / args.global_batch_size
+
+                reconfigure_num_microbatches_calculator(
+                    rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0,
+                    global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), 
+                    rampup_batch_size=args.rampup_batch_size, 
+                    micro_batch_size=args.micro_batch_size, 
+                    decrease_batch_size_if_needed=args.decrease_batch_size_if_needed,
+                    data_parallel_size=mpu.get_data_parallel_world_size(),
+                )
+
                 dataset_tensors = [
                     compute_trajs,
                     advantages,
@@ -1273,87 +1410,84 @@ def _compute_logprobs_batch():
                     original_position_ids,
                     ref_logprobs,
                 ]
-                if args.rl_inference_logprobs_is_correction and inference_logprobs is not None:
+                if is_correction and inference_logprobs is not None:
                     dataset_tensors.append(inference_logprobs)
                 else:
                     dataset_tensors.append(torch.zeros_like(old_logprobs))
-
                 data = TensorDataset(*dataset_tensors)
                 loader = DataLoader(data, batch_size=args.micro_batch_size)
 
+
         with nvtx_range("log-wandb-tb"):
             maybe_log_training_metrics(
                 group_stats=group_stats,
                 current_iteration=args.curr_iteration,
                 tokenizer=tokenizer,
-                example_group=rollouts[0],
-                wandb_writer=wandb_writer,
-                tb_writer=tb_writer,
+                example_groups=example_groups,
             )
 
     return RerunDataIterator(itertools.cycle(loader))
 
 
-def get_rollout_data_iterator(
-    model: LanguageModule,
-    inference_model: LanguageModule | None,
-    optimizer: MegatronOptimizer,
-    iteration: int,
-    ref_state_dict: Dict[str, torch.Tensor],
-) -> RerunDataIterator:
-
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    buffered_rollouts = get_environment_rollouts(
-        model, inference_model, optimizer, args.grpo_prompts_per_step, args.grpo_group_size
-    )
-    buffered_rollouts = prepare_data_for_update(model, ref_state_dict, buffered_rollouts, tokenizer)
-
-    return buffered_rollouts
-
-
-def setup_grpo_data_iterator(
+def get_grpo_data_iterator(
     model: LanguageModule,
     inference_model: LanguageModule | None,
     optimizer: MegatronOptimizer,
     iteration: int,
     ref_state_dict: Dict[str, torch.Tensor],
+    grpo_iterations: int,
+    grpo_prompts_per_step: int,
+    grpo_group_size: int,
+    global_batch_size: int,
+    sequence_packing: bool,
+    is_correction: bool,
     buffered_rollouts: RerunDataIterator | None = None,
 ) -> RerunDataIterator:
     """
-    Set up the data iterator for GRPO training.
+    Get the data iterator for GRPO training.
+
+    Depending on the sampling parameters either performs data collections or returns
+    the buffered_rollouts as is.
 
     Args:
         model: The language model
         optimizer: The Megatron optimizer
         iteration: Current training iteration
         ref_state_dict: Reference model state dict for GRPO
+        grpo_iterations: How many steps we reuse the sampled data for.
+        grpo_prompts_per_step: How many prompts we sample per data collection.
+        grpo_group_size: How many samples we do per prompt.
+        global_batch_size: Global batch size.
+        sequence_packing: Use sequence packing if True.
+        is_correction: Use IS correction if True.
         buffered_rollouts: Previously collected rollouts (if any)
 
     Returns:
         RerunDataIterator for the current training step
     """
-    args = get_args()
     runtime_state = get_rl_runtime_state()
 
-    if inference_model is not None:
-        inference_pg_collection = unwrap_model(inference_model[0]).pg_collection
-    else:
-        inference_pg_collection = ProcessGroupCollection.use_mpu_process_groups()
-
     # We collect new rollouts when we've gone over the collected data 'grpo_iterations' times.
+    global_batches_per_collection = (grpo_prompts_per_step * grpo_group_size) // global_batch_size 
     if (
         buffered_rollouts is None or
         iteration == runtime_state.last_collection_iteration + 
-        (args.grpo_iterations * runtime_state.global_batches_per_collection)
+        (grpo_iterations * global_batches_per_collection)
     ):
-        train_data_iterator = get_rollout_data_iterator(model,inference_model, optimizer, iteration, ref_state_dict)
+
+        buffered_rollouts = get_environment_rollouts(
+            model, inference_model, optimizer, grpo_prompts_per_step, grpo_group_size
+        )
+        buffered_rollouts = prepare_data_for_update(model=model, 
+            ref_state_dict=ref_state_dict, 
+            rollouts=buffered_rollouts,
+            tokenizer=get_tokenizer(),
+            sequence_packing=sequence_packing,
+            is_correction=is_correction,
+            )
         runtime_state.reset_iteration_counters(iteration)
-    else:
-        train_data_iterator = buffered_rollouts
 
-    return train_data_iterator
+    return buffered_rollouts
 
 
 def evaluate_and_print_results_rl(
@@ -1362,14 +1496,17 @@ def evaluate_and_print_results_rl(
     optimizer: MegatronOptimizer,
     iteration: int,
     write_to_tensorboard: bool = True,
+    training_model: Optional[list[LanguageModule]] = None,
 ):
     """Helper function to evaluate and dump results on screen.
 
     Args:
         data_iterator: Iterator over batches of evaluation dataset.
-        model: Model to evaluate with.
+        model: Model to evaluate with (may be separate inference model).
         iteration: Current training iteration.
         write_to_tensorboard: Dumpt stuff to tensorboard or not.
+        training_model: Training model (if separate from inference model). Used to offload
+            grad buffers and restore to train mode. If None, uses model parameter.
     """
     args = get_args()
 
@@ -1383,17 +1520,15 @@ def evaluate_and_print_results_rl(
             model,
             optimizer,
             args.cuda_graph_impl,
-            args.rl_reset_cuda_graphs,
             args.rl_offload_optimizer_during_inference,
-            args.rl_offload_kv_cache_during_training,
-            args.rl_remove_kv_cache_during_training,
+            training_model,
         ) as inference_interface:
 
             loop = get_asyncio_loop()
 
             rank = torch.distributed.get_rank()
             if rank == 0:
-                logger.info(f"Collecting evaluation results...")
+                logger.info("Collecting evaluation results...")
                 agent = get_agent(args)
                 request = EvaluationRequest(
                     inference_interface=inference_interface,
@@ -1462,10 +1597,10 @@ def evaluate_and_print_results_rl(
                 with open(
                     lang_rl_log_dir
                     + f'/eval_rank{rank}_iteration{args.curr_iteration}_'
-                    + f'{Path(args.langrl_env_config).stem}.pkl',
-                    'wb',
+                    + f'{Path(args.langrl_env_config).stem}.json',
+                    'w',
                 ) as f:
-                    pickle.dump(dp_eval_results, f)
+                    json.dump([[r.model_dump() for r in group] for group in dp_eval_results], f)
 
 
 def calculate_grpo_loss(
@@ -1567,21 +1702,18 @@ def megatron_rl_inference_mode(
     model: list[LanguageModule],
     optimizer: MegatronOptimizer,
     cuda_graph_impl: str,
-    reset_cuda_graphs: bool,
     offload_optimizer_during_inference: bool,
-    offload_kv_cache_during_training: bool,
-    remove_kv_cache_during_training: bool,
+    training_model: Optional[list[LanguageModule]] = None,
 ):
     """Manage the model inference context when collecting rollouts.
 
     Args:
-        model: model to prepare.
+        model: model to prepare for inference (may be separate inference model).
         optimizer: optimizer used to train the model.
         cuda_graph_impl: which cuda graph implementation to use.
-        reset_cuda_graphs: rebuild cuda graphs for each inference stage or not.
         offload_optimizer_during_inference: move optimizer to cpu during inference or not.
-        offload_kv_cache_during_training: manually offload kv cache to host before training or not.
-        remove_kv_cache_during_training: manually remove kv cache before training or not.
+        training_model: training model (if separate from inference model). Used to offload
+            grad buffers and restore to train mode. If None, uses model parameter.
 
     Yields:
         None: this context manager does not return a value.
@@ -1597,10 +1729,10 @@ def megatron_rl_inference_mode(
     lang_module = model[0].module.module if hasattr(model[0].module, "module") else model[0].module
 
     lang_module.eval()
-    # If this is a separate RL inference model allocated with UVM, ensure weights are resident on GPU
-    # before any CUDA-graph capture/replay or inference.
+    # If this is a separate RL inference model with offloading enabled, ensure weights are on GPU
+    # before any CUDA-graph capture/replay or inference. This is a no-op if already on GPU.
+    model_core = unwrap_model(model[0])
     with nvtx_range("prefetch-inference-model-weights-to-gpu"):
-        model_core = unwrap_model(model[0])
         _maybe_prefetch_separate_inference_model_weights(model_core, to_cpu=False)
 
     rotary_module = getattr(lang_module, "rotary_pos_emb", None)
@@ -1614,40 +1746,21 @@ def megatron_rl_inference_mode(
 
         if offload_optimizer_during_inference:
             with nvtx_range("offload-optimizer-state-and-grad-buffers-before-inference"):
-                model[0].offload_grad_buffers()
+                if not args.rl_training_cuda_graphs:
+                    # Offload grad buffers from the training model (if separate inference model is used)
+                    # or from the inference model (if they're the same model)
+                    model_for_grad_offload = training_model if training_model is not None else model
+                    model_for_grad_offload[0].offload_grad_buffers()
+                else:
+                    logger.warning(
+                        "Gradient buffers will not be offloaded when training cudagraphs are used!"
+                    )
                 optimizer.offload_to_cpu()
 
-        # TODO: Remove this if statement once a change to `toggle_cuda_graphs` makes it safe to.
         if cuda_graph_impl != "none" and not args.rl_training_cuda_graphs:
-            toggle_cuda_graphs(lang_module, cuda_graph_impl, reset_cuda_graphs=reset_cuda_graphs)
+            toggle_cuda_graphs(lang_module, cuda_graph_impl)
 
         inference_interface = get_inference_interface(args, loop, model)
-
-        with nvtx_range("onload-kv-cache-before-inference"):
-            if offload_kv_cache_during_training:
-                # Restore the KV cache by re-binding physical pages to a consistent virtual address
-                torch_memory_saver.resume("kv_cache")
-
-                logger.debug(
-                    f"[{dist.get_rank()}] Restoring kv cache ({inference_interface._inference_engine.context.memory_buffer.numel() / 1024**3:.2f} GB) to GPU"
-                )
-                kv_cache = inference_interface._inference_engine.context.memory_buffer
-                inference_interface._inference_engine.context.memory_buffer = kv_cache.cuda()
-            elif remove_kv_cache_during_training:
-                if inference_interface._inference_engine.context.memory_buffer is None:
-                    inference_interface._inference_engine.context.build_memory_buffer()
-
-        # TODO: Improve this if statement once a change is made to CUDA graph handling.
-        cuda_graph_exists = len(_CudagraphGlobalRecord.cudagraph_inference_record) != 0
-        if cuda_graph_impl != "none" and not cuda_graph_exists:
-            with nvtx_range("wait-for-decode-only"):
-                while not inference_interface._inference_engine.context.is_decode_only():
-                    active_requests, finished_requests, step_time = loop.run_until_complete(
-                        inference_interface._inference_engine.async_step()
-                    )
-            with nvtx_range("build-cuda-graphs"):
-                inference_interface._inference_engine.create_cuda_graphs(reset_context=True)
-
         loop.run_until_complete(inference_interface.resume())
 
         logger.debug(f"[{dist.get_rank()}] Entered inference mode")
@@ -1656,32 +1769,25 @@ def megatron_rl_inference_mode(
         with nvtx_range("suspend-engine"):
             loop.run_until_complete(inference_interface.suspend())
 
-        with nvtx_range("offload-kv-cache-after-inference"):
-            if offload_kv_cache_during_training:
-                kv_cache = inference_interface._inference_engine.context.memory_buffer
-                logger.debug(
-                    f"[{dist.get_rank()}] Offloading kv cache ({kv_cache.numel() * kv_cache.element_size() / 1024**3:.2f} GB) to CPU"
-                )
-                torch_memory_saver.pause("kv_cache")
-
-            elif remove_kv_cache_during_training:
-                inference_interface._inference_engine.context.memory_buffer = None
-
-        # TODO: Remove this if statement once a change to `toggle_cuda_graphs` makes it safe to.
         if cuda_graph_impl != "none" and not args.rl_training_cuda_graphs:
-            toggle_cuda_graphs(lang_module, 'none', reset_cuda_graphs=reset_cuda_graphs)
+            toggle_cuda_graphs(lang_module, 'none')
 
-        # If this is a separate RL inference model, prefetch weights back to CPU so they don't consume
-        # GPU memory during training.
+        # If this is a separate RL inference model, prefetch weights back to CPU so they
+        # don't consume GPU memory during training.
         with nvtx_range("prefetch-inference-model-weights-to-cpu"):
             _maybe_prefetch_separate_inference_model_weights(model_core, to_cpu=True)
 
         if offload_optimizer_during_inference:
             with nvtx_range("onload-optimizer-state-and-grad-buffers-after-inference"):
-                model[0].restore_grad_buffers()
+                # Restore grad buffers to the training model (if separate inference model is used)
+                # or to the inference model (if they're the same model)
+                model_for_grad_offload = training_model if training_model is not None else model
+                model_for_grad_offload[0].restore_grad_buffers()
                 optimizer.restore_from_cpu()
 
-        lang_module.train()
+        # Set training model back to train mode (not inference model if they're separate)
+        training_lang_module = unwrap_model(training_model[0]) if training_model is not None else lang_module
+        training_lang_module.train()
 
         if has_lru_cache:
             rotary_module.forward.cache_clear()
@@ -1698,6 +1804,12 @@ def rl_inference_interface_shutdown():
     else:
         logger.warning("No inference interface to shutdown. This should not happen.")
 
+    # TODO(rkirby): This is a hack to hard exit. There is a bug that is preventing us from using sys.exit(0).
+    # It seem the Flask server has non-daemon threads that are preventing the program from exiting.
+    # We need to find a way to gracefully complete all in progress requests and shutdown the Flask server.
+    import os
+    os._exit(0)
+
 
 def get_iteration_sequence_count(args):
     """Get the total number of sequences processed in this iteration across all ranks."""
@@ -1708,3 +1820,32 @@ def get_iteration_sequence_count(args):
     if torch.distributed.is_initialized():
         torch.distributed.all_reduce(sequences_tensor, group=mpu.get_data_parallel_group())
     return int(sequences_tensor.item())
+    
+def _pad_nonnull_with_zeros(data: list[Optional[torch.Tensor]], max_len: int) -> torch.Tensor:
+    """Pad each element of a list of tensors to the length required.
+    Args:
+        data: List of tensors to pad.
+        max_len: Maximum length to pad to. Must be higher or equal than the max len of the data tensors.
+    Returns:
+        A padded tensor which is a stacked list of padded input tensors.
+
+    """
+    if all([el is None for el in data]):
+        raise ValueError("At least one element of the data list should be not None.")
+    padded_data = []
+    for chunk in data:
+        if chunk is not None:
+            padding_size = max_len - len(chunk)
+            if padding_size > 0:
+                # Pad with zeros (these positions will be masked anyway)
+                padded = torch.nn.functional.pad(chunk, (0, padding_size), value=0.0)
+                padded_data.append(padded)
+            elif padding_size == 0:
+                padded_data.append(chunk)
+            else:
+                raise ValueError("One of the input tensors has larger length than padding max len.")
+        else:
+            # Create zero tensor for None logprobs
+            padded_data.append(torch.zeros(max_len))
+    return torch.stack(padded_data)
+
diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py
index a5703a4580c..b641ecd85d0 100644
--- a/megatron/rl/sequence_packing_utils.py
+++ b/megatron/rl/sequence_packing_utils.py
@@ -10,7 +10,6 @@
 from megatron.training.global_vars import get_args, get_tokenizer
 from megatron.training.utils import get_nvtx_range
 from megatron.core.packed_seq_params import PackedSeqParams
-from megatron.core.num_microbatches_calculator import get_num_microbatches
 from megatron.core import mpu
 import logging
 import typing
@@ -78,7 +77,6 @@ def load_packed_data_by_index(bin_idx: int, packing_context: PackingContext, log
     Args:
         bin_idx: Index of the bin to load.
     """
-    args = get_args()
     # Get packing context (should always be available in packed mode)
     idx = slice(bin_idx, bin_idx + 1)
 
@@ -156,9 +154,8 @@ def log_packing_efficiency(packing_context: PackingContext):
     packing_efficiency = my_tokens / total_capacity if total_capacity > 0 else 0
     avg_seq_length = total_tokens / len(packing_info.seq_lengths)
     rank = mpu.get_data_parallel_rank()
-    data_parallel_world_size = mpu.get_data_parallel_world_size()
 
-    log_single_rank(logger, logging.INFO, f"[Sequence Packing] Statistics:")
+    log_single_rank(logger, logging.INFO, "[Sequence Packing] Statistics:")
     log_single_rank(
         logger,
         logging.INFO,
@@ -269,7 +266,7 @@ def log_packing_efficiency(packing_context: PackingContext):
             log_single_rank(
                 logger,
                 logging.INFO,
-                f"[Sequence Packing]  Round-robin distribution quality:",
+                "[Sequence Packing]  Round-robin distribution quality:",
             )
             log_single_rank(
                 logger,
@@ -398,7 +395,7 @@ def create_empty_bins(
         empty_packing_info_entries,
     )
 
-def get_default_packed_seq_params(seq_length: int, device: torch.device) -> PackedSeqParams:
+def get_default_packed_seq_params(seq_length: int, max_sequences_per_bin: int, device: torch.device) -> PackedSeqParams:
     """Create a default PackedSeqParams that acts as no-op for a single sequence.
 
     This ensures CUDA graph signature consistency when packed_seq_params
@@ -407,6 +404,7 @@ def get_default_packed_seq_params(seq_length: int, device: torch.device) -> Pack
 
     Args:
         seq_length: The sequence length 
+        max_sequences_per_bin: Max sequences to pack in a bin.
         device: Device to create tensors on.
 
     Returns:
@@ -416,7 +414,10 @@ def get_default_packed_seq_params(seq_length: int, device: torch.device) -> Pack
     args = get_args()
 
     # Pad to the maximum number of sequences in the bin for the attention kernel.
-    cu_seqlens = torch.full((args.rl_sequence_packing_max_sequences_per_bin,), seq_length, dtype=torch.int32, device=device)
+    # We add 2 to account for the initial 0 and the final bin_size.
+    cu_seqlens = torch.full(
+        (max_sequences_per_bin + 2,), seq_length, dtype=torch.int32, device=device,
+    )
     cu_seqlens[0] = 0
 
     return PackedSeqParams(
@@ -486,8 +487,9 @@ def create_packed_seq_params_for_bin(
 
     # Pad cu_seqlens to bin_size by repeating the last value (creates zero-length ghost sequences)
     # This ensures a fixed tensor size for CUDA graph compatibility
-    if len(cu_seqlens) < max_sequences_per_bin:
-        out = cu_seqlens.new_full((max_sequences_per_bin,), bin_size)
+    # We add 2 to account for the initial 0 and the final bin_size.
+    if len(cu_seqlens) < max_sequences_per_bin + 2:
+        out = cu_seqlens.new_full((max_sequences_per_bin + 2,), bin_size)
         out[:len(cu_seqlens)] = cu_seqlens
         cu_seqlens = out
 
@@ -774,7 +776,7 @@ def pack_sequences(
 
         seq_per_bin = [len(indices) for indices in packing_info.bin_seq_indices]
         log_single_rank(
-            logger, logging.DEBUG, (f"Initial packing output (before distribution):")
+            logger, logging.DEBUG, ("Initial packing output (before distribution):")
         )
         log_single_rank(
             logger,
@@ -969,33 +971,20 @@ def distribute_packed_bins(
 def pack_all_trajectories(trajs, generation_masks, inference_logprobs, global_advantages, bin_size, max_sequences_per_bin, packing_algo):
     tokenizer = get_tokenizer()
     data_parallel_world_size = mpu.get_data_parallel_world_size()
+    data_parallel_group = mpu.get_data_parallel_group()
     nvtx_range = get_nvtx_range()
 
     with nvtx_range("regather_trajectories", time=True):
-        # Regather trajectories from all ranks for packing
-        trajs = trajs.cuda()
-        trajs_list = [torch.empty_like(trajs) for _ in range(data_parallel_world_size)]
-        torch.distributed.all_gather(
-            trajs_list, trajs, group=mpu.get_data_parallel_group()
-        )
-        trajs = torch.cat(trajs_list, dim=0)
-
-        # Gather all generation masks
-        generation_masks = generation_masks.cuda()
-        masks_list = [torch.empty_like(generation_masks) for _ in range(data_parallel_world_size)]
-        torch.distributed.all_gather(
-            masks_list, generation_masks, group=mpu.get_data_parallel_group()
-        )
-        generation_masks = torch.cat(masks_list, dim=0)
-
-        # Gather inference logprobs if present
+        def _gather(data):
+            data = data.cuda()
+            data_list = [torch.empty_like(data) for _ in range(data_parallel_world_size)]
+            torch.distributed.all_gather(data_list, data, group=data_parallel_group)
+            return torch.cat(data_list, dim=0)
+
+        trajs = _gather(trajs)    
+        generation_masks = _gather(generation_masks) 
         if inference_logprobs is not None:
-            inference_logprobs = inference_logprobs.cuda()
-            logprobs_list = [torch.empty_like(inference_logprobs) for _ in range(data_parallel_world_size)]
-            torch.distributed.all_gather(
-                logprobs_list, inference_logprobs, group=mpu.get_data_parallel_group()
-            )
-            inference_logprobs = torch.cat(logprobs_list, dim=0)
+            inference_logprobs = _gather(inference_logprobs)
 
     with nvtx_range("pack_sequences", time=True):
         # Create packer with max sequences per bin limit to prevent extreme imbalance
@@ -1073,53 +1062,63 @@ def pack_all_trajectories(trajs, generation_masks, inference_logprobs, global_ad
 
     return packing_context
 
+def update_microbatch_calculator(
+    samples_ratio_per_step: float,
+    num_bins_this_rank: int,
+    bin_seq_indices: List[List[int]],
+    global_batch_size: int, 
+    rampup_batch_size: int, 
+    micro_batch_size: int, 
+    decrease_batch_size_if_needed: bool,
+):
+    """Return a data loader with seqpacked indices with microbatches in bins frame of reference.
+    Args:
+        samples_ratio_per_step: Fraction of sampled trajectories to use per iteration.
+        num_bins_this_rank: Amount of packing bins that belongs to current rank.
+        bin_seq_indices: Global seq indices in the bin, see PackingInfo.
+        global_batch_size: Current global batch size.
+        rampup_batch_size: Rampup batch size. See num_microbatches_calculator.py for more.
+        micro_batch_size: Micro batch size at init.
+        decrease_batch_size_if_needed: Scale down batch size. See num_microbatches_calculator.py for more.
+
+    As a side effect, we calculate the global batch size in the bins frame of reference.
+    In sequence packing, our batch dimension shrinks as we move some trajs onto free
+    space in sequence dimension. The resulting batch size is what we return here.
+    """
 
-def get_microbatch_dataloader(packing_context: PackingContext) -> Tuple[DataLoader, int]:
-    args = get_args()
-    num_bins_this_rank = len(packing_context.packed_trajs)
     dp_world_size = mpu.get_data_parallel_world_size()
 
-    # Ratio of collected sequences to the global batch size
-    pct_of_sequences_per_batch = len(packing_context.packing_info.seq_lengths) / args.global_batch_size
-
     # Ceiling division means we will reuse some bins
     # If we did floor we would leave some behind
-    local_bins_per_step = math.ceil(pct_of_sequences_per_batch * num_bins_this_rank)
-    effective_global_batch_size = local_bins_per_step * dp_world_size
+    local_bins_per_step = math.ceil(samples_ratio_per_step * num_bins_this_rank)
 
-    # Store packing plan in runtime state for the training loop to use
-    optimizer_steps = -(-num_bins_this_rank // local_bins_per_step)
+    bins_bs = local_bins_per_step * dp_world_size
 
     old_num_microbatches = get_num_microbatches()
-
     reconfigure_num_microbatches_calculator(
         rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0,
-        rampup_batch_size=args.rampup_batch_size,
-        global_batch_size=effective_global_batch_size,
-        micro_batch_size=args.micro_batch_size,
+        rampup_batch_size=rampup_batch_size,
+        global_batch_size=bins_bs,
+        micro_batch_size=micro_batch_size,
         data_parallel_size=dp_world_size,
-        decrease_batch_size_if_needed=args.decrease_batch_size_if_needed,
+        decrease_batch_size_if_needed=decrease_batch_size_if_needed,
     )
-
     new_num_microbatches = get_num_microbatches()
 
     log_single_rank(
-        logger, logging.INFO, f"[Sequence Packing] Multi-step training plan:"
-    )
-    log_single_rank(
-        logger,
-        logging.INFO,
-        f"[Sequence Packing]  - Target sequences per step: {args.global_batch_size}",
+        logger, logging.INFO, "[Sequence Packing] Multi-step training plan:"
     )
+
     log_single_rank(
         logger,
         logging.INFO,
-        f"[Sequence Packing]  - Bins per rank per step: {pct_of_sequences_per_batch}*{num_bins_this_rank}={local_bins_per_step}",
+        f"[Sequence Packing]  - Bins per rank per step: {samples_ratio_per_step}*{num_bins_this_rank}={local_bins_per_step}",
     )
+
     log_single_rank(
         logger,
         logging.INFO,
-        f"[Sequence Packing]  - Total optimizer steps: {optimizer_steps}",
+        f"[Sequence Packing]  - Target sequences per step: {global_batch_size}",
     )
     log_single_rank(
         logger,
@@ -1127,8 +1126,10 @@ def get_microbatch_dataloader(packing_context: PackingContext) -> Tuple[DataLoad
         f"[Sequence Packing]  - Microbatches per step: {new_num_microbatches} (was {old_num_microbatches})",
     )
 
-    bin_seq_indices = packing_context.packing_info.bin_seq_indices
-    for step in range(min(3, optimizer_steps)):
+    # Opt steps only depends on how much we sample and how much we consume.
+    # We make sure this is an integer division, check validate_args in arguments.py for details.
+    opt_steps = int(1 / samples_ratio_per_step)
+    for step in range(min(3, opt_steps)):
         start_idx = step * local_bins_per_step
         end_idx = min(start_idx + local_bins_per_step, num_bins_this_rank)
         step_bins = end_idx - start_idx
@@ -1145,22 +1146,13 @@ def get_microbatch_dataloader(packing_context: PackingContext) -> Tuple[DataLoad
             f"[Sequence Packing]  - Step {step + 1}: {step_bins} bins, ~{est_global_seqs} sequences globally",
         )
 
-    if optimizer_steps > 3:
-        log_single_rank(logger, logging.INFO, f"  - ... ({optimizer_steps - 3} more steps)")
+    if opt_steps > 3:
+        log_single_rank(logger, logging.INFO, f"  - ... ({opt_steps - 3} more steps)")
 
+def get_microbatch_dataloader(num_bins_this_rank, micro_batch_size):
     bin_indices = torch.arange(num_bins_this_rank)
     dataset = TensorDataset(bin_indices)
-    loader = DataLoader(dataset, batch_size=args.micro_batch_size, shuffle=False, collate_fn=lambda x: x[0], drop_last=True)
-    return loader, optimizer_steps
-
-def update_sequence_packing_metrics(args):
-    """Update bin tracking for sequence packing mode."""
-    if args.rl_use_sequence_packing:
-        bin_count = (
-            mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches()
-        )
-        args.consumed_train_bins += bin_count
-
+    return DataLoader(dataset, batch_size=micro_batch_size, shuffle=False, collate_fn=lambda x: x[0])
 
 def get_sequence_packing_log_info(args):
     """Get logging information for sequence packing mode."""
diff --git a/megatron/rl/server/inference/inference_interface_server.py b/megatron/rl/server/inference/inference_interface_server.py
index ba595c3ca0e..ceac4a6cab7 100644
--- a/megatron/rl/server/inference/inference_interface_server.py
+++ b/megatron/rl/server/inference/inference_interface_server.py
@@ -11,10 +11,10 @@
 from typing_extensions import Self
 from uvicorn import Config, Server
 
-from ...inference.api import ChatInferenceRequest, ChatInferenceResponse
 from ...inference.inference_interface import (
-    ChatInferenceInterface,
     InferenceInterface,
+    InferenceRequest,
+    InferenceResponse,
     ReturnsRaw,
     ReturnsTokens,
 )
@@ -22,20 +22,17 @@
 
 
 @InferenceServer.register_subclass
-class InferenceInterfaceClient(ChatInferenceInterface, InferenceServer):
+class InferenceInterfaceClient(InferenceServer):
     type_name: str = Field(default='InferenceInterfaceClient', frozen=True)
     env_server_host_port: str
     conversation_template: None = None
 
-    async def base_generate(self, request: ChatInferenceRequest) -> list[ChatInferenceResponse]:
+    async def base_generate(self, request: InferenceRequest) -> InferenceResponse:
         async with httpx.AsyncClient(timeout=None) as client:
             response = await client.post(
                 f"http://{self.env_server_host_port}/base_generate/", json=request.model_dump()
             )
-            return [
-                ChatInferenceResponse.model_validate(inference_response)
-                for inference_response in response.json()
-            ]
+            return InferenceResponse.model_validate(response.json())
 
 
 @InferenceServer.register_subclass
@@ -69,7 +66,7 @@ async def launch(cls, interface_cls: type[InferenceInterface], **kwargs) -> Self
         server_ref = weakref.ref(launched_server)
 
         @app.post("/base_generate/")
-        async def base_generate(request: ChatInferenceRequest):
+        async def base_generate(request: InferenceRequest) -> InferenceResponse:
             server = server_ref()
             if server is None:
                 raise RuntimeError("Server has been garbage collected")
diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py
index 1af066a8207..aa5c410ca35 100644
--- a/megatron/training/arguments.py
+++ b/megatron/training/arguments.py
@@ -25,6 +25,7 @@
 )
 from megatron.core.utils import (
     get_torch_version,
+    is_flashinfer_min_version,
     is_te_min_version,
     is_torch_min_version,
 )
@@ -327,29 +328,83 @@ def validate_args(args, defaults={}):
     total_model_size = args.tensor_model_parallel_size * args.pipeline_model_parallel_size * args.context_parallel_size
     args.data_parallel_size = args.world_size // total_model_size
 
-    # Assert that `torch_memory_saver` is installed if offloading KV cache during RL.
-    if args.rl_offload_kv_cache_during_training:
-        try:
-            from torch_memory_saver import torch_memory_saver
-        except ImportError:
-            raise AssertionError("To use offload-kv-cache-during-training, `torch_memory_saver` must be installed. See https://github.com/fzyzcjy/torch_memory_saver.")
-        assert not args.inference_dynamic_batching_unified_memory_level, "The KV cache should not be instantiated in unified memory when it is offloaded during training."
-
-    # Batch size checks if running RL.
     if args.perform_rl_step:
-        assert not (args.rl_remove_kv_cache_during_training and args.rl_offload_kv_cache_during_training), \
-            "Cannot use both remove-kv-cache-during-training and offload-kv-cache-during-training"
+        # ----------------------------------------------------------------
+        # CUDA graphs
+        #
+        #   --cuda-graph-impl controls whether CUDA graphs are built.
+        #   The sweep of various inference CUDA graphs is built inside inference, not the RL loop.
+        #   Both training and inference CUDA graphs are gated by this flag.
+        #
+        #   --rl-training-cuda-graphs controls whether CUDA graphs are used during training.
+        #   Toggling CUDA graphs on and off is done inside the RL loop.
+        #
+        #   --rl-persist-cuda-graphs controls whether CUDA graphs are built once, or repeatedly.
+        #   When this flag is True, inference requires static memory pointers for the KV cache.
+        #   When this flag is False, inference is in charge of deleting/rebuilding CUDA graphs.
+        #
+        # KV cache management (--rl-kv-cache-management-mode)
+        #
+        #   Inference initializes the KV cache, inside either a normal memory pool, UVM, or TMS.
+        #
+        #   On suspend (inference -> training):
+        #     "persist"   — no-op; KV cache stays on GPU.
+        #     "offload"   — KV cache is offloaded to CPU.
+        #     "recompute" — KV cache is deleted entirely.
+        #
+        #   On resume (training → inference):
+        #     "persist"   — no-op; KV cache is already on GPU.
+        #     "offload"   — KV cache is restored from CPU.
+        #     "recompute" — KV cache is recomputed from scratch.
+        # ----------------------------------------------------------------
+
+        # Persisting CGs only makes sense if we build any CGs.
+        assert not args.rl_persist_cuda_graphs or args.cuda_graph_impl != "none", (
+            "--rl-persist-cuda-graphs is set but no CUDA graphs are being built."
+        )
+        # Training CGs only makes sense if we build any CGs.
+        assert not args.rl_training_cuda_graphs or args.cuda_graph_impl != "none", (
+            "--rl-training-cuda-graphs is set but no CUDA graphs are being built."
+        )
+        # If CUDA graphs persist and KV cache memory address is not static, we need
+        # either UVM or torch_memory_saver to maintain memory address stability for CGs.
+        if args.rl_persist_cuda_graphs and args.rl_kv_cache_management_mode != "persist":
+            try:
+                from torch_memory_saver import torch_memory_saver
+            except ImportError:
+                assert args.inference_dynamic_batching_unified_memory_level > 0, (
+                    "Persisting CUDA graphs requires static KV cache memory. Use "
+                    "--rl-kv-cache-management-mode=persist, UVM, or install torch_memory_saver."
+                )
 
-        assert not (args.rl_partial_rollouts and args.rl_remove_kv_cache_during_training), \
-            "Cannot use both partial-rollouts and remove-kv-cache-during-training"
+        # Offload mode requires CG persistence: CG recapture runs dummy forward
+        # passes that corrupt the preserved KV data.
+        assert (
+            (not args.rl_kv_cache_management_mode == "offload") or (args.rl_persist_cuda_graphs)
+        ), "--rl-kv-cache-management-mode=offload requires --rl-persist-cuda-graphs"
 
+        # There's no need to manually offload the KV cache with UVM.
         assert not (
-            args.rl_offload_inference_model_weights_when_idle
-            and args.rl_inference_model_unified_memory_level != 1
-        ), (
-            "--rl-offload-inference-model-weights-when-idle requires "
-            "--rl-inference-model-unified-memory-level=1."
-        )
+            args.inference_dynamic_batching_unified_memory_level > 0
+            and args.rl_kv_cache_management_mode == "offload"
+        ), "--rl-kv-cache-management-mode=offload is incompatible with UVM"
+        # We currently cannot recapture CGs in offload mode.
+        assert not(
+            not args.rl_persist_cuda_graphs and args.rl_kv_cache_management_mode == "offload"
+        ), "Cannot recapture CUDA graphs while offloading KV cache."
+
+        # Validate inference model offloading - requires either UVM or torch_memory_saver
+        if args.rl_offload_inference_model_weights_when_idle:
+            if args.rl_inference_model_unified_memory_level != 1:
+                # Not using UVM, so we need torch_memory_saver
+                try:
+                    from torch_memory_saver import torch_memory_saver
+                except ImportError:
+                    raise AssertionError(
+                        "To use --rl-offload-inference-model-weights-when-idle without UVM "
+                        "(--rl-inference-model-unified-memory-level=1), `torch_memory_saver` must be "
+                        "installed. See https://github.com/fzyzcjy/torch_memory_saver."
+                    )
 
         # When using different EP sizes for inference and training (EP refit), the legacy
         # GroupedMLP is not supported. Only SequentialMLP or TEGroupedMLP can be used.
@@ -499,6 +554,79 @@ def validate_args(args, defaults={}):
         print_rank_0('setting global batch size to {}'.format(args.global_batch_size))
     assert args.global_batch_size > 0
 
+    # === MTP validation ===
+    # Deprecation warnings for legacy MTP arguments
+    if args.mtp_hybrid_override_pattern is not None:
+        warn_rank_0(
+            "--mtp-hybrid-override-pattern is deprecated. "
+            "For new hybrid models with MTP models, use unified --hybrid-override-pattern instead. "
+            "Example: 'M*M*/MM/MM' means main='M*M*', MTP pattern='MM' with 2 depths. "
+            "This argument is kept only for loading old checkpoints.",
+            args.rank,
+        )
+
+    # Backward compatibility: convert legacy mtp_hybrid_override_pattern to unified format
+    from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, parse_hybrid_pattern
+    sep = Symbols.MTP_SEPARATOR
+    if (
+        getattr(args, 'mtp_hybrid_override_pattern', None) is not None
+        and args.mtp_num_layers is not None
+        and args.mtp_num_layers > 0
+        and (args.hybrid_override_pattern is None or sep not in args.hybrid_override_pattern)
+    ):
+        main_pattern = args.hybrid_override_pattern or ''
+        mtp_pattern = args.mtp_hybrid_override_pattern
+        args.hybrid_override_pattern = main_pattern + sep + sep.join([mtp_pattern] * args.mtp_num_layers)
+        args.mtp_hybrid_override_pattern = None
+        print_rank_0(f"Converted legacy MTP pattern to unified: {args.hybrid_override_pattern}")
+
+    # Infer mtp_num_layers from unified pattern
+    if args.hybrid_override_pattern and sep in args.hybrid_override_pattern:
+        parsed = parse_hybrid_pattern(args.hybrid_override_pattern)
+        if parsed.mtp_pattern and parsed.mtp_num_depths > 0:
+            inferred_mtp_num_layers = parsed.mtp_num_depths
+            if args.mtp_num_layers is None:
+                args.mtp_num_layers = inferred_mtp_num_layers
+            elif args.mtp_num_layers != inferred_mtp_num_layers:
+                warn_rank_0(
+                    f"--mtp-num-layers ({args.mtp_num_layers}) conflicts with "
+                    f"MTP depth count ({inferred_mtp_num_layers}) in pattern '{args.hybrid_override_pattern}'. "
+                    f"Using the inferred value ({inferred_mtp_num_layers}).",
+                    args.rank
+                )
+                args.mtp_num_layers = inferred_mtp_num_layers
+
+    # MTP validation
+    if args.mtp_num_layers:
+        assert not args.use_legacy_models, "The legacy Megatron models does not support Multi-Token Prediction (MTP)."
+        assert args.position_embedding_type == "rope" or args.position_embedding_type == "none", (
+            f"Multi-Token Prediction (MTP) is not supported with {args.position_embedding_type} position embedding type."
+            + f"The supported position embedding types are rope and none."
+        )
+
+    # Validate MTP args for hybrid vs non-hybrid models
+    if args.is_hybrid_model:
+        # Mamba/hybrid model MTP validation
+        if args.mtp_num_layers and not (args.hybrid_override_pattern and sep in args.hybrid_override_pattern):
+            # Hybrid model wants MTP but no unified pattern - check for legacy args
+            if args.mtp_hybrid_override_pattern is None:
+                warn_rank_0(
+                    "Hybrid model with --mtp-num-layers but no MTP pattern. "
+                    "Use unified --hybrid-override-pattern with '/' separator (e.g., 'M*M*/MM/MM') "
+                    "or legacy --mtp-hybrid-override-pattern for old checkpoints.",
+                    args.rank
+                )
+    else:
+        # Non-hybrid (GPT) model MTP validation
+        if args.mtp_hybrid_override_pattern is not None:
+            warn_rank_0(
+                "--mtp-hybrid-override-pattern is for Mamba/hybrid models only. "
+                "For GPT models, MTP replicates the main transformer layer structure. "
+                "This argument will be ignored.",
+                args.rank
+            )
+    # === End of MTP validation ===
+    
     # Uneven virtual pipeline parallelism
     assert (
         int(args.num_layers_per_virtual_pipeline_stage is not None)
@@ -668,6 +796,13 @@ def validate_args(args, defaults={}):
     if args.fp4 and not is_te_min_version("2.7.0.dev0"):
         raise ValueError("--fp4-format requires Transformer Engine >= 2.7.0.dev0 for NVFP4BlockScaling support.")
 
+    if (
+        args.fp8_recipe == 'mxfp8'
+        and args.transformer_impl == 'inference_optimized'
+        and not is_flashinfer_min_version("0.6.4")
+    ):
+        raise ValueError("MXFP8 with inference optimized layers requires FlashInfer >= 0.6.4")
+
     if args.use_megatron_fsdp:
         # NOTE: The flag `use_custom_fsdp` is deprecated and will be removed in future versions.
         #       Please use `use_megatron_fsdp` instead, as all functionality will be migrated there.
@@ -722,12 +857,17 @@ def validate_args(args, defaults={}):
             args.accumulate_allreduce_grads_in_fp32 = True
             print_rank_0('accumulate and all-reduce gradients in fp32 for bfloat16 data type.')
     if args.cuda_graph_impl == "local" and CudaGraphScope.full_iteration in args.cuda_graph_scope:
-        if not args.inference_dynamic_batching:
-            assert not args.check_for_nan_in_loss_and_grad, \
-            "--no-check-for-nan-in-loss-and-grad should be set with full_iteration CUDA graph"
-        else:
-            assert args.fp8 is None, \
-            "fp8 is not supported with inference dynamic batching and full_iteration CUDA graph"
+        assert not args.check_for_nan_in_loss_and_grad, \
+        "--no-check-for-nan-in-loss-and-grad should be set with --cuda-graph-scope=full_iteration for training. Note: If you are trying to use full_iteration CUDA graphs for inference, please use --cuda-graph-scope full_iteration_inference instead"
+    
+    if args.cuda_graph_impl == "local" and CudaGraphScope.full_iteration_inference in args.cuda_graph_scope:
+        assert args.fp8 is None, \
+            "fp8 is not supported with inference dynamic batching and full_iteration_inference CUDA graph"
+
+    if args.cuda_graph_impl == 'local':
+        assert args.inference_dynamic_batching_num_cuda_graphs > 0 or args.inference_dynamic_batching_num_cuda_graphs == -1, \
+            'inference_dynamic_batching_num_cuda_graphs should be a positive integer or -1' \
+            '-1 means that we will automatically determine the number of CUDA graphs to capture based on the `max_requests` value.'
 
     print_rank_0('using {} for parameters ...'.format(args.params_dtype))
 
@@ -1063,7 +1203,6 @@ def validate_args(args, defaults={}):
             args.fim_eod_token,
         ]
         assert not args.mock_data, "Mock dataset is not supported with FIM dataset."
-        assert not args.legacy_tokenizer, "FIM dataset is not supported with legacy tokenizers."
         assert args.fim_rate, "--fim-rate should be specified."
         assert args.fim_spm_rate, "--fim-spm-rate should be specified."
         assert all(token is not None for token in extra_tokens), "FIM extra tokens should be specified."
@@ -1131,6 +1270,13 @@ def validate_args(args, defaults={}):
         print('--dist-ckpt-format is deprecated and has no effect.'
               ' Use --ckpt-format to select the checkpoint format.')
 
+    if args.use_dist_ckpt and args.ckpt_fully_parallel_load:
+        if args.ckpt_fully_parallel_load_exchange_algo != "broadcast":
+            warn_rank_0(
+                "Currently only the 'broadcast' exchange algorithm is supported for fully parallel load. "
+                "Other algorithms cannot guarantee numerical stability yet."
+            )
+
     if args.load_main_params_from_ckpt:
         assert args.no_load_optim, '--load-main-params-from-ckpt must be used with --no-load-optim.'
 
@@ -1141,6 +1287,12 @@ def validate_args(args, defaults={}):
                 'Disabling --async-save.'
             )
             args.async_save = False
+        elif args.dist_ckpt_workers > 1:
+            warn_rank_0(
+                'async ckpt forks processes for parallel writing which may introduce '
+                'instability on checkpoints. Consider using --dist-ckpt-workers=1 in case of '
+                'issues.'
+            )
 
     # Inference args
     if args.inference_batch_times_seqlen_threshold > -1:
@@ -1251,9 +1403,11 @@ def validate_args(args, defaults={}):
 
     if args.mtp_num_layers:
         assert not args.use_legacy_models, "The legacy Megatron models does not support Multi-Token Prediction (MTP)."
-        assert args.position_embedding_type == "rope" or args.position_embedding_type == "none", (
-            f"Multi-Token Prediction (MTP) is not supported with {args.position_embedding_type} position embedding type."
-            + f"The supported position embedding types are rope and none."
+        # MTP is compatible with position embedding types that use position_ids.
+        supported_position_types = ["learned_absolute", "rope", "mrope", "none"]
+        assert args.position_embedding_type in supported_position_types, (
+            f"Multi-Token Prediction (MTP) is not supported with '{args.position_embedding_type}' position embedding type. "
+            f"The supported position embedding types are: {', '.join(supported_position_types)}."
         )
 
     if args.cpu_offloading_num_layers > 0:
@@ -1301,6 +1455,20 @@ def validate_args(args, defaults={}):
             "Use --tokenizer-special-tokens instead."
         )
         args.tokenizer_special_tokens = args.tiktoken_special_tokens
+    
+    if args.tokenizer_hf_use_fast:
+        warn_rank_0(
+            "--tokenizer-hf-use-fast argument is deprecated and will be removed soon. "
+            "`use_fast` is set to True by default for HF tokenizers."
+            "Use --tokenizer-hf-no-use-fast if you want to disable `use_fast`."
+        )
+
+    if args.tokenizer_hf_include_special_tokens:
+        warn_rank_0(
+            "--tokenizer-hf-include-special-tokens argument is deprecated and will be removed soon. "
+            "`include_special_tokens` is set to True by default for HF tokenizers."
+            "Use --tokenizer-hf-no-include-special-tokens if you want to disable `include_special_tokens`."
+        )
 
     # Print arguments.
     _print_args("arguments", args)
@@ -1388,6 +1556,9 @@ def core_transformer_config_from_args(args, config_class=None):
 
     kw_args['inference_sampling_seed'] = args.seed
 
+    kw_args['post_self_attn_layernorm'] = args.post_self_attn_layernorm
+    kw_args['post_mlp_layernorm'] = args.post_mlp_layernorm
+
     # handle quantization config
     # NOTE: Kitchen arguments are only added to the namespace when
     # Kitchen library is available.
@@ -1460,8 +1631,9 @@ def _add_inference_args(parser):
                        'including the shared experts if they are not overlapped with EP comm. '
                        '"moe_preprocess": captures operations in MoELayer.preprocess(). Must be used together with "moe_router". '
                        '"mamba": captures the mamba layer. '
-                       '"full_iteration": captures a whole iteration. '
-                       'full_iteration scope is only supported with --cuda-graph-impl=local, other scopes are only supported with --cuda-graph-impl=transformer_engine. '
+                       '"full_iteration": captures a whole training iteration. '
+                       '"full_iteration_inference": captures a whole inference iteration. '
+                       'full_iteration and full_iteration_inference scopes are only supported with --cuda-graph-impl=local, other scopes are only supported with --cuda-graph-impl=transformer_engine. '
                        'If not specified, the default scope is to capture the whole Transformer layer. '
                        'For backward compatibility, we still allow passing "full" to specify capturing the whole layer, and convert it to an empty list.')
     group.add_argument('--use-legacy-static-engine', action='store_true', default=False,
@@ -1469,13 +1641,10 @@ def _add_inference_args(parser):
                        dest='use_legacy_static_engine')
     group.add_argument('--inference-max-requests', type=int, default=8,
                        help='Maximum number of requests for inference.',
-                       dest='inference_max_batch_size')
+                       dest='inference_max_requests')
     group.add_argument('--inference-max-seq-length', type=int, default=2560,
                        help='Maximum sequence length expected for inference (prefill + decode).',
                        dest='inference_max_seq_length')
-    group.add_argument('--inference-max-batch-size', type=int, default=None,
-                       help='Maximum batch size for inference.',
-                       dest='inference_max_batch_size')
     group.add_argument('--inference-dynamic-batching',
                        action='store_true', default=False,
                        help='Enable dynamic batching mode.')
@@ -1497,6 +1666,10 @@ def _add_inference_args(parser):
                        'the dynamic inference context. Active requests are '
                        'paused when there are not enough active blocks available '
                        'to continue generating a request.')
+    group.add_argument('--inference-dynamic-batching-mamba-memory-ratio', type=float, default=None,
+                       help='Percentage of memory buffer to allocate for Mamba states. '
+                       'If not specified, allocates Mamba state tensors for each KV cache block. '
+                       'Only used for hybrid models.')
     group.add_argument('--inference-dynamic-batching-block-size',
                        type=int, default=256,
                        help='KV cache block size. '
@@ -1515,12 +1688,19 @@ def _add_inference_args(parser):
                        'cuda graph batch sizes range from 1 to `max_requests`. '
                        '(See `dynamic_context.py` for details on how '
                        '`max_requests` is computed). Due to rounding, the actual '
-                       'number of cuda graphs may not equal this argument.')
+                       'number of cuda graphs may not equal this argument.'
+                       'The user can also pass -1, in which case we automatically determine the number of graphs ' \
+                       'to capture based on the `max_requests`.')
     group.add_argument('--inference-dynamic-batching-track-paused-request-events',
                        action='store_true',
                        help='Track paused request ids by adding \'paused\' events '
                        'to each request\'s event history. This has a very minor '
                        'impact on latency.')
+    group.add_argument('--inference-dynamic-batching-track-generated-token-events',
+                       action='store_true',
+                       help='Track per-token events with timestamps for each generated token. '
+                       'When enabled, each generated token creates a GENERATED_TOKEN event '
+                       'with a timestamp, useful for per-token latency analysis.')
     group.add_argument('--decode-only-cuda-graphs',
                        action='store_true', default=False,
                        help='Only use cuda graphs for decode-only steps, not prefill and mixed steps.')
@@ -1531,15 +1711,10 @@ def _add_inference_args(parser):
                        '1) allocate `memory_buffer` in unified memory. '
                        'Eventually, additional levels will be included to '
                        'control other tensors within the context.')
-    group.add_argument('--nccl-all-reduce-for-prefill',
-                       action='store_true', default=False,
-                       help='When using symmeric all reduce kernels this will use regular nccl kernels for prefill. This can be more effecient when prefill is large as the nccl kernels can be more bandwith optimized')
     # TODO(ksanthanam): Clean this up in future PR
-    group.add_argument('--enable-chunked-prefill', dest='disable_chunked_prefill',
-                       action='store_false', default=True,
+    group.add_argument('--enable-chunked-prefill', dest='enable_chunked_prefill',
+                       action='store_true', default=False,
                        help="Enable chunked prefill (disabled by default)")
-    group.add_argument('--disable-chunked-prefill', dest='disable_chunked_prefill',
-                       action='store_true', help=argparse.SUPPRESS)
     group.add_argument('--inference-dynamic-batching-cuda-graph-max-tokens',
                        type=int, default=16384,
                        help='Maximum number of tokens to capture in a cuda graph.')
@@ -1549,9 +1724,12 @@ def _add_inference_args(parser):
     group.add_argument('--inference-logging-step-interval', type=int, default=0,
                        help='Step interval for logging inference metrics. '
                             'Default to 0 to disable inference logging.')
+    group.add_argument('--inference-flask-server-logging', action=argparse.BooleanOptionalAction,
+                       required=False, default=False,
+                       help='Enable per-request logging in the Flask inference server.')
     group.add_argument('--inference-wandb-logging', action=argparse.BooleanOptionalAction,
                        required=False, default=False, help='Enable inference wandb logging.')
-    group.add_argument("--inference-coordinator-port", type=int, default=12346,
+    group.add_argument("--inference-coordinator-port", type=int,
                        help="This port will be used to setup the inference coordinator on node-0")
     return parser
 
@@ -1915,12 +2093,6 @@ def _add_rl_args(parser):
                        help="Entropy term weight in GRPO loss.")
     group.add_argument('--grpo-filter-groups-with-same-reward', action='store_true',
                        help="Filter groups with same reward.")
-    group.add_argument('--langrl-inference-server-type', type=str,
-                       choices=['inplace_megatron', 'inplace_megatron_chat'], default='inplace_megatron',
-                       help="Type of inference server to use.")
-    group.add_argument('--langrl-inference-server-conversation-template', type=str, default=None,
-                       help="Conversation template, if using a chat server.")
-    group.add_argument('--langrl-external-server', action=argparse.BooleanOptionalAction, required=False, default=False)
     group.add_argument('--langrl-env-config', type=str, default=None,
                        help="Path to YAML config file for RL environment configuration.")
     group.add_argument('--rl-default-temperature', type=float, default=1.0,
@@ -1931,20 +2103,21 @@ def _add_rl_args(parser):
                        help="Default top-k for model inference.")
     group.add_argument('--rl-offload-optimizer-during-inference', action='store_true',
                        help='Offload optimizer state to CPU during inference/rollout to save GPU memory')
-    group.add_argument('--rl-offload-kv-cache-during-training', action=argparse.BooleanOptionalAction, default=False,
-                       help='Offload KV cache to CPU during training to save GPU memory')
-    group.add_argument('--rl-remove-kv-cache-during-training', action=argparse.BooleanOptionalAction, default=False,
-                       help='Remove KV cache during training to save GPU memory')
-    group.add_argument('--rl-reset-cuda-graphs', action=argparse.BooleanOptionalAction, type=bool, default=False,
-                       help='Reset CUDA graphs between inference/training to save GPU memory')
+    group.add_argument('--rl-kv-cache-management-mode', type=str, default='persist',
+                       choices=['persist', 'offload', 'recompute'],
+                       help='KV cache management mode during RL training: '
+                            'persist: leave KV cache in GPU memory (default), '
+                            'offload: offload KV cache to CPU during training, '
+                            'recompute: deallocate KV cache and recompute from scratch each cycle')
+    group.add_argument('--rl-persist-cuda-graphs', action=argparse.BooleanOptionalAction, type=bool, default=True,
+                       help='Persist CUDA graphs when the inference engine is suspended. '
+                            'If False, CUDA graphs are deleted on suspend and re-captured on resume.')
     group.add_argument('--rl-partial-rollouts', action=argparse.BooleanOptionalAction, default=False,
                        help='If set, use partial rollouts.')
     group.add_argument('--rl-inference-logprobs-is-correction', action=argparse.BooleanOptionalAction, type=bool, default=False,
                        help='If set, use inference logprobs in importance sampling correction of the loss.')
     group.add_argument('--rl-importance-sampling-truncation-coef', type=float, default=None,
                        help="If --inference-logprobs-is-correction is on and this coefficient is set, apply truncation for the IS correction at GRPO loss.")
-    group.add_argument('--rl-calculate-intra-group-similarity', action=argparse.BooleanOptionalAction, default=False,
-                       help='If set, calculate the intra-group similarity of rollouts.')
     group.add_argument('--rl-use-sequence-packing', action=argparse.BooleanOptionalAction, type=bool, default=False,
                        help='Enable sequence packing')
     group.add_argument('--rl-sequence-packing-max-sequences-per-bin', type=int, default=50,
@@ -1956,7 +2129,7 @@ def _add_rl_args(parser):
                             'round-robin: distribute bins cyclically across ranks for better load balancing')
     group.add_argument('--rl-training-cuda-graphs', action=argparse.BooleanOptionalAction, type=bool,
                        default=False,
-                       help='If set, do not call `delete_cuda_graphs` or `toggle_cuda_graphs` when the inference engine is suspended.')
+                       help='If set, do not toggle CUDA graphs on/off between inference and training phases.')
     group.add_argument('--rl-inference-tensor-model-parallel-size', type=int, default=None,
                        help='Degree of tensor model parallelism for inference for RL.')     
     group.add_argument(
@@ -1997,17 +2170,18 @@ def _add_rl_args(parser):
         required=False,
         default=False,
         help=(
-            'When using a separate RL inference model with UVM-enabled parameters, prefetch its weights '
-            'to CPU when not doing rollout inference, and prefetch back to GPU right before inference. '
-            'Requires --rl-inference-model-unified-memory-level=1.'
+            'When using a separate RL inference model, offload its weights to CPU when not doing rollout '
+            'inference, and restore to GPU right before inference. Works with two backends: '
+            '1) UVM (when --rl-inference-model-unified-memory-level=1), or '
+            '2) torch_memory_saver (when UVM is not enabled; requires torch_memory_saver to be installed).'
         ),
     )
     group.add_argument('--refit-method', type=str, default='gloo',
-                       choices=['nccl', 'gloo'],
+                       choices=['nccl', 'gloo', 'nvshmem'],
                        help=('Method to refit the model weights between training and inference models during RL. '
                              'nccl: use NCCLCopyService to refit using NCCL; '
                              'gloo: use GlooCopyService over CPU; '
-                             ))
+                             'nvshmem: use NVSHMEMCopyService to refit using the NVSHMEM.'))
     group.add_argument('--rl-verify-model-weights-swap', action=argparse.BooleanOptionalAction, default=False,
                        help='If set, verify that the model weights were correctly transferred by comparing forward pass outputs on'
                        'the first swap of model weights.')
@@ -2042,9 +2216,6 @@ def _add_training_args(parser):
                        dest='check_for_large_grads')
     group.add_argument('--result-rejected-tracker-filename', type=str, default=None,
                        help='Optional name of file tracking `result_rejected` events.')
-    group.add_argument('--disable-gloo-process-groups', action='store_false',
-                       dest='enable_gloo_process_groups',
-                       help='Disables creation and usage of Gloo process groups.')
     group.add_argument('--tp-comm-overlap-cfg', type=str, default=None,
                        help='Config file when tp_comm_overlap is enabled.')
 
@@ -2125,10 +2296,6 @@ def _add_training_args(parser):
                        '--use-legacy-models to not use core models.')
     group.add_argument('--use-legacy-models', action='store_true',
                        help='Use the legacy Megatron models, not Megatron-Core models.')
-    group.add_argument('--high-priority-stream-groups', nargs='*', type=str, default=[],
-                       help='The communicator group names to use high priority streams.')
-    group.add_argument('--disable-jit-fuser', action='store_true',
-                       help='Disable the JIT fuser.')
 
     return parser
 
@@ -2199,6 +2366,10 @@ def _add_checkpointing_args(parser):
     group.add_argument('--dist-ckpt-format',
                        dest='dist_ckpt_format_deprecated',
                        help='Deprecated: see --ckpt-format.')
+    group.add_argument('--dist-ckpt-workers', type=int, default=1,
+                       help='Number of workers for distributed checkpointing. '
+                       'Only used for async save. '
+                       'If set to 1, the checkpointing is performed in a single process.')
     group.add_argument('--ckpt-fully-parallel-save', action='store_true',
                        dest='ckpt_fully_parallel_save_deprecated',
                        help='Deprecated: see --no-ckpt-fully-parallel-save.')
@@ -2237,7 +2408,10 @@ def _add_mixed_precision_args(parser):
 
 
 def _add_distributed_args(parser):
-    group = parser.add_argument_group(title='distributed')
+    from megatron.training.common_config import DistributedInitConfig
+
+    dist_init_factory = ArgumentGroupFactory(DistributedInitConfig)
+    group = dist_init_factory.build_group(parser, "distributed init")
 
     group.add_argument('--decoder-first-pipeline-num-layers',
                        type=int, default=None,
@@ -2265,20 +2439,8 @@ def _add_distributed_args(parser):
     group.add_argument('--no-overlap-p2p-communication', action='store_false',
                        help='overlap pipeline parallel communication with forward and backward chunks in 1F1B',
                        dest='overlap_p2p_comm')
-    group.add_argument('--distributed-backend', default='nccl',
-                       choices=['nccl', 'gloo'],
-                       help='Which backend to use for distributed training.')
-    group.add_argument('--distributed-timeout-minutes', type=int, default=10,
-                       help='Default timeout minutes for torch.distributed.')
-    group.add_argument('--distributed-timeout-seconds-after-init', type=int, default=None,
-                       help='Timeout seconds for process groups after initialization.'
-                            'This timeout is applied to all process groups after initialization.')
     group.add_argument('--overlap-grad-reduce', action='store_true',
                        default=False, help='If set, overlap DDP grad reduce.')
-    group.add_argument('--no-align-grad-reduce', action='store_false',
-                       help='If not set, all PP stages will launch gradient reduces simultaneously. '
-                       'Otherwise, each PP stage will independently launch as needed.',
-                       dest='align_grad_reduce')
     group.add_argument('--ddp-num-buckets', type=int, default=None,
                        help='Number of buckets for data-parallel communication')
     group.add_argument('--ddp-bucket-size', type=int, default=None,
@@ -2305,14 +2467,6 @@ def _add_distributed_args(parser):
     group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false',
                        help='If not set, use scatter/gather to optimize communication of tensors in pipeline.',
                        dest='scatter_gather_tensors_in_pipeline')
-    group.add_argument('--local-rank', type=int, default=int(os.getenv('LOCAL_RANK', '0')),
-                       help='local rank passed from distributed launcher.')
-    group.add_argument('--lazy-mpu-init', type=bool, required=False,
-                       help='If set to True, initialize_megatron() '
-                       'skips DDP initialization and returns function to '
-                       'complete it instead. Also turns on '
-                       '--use-cpu-initialization flag. This is for '
-                       'external DDP manager.' )
     group.add_argument('--use-distributed-optimizer', action='store_true',
                        help='Use distributed optimizer.')
     group.add_argument('--use-nccl-ub', action='store_true', dest='nccl_ub',
@@ -2325,18 +2479,9 @@ def _add_distributed_args(parser):
     group.add_argument('--fsdp-manual-registration', action='store_true', dest='fsdp_manual_registration',
                        default=False, help='Manually register the FSDP communication buffers to NCCL user buffer.'
                        'This option is only effective when use-megatron-fsdp and use-nccl-ub is set.')
-    group.add_argument('--use-sharp', action='store_true', 
-                       help='Required to enable SHARP communication.')
-    group.add_argument('--sharp-enabled-group', type=str, default=None,
-                       choices=['dp', 'dp_replica'],
-                       help='IB SHARP can be enabled from only one communication group. '
-                       'By default, it is enabled from dp group. '
-                       'Available options: [dp, dp_replica]')
     group.add_argument('--create-all-gather-group', action='store_true',
                    help='Create a separate process group for all-gather operations '
                    'to overlap reduce-scatter and all-gather operations.')
-    group.add_argument('--use-megatron-fsdp', action='store_true',
-                       help='Use the Megatron FSDP code path in DDP.')
     group.add_argument('--data-parallel-sharding-strategy', type=str, default='no_shard',
                        choices=['no_shard', 'optim', 'optim_grads', 'optim_grads_params'],
                        help='Sharding strategy of data parallelism.')
@@ -2365,9 +2510,6 @@ def _add_distributed_args(parser):
                        help='If set, enable full sharding in megatron-fsdp Hybrid Sharded Data Parallel (HSDP) mode.')
     group.add_argument('--num-distributed-optimizer-instances', type=int, default=1,
                        help='Number of Distributed Optimizer copies across Data Parallel domain.')
-    group.add_argument('--use-torch-fsdp2', action='store_true',
-                       help='Use the torch FSDP2 implementation. FSDP2 has not been tested with pipeline parallelism, '
-                       'and may contain bugs.')
     group.add_argument('--torch-fsdp2-no-reshard-after-forward', action='store_false', dest='torch_fsdp2_reshard_after_forward',
                        help='Whether to reshard weights after forward pass when using PyTorch FSDP2. '
                        'Set to enable FSDP ZeRO-2.')
@@ -2377,14 +2519,6 @@ def _add_distributed_args(parser):
                        'all layers will share the same communication type. Users can also '
                        'specify separated types for each layer like '
                        '--cp-comm-type p2p p2p a2a a2a a2a+p2p a2a+p2p')
-    group.add_argument('--nccl-communicator-config-path', type=str, default=None,
-                       help='Path to the yaml file with NCCL communicator '
-                       'configurations. The number of min/max thread groups and thread '
-                       'group cluster size of each communicator can be configured by '
-                       'setting `min_ctas`, `max_ctas`, and `cga_cluster_size`.')
-    group.add_argument('--use-tp-pp-dp-mapping', action='store_true', default=False,
-                        help='If set, distributed ranks initialize order is changed '
-                        'from tp-cp-ep-dp-pp to tp-cp-ep-pp-dp.')
     group.add_argument('--fake-process-group', action='store_true', default=False,
                        help='If set, initialize with fake distributed process group and all distributed communication operations will be skipped. \
                        This is quite useful for profiling memory usage of distributed training with just one GPU. \
@@ -2438,8 +2572,6 @@ def _add_tokenizer_args(parser):
     group.add_argument('--tokenizer-special-tokens', type=str, nargs='+', default=None,
                        help='List of special tokens. For TikTokenizer needs to have '
                             '["<unk>", "<s>", "</s>", "<mask>", "<pad>", "<cls>", "<sep>"]')
-    group.add_argument('--legacy-tokenizer', action='store_true', default=False,
-                       help='To use Megatron-LM legacy tokenizer system.')
     group.add_argument('--tiktoken-pattern', type=str, default=None,
                        help='Which tiktoken pattern to use. Options: [v1, v2]')
     group.add_argument('--tiktoken-num-special-tokens', type=int, default=1000,
@@ -2449,10 +2581,14 @@ def _add_tokenizer_args(parser):
                             '["<unk>", "<s>", "</s>", "<mask>", "<pad>", "<cls>", "<sep>"]')
     group.add_argument('--tokenizer-sentencepiece-legacy', action='store_true', default=False,
                        help='SentencePiece tokenizer wrapper legacy behavior. Allows special tokens usage.')
-    group.add_argument('--tokenizer-hf-use-fast', action='store_true', default=False,
+    group.add_argument('--tokenizer-hf-use-fast', action='store_true', default=True,
                        help='Whether to use fast HuggingFace tokenizer.')
-    group.add_argument('--tokenizer-hf-include-special-tokens', action='store_true', default=False,
+    group.add_argument('--tokenizer-hf-include-special-tokens', action='store_true', default=True,
                        help='Converting text to ids will include special for HuggingFace tokenizer.')
+    group.add_argument('--tokenizer-hf-no-use-fast', action='store_true', default=False,
+                       help='Whether to use fast HuggingFace tokenizer.')
+    group.add_argument('--tokenizer-hf-no-include-special-tokens', action='store_true', default=False,
+                       help='Converting text to ids will not include special for HuggingFace tokenizer.')
     group.add_argument("--trust-remote-code", action="store_true", default=False,
                        help='Whether or not to allow PreTrainedTokenizer to execute remote code')
     return parser
@@ -2747,10 +2883,6 @@ def _add_moe_args(parser):
     group.add_argument('--moe-upcycling-granularity', type=int, default=1,
                        help='This param sepecifics how many times smaller is the expert hidden size compared with the original dense FFN hidden size. '
                        'For using granular upcycling strategy, please set this param as a positive integer. If this param is set to 1, it means using the default upcycling strategy.')
-    group.add_argument('--moe-pad-experts-for-cuda-graph-inference', action='store_true',
-                       help="some MoE routers have a D2H sync that will break cuda graphs.  If this flag is set the router will switch" \
-                       " to dropping and padding during decode time which does not have a D2H sync. The capacity factor is set to the" \
-                       " max that an expert could see during inference so no tokens are actually dropped.")
     return parser
 
 def _add_mla_args(parser):
diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py
index 7c87eca191a..a64d0cd318c 100644
--- a/megatron/training/checkpointing.py
+++ b/megatron/training/checkpointing.py
@@ -616,6 +616,13 @@ def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, num_floati
                 save_strategy = get_default_save_sharded_strategy(args.ckpt_format)
                 if args.ckpt_assume_constant_structure and args.ckpt_format == 'torch_dist':
                     save_strategy.use_cached_ckpt_structure = args.ckpt_assume_constant_structure
+                    if args.async_save:
+                        save_strategy.thread_count = args.dist_ckpt_workers
+                    else:
+                        # We don't allow per-rank parallel save for sync save
+                        logger.warning('Per-rank parallel save is not supported for sync save. '
+                                       'Setting args.dist_ckpt_workers to 1')
+                        save_strategy.thread_count = 1
                     if checkpointing_context is not None and 'load_strategy' in checkpointing_context:
                         cached_global_metadata = getattr(checkpointing_context['load_strategy'], 'cached_global_metadata', None)
                         if cached_global_metadata is not None:
@@ -625,7 +632,11 @@ def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, num_floati
                             logger.debug("Failed to plug in the read metadata from the load strategy...")
 
                 if args.ckpt_fully_parallel_save:
-                    save_strategy = FullyParallelSaveStrategyWrapper(save_strategy, mpu.get_data_parallel_group(with_context_parallel=True),
+                    if args.ckpt_fully_parallel_save_process_group == 'dp':
+                        process_group = mpu.get_data_parallel_group(with_context_parallel=True)
+                    elif args.ckpt_fully_parallel_save_process_group == 'ep_dp':
+                        process_group = mpu.get_expert_data_parallel_group()
+                    save_strategy = FullyParallelSaveStrategyWrapper(save_strategy, process_group,
                                                                      args.ckpt_assume_constant_structure)
             # Store save strategy for future checkpoint saves
             if checkpointing_context is not None:
@@ -1122,8 +1133,15 @@ def _load_global_dist_base_checkpoint(
     load_strategy = get_default_load_sharded_strategy(checkpoint_name)
     # NOTE: `args.ckpt_fully_parallel_load` applies to both persistent and non-persistent checkpoints.
     if args.ckpt_fully_parallel_load:
+        if args.ckpt_fully_parallel_load_process_group == 'dp':
+            process_group = mpu.get_data_parallel_group(with_context_parallel=True)
+        elif args.ckpt_fully_parallel_load_process_group == 'ep_dp':
+            process_group = mpu.get_expert_data_parallel_group()
+        else:
+            raise ValueError(f"Invalid load process group: {args.ckpt_fully_parallel_load_process_group}")
+
         load_strategy = FullyParallelLoadStrategyWrapper(
-            load_strategy, mpu.get_data_parallel_group(with_context_parallel=True)
+            load_strategy, process_group, exchange_algo=args.ckpt_fully_parallel_load_exchange_algo
         )
     if checkpointing_context is not None:
         checkpointing_context["load_strategy"] = load_strategy
@@ -1418,6 +1436,12 @@ def _set_arg(arg_name, old_arg_name=None, force=False):
     _set_arg('hidden_dropout', force=True)
 
     _set_arg('hybrid_override_pattern', force=True)
+
+    # Legacy MTP pattern for old checkpoints
+    _set_arg('mtp_hybrid_override_pattern', force=True)
+    _set_arg('mtp_num_layers', force=True)
+    _set_arg('mtp_use_repeated_layer', force=True)
+
     _set_arg('spec', force=True)
     _set_arg('hybrid_attention_ratio', force=True)
     _set_arg('hybrid_mlp_ratio', force=True)
@@ -1452,8 +1476,8 @@ def _set_arg(arg_name, old_arg_name=None, force=False):
     _set_arg('moe_latent_size', force=True)
 
     # Tokenizer args.
-    # Using checkpoint version might not always be safe (e.g., if running on different cluster).
     if args.use_tokenizer_model_from_checkpoint_args:
+        # Using checkpoint version might not always be safe (e.g., if running on different cluster).
         _set_arg('tokenizer_model', force=True)
         _set_arg('tokenizer_type', force=True)
     _set_arg('tiktoken_pattern', force=True)
diff --git a/megatron/training/common_config.py b/megatron/training/common_config.py
index d1096e91154..eb7e6313dec 100644
--- a/megatron/training/common_config.py
+++ b/megatron/training/common_config.py
@@ -1,5 +1,7 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 from dataclasses import dataclass, field
+from typing import Literal
+import os
 
 @dataclass(kw_only=True)
 class RNGConfig:
@@ -39,7 +41,16 @@ class ProfilingConfig:
     use_pytorch_profiler: bool = False
     """Use the built-in pytorch profiler. Useful if you wish to view profiles in tensorboard."""
 
-    profile_ranks: list[int] = field(default_factory=lambda: [0])
+    pytorch_profiler_collect_shapes: bool = False
+    """Collect tensor shape in pytorch profiler."""
+  
+    pytorch_profiler_collect_callstack: bool = False
+    """Collect callstack in pytorch profiler."""
+  
+    pytorch_profiler_collect_chakra: bool = False                
+    """Collect chakra trace in pytorch profiler."""
+
+    profile_ranks: list[int] = field(default_factory=lambda: [])
     """Global ranks to profile."""
 
     record_memory_history: bool = False
@@ -54,3 +65,71 @@ class ProfilingConfig:
     nvtx_ranges: bool = False
     """Enable NVTX range annotations for profiling. When enabled, inserts NVTX markers
     to categorize execution in profiler output."""
+
+
+@dataclass(kw_only=True)
+class DistributedInitConfig:
+    """Configuration settings for distributed training initialization."""
+
+    distributed_backend: Literal["nccl", "gloo"] = "nccl"
+    """Which backend to use for distributed training."""
+
+    distributed_timeout_minutes: int = 10
+    """Timeout minutes for torch.distributed."""
+
+    align_grad_reduce: bool = True
+    """If not set, all PP stages will launch gradient reduces simultaneously.
+    Otherwise, each PP stage will independently launch as needed.
+    """
+
+    local_rank: int = field(default_factory=lambda: int(os.getenv("LOCAL_RANK", "0")))
+    """local rank passed from distributed launcher."""
+
+    lazy_mpu_init: bool = False
+    """If set to True, initialize_megatron() skips DDP initialization and returns function to complete it instead.
+    Also turns on --use-cpu-initialization flag. This is for external DDP manager."""
+
+    use_megatron_fsdp: bool = False
+    """Use Megatron's Fully Sharded Data Parallel. Cannot be used together with use_torch_fsdp2."""
+
+    use_torch_fsdp2: bool = False
+    """Use the torch FSDP2 implementation. FSDP2 is not currently working with Pipeline Parallel.
+    It is still not in a stable release stage, and may therefore contain bugs or other
+    potential issues."""
+
+    nccl_communicator_config_path: str | None = None
+    """Path to the yaml file with NCCL communicator configurations. The number of min/max thread
+    groups and thread group cluster size of each communicator can be configured by setting
+    `min_ctas`, `max_ctas`, and `cga_cluster_size`."""
+
+    use_tp_pp_dp_mapping: bool = False
+    """If set, distributed ranks initialize order is changed from tp-cp-ep-dp-pp to tp-cp-ep-pp-dp.
+    """
+
+    enable_gloo_process_groups: bool = field(default=True, metadata={"argparse_meta": {"arg_names": ["--disable-gloo-process-groups"]}})
+    """If enabled, create Gloo process groups for communications."""
+
+    use_sharp: bool = False
+    """Set the use of SHARP for the collective communications of data-parallel process groups.
+    When `True`, run barrier within each data-parallel process group,
+    which specifies the SHARP application target groups.
+    """
+
+    sharp_enabled_group: Literal["dp", "dp_replica"] | None = None
+    """IB SHARP can be enabled from only one communication group.
+    By default, it is enabled from dp group if not specified and use_sharp=True.
+    Available options: [dp, dp_replica]
+    """
+
+    high_priority_stream_groups: list[str] | None = field(default_factory=list)
+    """Specify which communicator groups should use high priority streams during creation.
+    Assigning high priority to communication streams ensures that communication kernels
+    are scheduled with higher priority, minimizing the exposed communication when it is
+    overlapped with other computation kernels.
+    """
+
+    distributed_timeout_seconds_after_init: int | None = None
+    """Timeout in seconds for process groups after initialization. This timeout is applied to all process groups after initialization and the first iteration completes."""
+
+    disable_jit_fuser: bool = False
+    """Disable the JIT fuser."""
diff --git a/megatron/training/datasets/fim_dataset.py b/megatron/training/datasets/fim_dataset.py
index 730b7e033a1..875f979c91b 100644
--- a/megatron/training/datasets/fim_dataset.py
+++ b/megatron/training/datasets/fim_dataset.py
@@ -249,7 +249,10 @@ def _permute(
         """
         if self.np_rng.binomial(1, fim_rate):  # sample bernoulli dist
 
-            contents = tokenizer._tokenizer.ids_to_text(sample)
+            # Use remove_special_tokens=True so character-level boundaries and re-tokenization
+            # are consistent; otherwise ids_to_text(..., None) keeps special tokens when
+            # include_special_tokens=True, changing contents and breaking e.g. split_sample.
+            contents = tokenizer._tokenizer.ids_to_text(sample, remove_special_tokens=True)
 
             # Do not apply FIM if the sample starts with no_fim_prefix
             if no_fim_prefix is not None and contents.startswith(no_fim_prefix):
diff --git a/megatron/training/datasets/sft_dataset.py b/megatron/training/datasets/sft_dataset.py
index 2cbc4e424eb..9de5d2a52fe 100644
--- a/megatron/training/datasets/sft_dataset.py
+++ b/megatron/training/datasets/sft_dataset.py
@@ -117,10 +117,6 @@ def extend_with_padding(tokens, targets, positions, pad_len):
             tokens_list = tokens.tolist()
             targets_list = targets.tolist()
 
-            # Add EOD, unless it's already present
-            if tokens_list[-1] != eod:
-                tokens_list.append(eod)
-                targets_list.append(eod)
 
             pack_tokens.extend(tokens_list)
             pack_targets.extend(targets_list)
@@ -142,19 +138,12 @@ def extend_with_padding(tokens, targets, positions, pad_len):
 
             # Handle any necessary truncation
             if len(pack_tokens) >= pack_length + 1:  # +1 here to account for later alignment
-                truncate_left_not_right = True  # TODO(duncan): plumb this switch in
-                if truncate_left_not_right:  # Retain existing eod
-                    max_body = pack_length
-                    pack_tokens = pack_tokens[-max_body:]
-                    pack_targets = pack_targets[-max_body:]
-                    pack_tokens.append(pad)
-                    pack_targets.append(pad)
-                else:  # Truncate right (need to add eod)
-                    max_body = pack_length - 1
-                    pack_tokens = pack_tokens[:max_body]
-                    pack_targets = pack_targets[:max_body]
-                    pack_tokens.extend([eod, pad])
-                    pack_targets.extend([eod, pad])
+                # Truncate on the right
+                max_body = pack_length
+                pack_tokens = pack_tokens[:max_body]
+                pack_targets = pack_targets[:max_body]
+                pack_tokens.append(pad)
+                pack_targets.append(pad)
                 pack_positions = pack_positions[:pack_length+1]
                 # Note len({pack_tokens, pack_targets, pack_positions}) should be pack_length + 1
                 cu_seqlens[-1] = len(pack_tokens) - 1
diff --git a/megatron/training/global_vars.py b/megatron/training/global_vars.py
index 76e8df7cee3..a3d41f5e45a 100644
--- a/megatron/training/global_vars.py
+++ b/megatron/training/global_vars.py
@@ -11,8 +11,8 @@
 from megatron.core.energy_monitor import EnergyMonitor
 from megatron.core.jit import disable_jit_fuser
 from megatron.core.num_microbatches_calculator import init_num_microbatches_calculator, unset_num_microbatches_calculator
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.training.dist_signal_handler import DistributedSignalHandler
-from megatron.training.tokenizer import build_tokenizer
 
 _GLOBAL_ARGS = None
 _GLOBAL_TOKENIZER = None
diff --git a/megatron/training/theoretical_memory_usage.py b/megatron/training/theoretical_memory_usage.py
index 8737015dfa4..7d4043b62d7 100644
--- a/megatron/training/theoretical_memory_usage.py
+++ b/megatron/training/theoretical_memory_usage.py
@@ -56,20 +56,23 @@ def compute_weight_and_optimizer_memory(args, verbose=False):
         mtp_num_moe_layers = 0
         mtp_num_dense_layers = 0
 
+    # RMSNorm does not have bias, but LayerNorm has.
+    norm_size = 1 if args.normalization == "RMSNorm" else 2
+
     if args.multi_latent_attention:
         assert not args.group_query_attention
         if args.q_lora_rank is None:
             q_term = args.hidden_size * args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim)
         else:
             ## q lora + rope + q norm
-            q_term = args.q_lora_rank * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim) + 1) 
+            q_term = args.q_lora_rank * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.qk_pos_emb_head_dim) + norm_size) 
         
         self_attn_term = (
             q_term
 
             ## kv lora + rope + kv norm
             + args.kv_lora_rank
-            * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.v_head_dim) + 1)
+            * (args.hidden_size + args.num_attention_heads * (args.qk_head_dim + args.v_head_dim) + norm_size)
             + args.hidden_size * args.qk_pos_emb_head_dim
 
             ## o proj
@@ -96,7 +99,7 @@ def compute_weight_and_optimizer_memory(args, verbose=False):
             # Dense MoE MLP.
             (args.ffn_hidden_size * gated_linear_multiplier)
             # Transformer layernorms.
-            + (2)
+            + norm_size
         )
         + self_attn_term
     )
@@ -109,12 +112,12 @@ def compute_weight_and_optimizer_memory(args, verbose=False):
             # Shared MoE MLP.
             + (shared_expert_ffn_hidden_size * gated_linear_multiplier)
             # Transformer layernorms.
-            + (2)
+            + norm_size
         )
         + self_attn_term
     )
     embedding_size = args.hidden_size * args.padded_vocab_size
-    final_layernorm = 2 * args.hidden_size
+    final_layernorm = norm_size * args.hidden_size
     if args.untie_embeddings_and_output_weights:
         num_parameters_in_embedding_layers = 2 * embedding_size
     else:
diff --git a/megatron/training/tokenizer/__init__.py b/megatron/training/tokenizer/__init__.py
deleted file mode 100644
index 59ceb33865a..00000000000
--- a/megatron/training/tokenizer/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-
-
-from .tokenizer import build_tokenizer
diff --git a/megatron/training/tokenizer/bert_tokenization.py b/megatron/training/tokenizer/bert_tokenization.py
deleted file mode 100644
index 642041e778e..00000000000
--- a/megatron/training/tokenizer/bert_tokenization.py
+++ /dev/null
@@ -1,431 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization classes."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import re
-import unicodedata
-import six
-
-
-def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
-    """Checks whether the casing config is consistent with the checkpoint name."""
-
-    # The casing has to be passed in by the user and there is no explicit check
-    # as to whether it matches the checkpoint. The casing information probably
-    # should have been stored in the bert_config.json file, but it's not, so
-    # we have to heuristically detect it to validate.
-
-    if not init_checkpoint:
-        return
-
-    m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
-    if m is None:
-        return
-
-    model_name = m.group(1)
-
-    lower_models = [
-        "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
-        "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
-    ]
-
-    cased_models = [
-        "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
-        "multi_cased_L-12_H-768_A-12"
-    ]
-
-    is_bad_config = False
-    if model_name in lower_models and not do_lower_case:
-        is_bad_config = True
-        actual_flag = "False"
-        case_name = "lowercased"
-        opposite_flag = "True"
-
-    if model_name in cased_models and do_lower_case:
-        is_bad_config = True
-        actual_flag = "True"
-        case_name = "cased"
-        opposite_flag = "False"
-
-    if is_bad_config:
-        raise ValueError(
-            "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
-            "However, `%s` seems to be a %s model, so you "
-            "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
-            "how the model was pre-training. If this error is wrong, please "
-            "just comment out this check." % (actual_flag, init_checkpoint,
-                                              model_name, case_name, opposite_flag))
-
-
-def convert_to_unicode(text):
-    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
-    if six.PY3:
-        if isinstance(text, str):
-            return text
-        elif isinstance(text, bytes):
-            return text.decode("utf-8", "ignore")
-        else:
-            raise ValueError("Unsupported string type: %s" % (type(text)))
-    elif six.PY2:
-        if isinstance(text, str):
-            return text.decode("utf-8", "ignore")
-        elif isinstance(text, unicode):
-            return text
-        else:
-            raise ValueError("Unsupported string type: %s" % (type(text)))
-    else:
-        raise ValueError("Not running on Python2 or Python 3?")
-
-
-def printable_text(text):
-    """Returns text encoded in a way suitable for print or `tf.logging`."""
-
-    # These functions want `str` for both Python2 and Python3, but in one case
-    # it's a Unicode string and in the other it's a byte string.
-    if six.PY3:
-        if isinstance(text, str):
-            return text
-        elif isinstance(text, bytes):
-            return text.decode("utf-8", "ignore")
-        else:
-            raise ValueError("Unsupported string type: %s" % (type(text)))
-    elif six.PY2:
-        if isinstance(text, str):
-            return text
-        elif isinstance(text, unicode):
-            return text.encode("utf-8")
-        else:
-            raise ValueError("Unsupported string type: %s" % (type(text)))
-    else:
-        raise ValueError("Not running on Python2 or Python 3?")
-
-
-def load_vocab(vocab_file):
-    """Loads a vocabulary file into a dictionary."""
-    vocab = collections.OrderedDict()
-    index = 0
-    with open(vocab_file, "r", encoding = "utf-8") as reader:
-        while True:
-            token = convert_to_unicode(reader.readline())
-            if not token:
-                break
-            token = token.strip()
-            vocab[token] = index
-            index += 1
-    return vocab
-
-
-def convert_by_vocab(vocab, items):
-    """Converts a sequence of [tokens|ids] using the vocab."""
-    output = []
-    for item in items:
-        output.append(vocab[item])
-    return output
-
-
-def convert_tokens_to_ids(vocab, tokens):
-    return convert_by_vocab(vocab, tokens)
-
-
-def convert_ids_to_tokens(inv_vocab, ids):
-    return convert_by_vocab(inv_vocab, ids)
-
-
-def whitespace_tokenize(text):
-    """Runs basic whitespace cleaning and splitting on a piece of text."""
-    text = text.strip()
-    if not text:
-        return []
-    tokens = text.split()
-    return tokens
-
-
-class FullTokenizer(object):
-    """Runs end-to-end tokenziation."""
-
-    def __init__(self, vocab_file, do_lower_case=True):
-        self.vocab = load_vocab(vocab_file)
-        self.inv_vocab = {v: k for k, v in self.vocab.items()}
-        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
-        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
-
-    def tokenize(self, text):
-        split_tokens = []
-        for token in self.basic_tokenizer.tokenize(text):
-            for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                split_tokens.append(sub_token)
-
-        return split_tokens
-
-    def convert_tokens_to_ids(self, tokens):
-        return convert_by_vocab(self.vocab, tokens)
-
-    def convert_ids_to_tokens(self, ids):
-        return convert_by_vocab(self.inv_vocab, ids)
-
-    @staticmethod
-    def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True):
-        """ Converts a sequence of tokens (string) in a single string. """
-
-        def clean_up_tokenization(out_string):
-            """ Clean up a list of simple English tokenization artifacts
-            like spaces before punctuations and abreviated forms.
-            """
-            out_string = (
-                out_string.replace(" .", ".")
-                    .replace(" ?", "?")
-                    .replace(" !", "!")
-                    .replace(" ,", ",")
-                    .replace(" ' ", "'")
-                    .replace(" n't", "n't")
-                    .replace(" 'm", "'m")
-                    .replace(" 's", "'s")
-                    .replace(" 've", "'ve")
-                    .replace(" 're", "'re")
-            )
-            return out_string
-
-        text = ' '.join(tokens).replace(' ##', '').strip()
-        if clean_up_tokenization_spaces:
-            clean_text = clean_up_tokenization(text)
-            return clean_text
-        else:
-            return text
-
-    def vocab_size(self):
-        return len(self.vocab)
-
-
-class BasicTokenizer(object):
-    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
-    def __init__(self, do_lower_case=True):
-        """Constructs a BasicTokenizer.
-
-        Args:
-          do_lower_case: Whether to lower case the input.
-        """
-        self.do_lower_case = do_lower_case
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text."""
-        text = convert_to_unicode(text)
-        text = self._clean_text(text)
-
-        # This was added on November 1st, 2018 for the multilingual and Chinese
-        # models. This is also applied to the English models now, but it doesn't
-        # matter since the English models were not trained on any Chinese data
-        # and generally don't have any Chinese data in them (there are Chinese
-        # characters in the vocabulary because Wikipedia does have some Chinese
-        # words in the English Wikipedia.).
-        text = self._tokenize_chinese_chars(text)
-
-        orig_tokens = whitespace_tokenize(text)
-        split_tokens = []
-        for token in orig_tokens:
-            if self.do_lower_case:
-                token = token.lower()
-                token = self._run_strip_accents(token)
-            split_tokens.extend(self._run_split_on_punc(token))
-
-        output_tokens = whitespace_tokenize(" ".join(split_tokens))
-        return output_tokens
-
-    def _run_strip_accents(self, text):
-        """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
-        output = []
-        for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
-                continue
-            output.append(char)
-        return "".join(output)
-
-    def _run_split_on_punc(self, text):
-        """Splits punctuation on a piece of text."""
-        chars = list(text)
-        i = 0
-        start_new_word = True
-        output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
-
-        return ["".join(x) for x in output]
-
-    def _tokenize_chinese_chars(self, text):
-        """Adds whitespace around any CJK character."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if self._is_chinese_char(cp):
-                output.append(" ")
-                output.append(char)
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-    def _is_chinese_char(self, cp):
-        """Checks whether CP is the codepoint of a CJK character."""
-        # This defines a "chinese character" as anything in the CJK Unicode block:
-        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-        #
-        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-        # despite its name. The modern Korean Hangul alphabet is a different block,
-        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-        # space-separated words, so they are not treated specially and handled
-        # like the all of the other languages.
-        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-            (cp >= 0x3400 and cp <= 0x4DBF) or  #
-            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-            (cp >= 0x2B820 and cp <= 0x2CEAF) or
-            (cp >= 0xF900 and cp <= 0xFAFF) or  #
-                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
-            return True
-
-        return False
-
-    def _clean_text(self, text):
-        """Performs invalid character removal and whitespace cleanup on text."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if cp == 0 or cp == 0xfffd or _is_control(char):
-                continue
-            if _is_whitespace(char):
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-
-class WordpieceTokenizer(object):
-    """Runs WordPiece tokenziation."""
-
-    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
-        self.vocab = vocab
-        self.unk_token = unk_token
-        self.max_input_chars_per_word = max_input_chars_per_word
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text into its word pieces.
-
-        This uses a greedy longest-match-first algorithm to perform tokenization
-        using the given vocabulary.
-
-        For example:
-          input = "unaffable"
-          output = ["un", "##aff", "##able"]
-
-        Args:
-          text: A single token or whitespace separated tokens. This should have
-            already been passed through `BasicTokenizer.
-
-        Returns:
-          A list of wordpiece tokens.
-        """
-
-        text = convert_to_unicode(text)
-
-        output_tokens = []
-        for token in whitespace_tokenize(text):
-            chars = list(token)
-            if len(chars) > self.max_input_chars_per_word:
-                output_tokens.append(self.unk_token)
-                continue
-
-            is_bad = False
-            start = 0
-            sub_tokens = []
-            while start < len(chars):
-                end = len(chars)
-                cur_substr = None
-                while start < end:
-                    substr = "".join(chars[start:end])
-                    if start > 0:
-                        substr = "##" + substr
-                    if substr in self.vocab:
-                        cur_substr = substr
-                        break
-                    end -= 1
-                if cur_substr is None:
-                    is_bad = True
-                    break
-                sub_tokens.append(cur_substr)
-                start = end
-
-            if is_bad:
-                output_tokens.append(self.unk_token)
-            else:
-                output_tokens.extend(sub_tokens)
-        return output_tokens
-
-
-def _is_whitespace(char):
-    """Checks whether `chars` is a whitespace character."""
-    # \t, \n, and \r are technically contorl characters but we treat them
-    # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
-
-
-def _is_control(char):
-    """Checks whether `chars` is a control character."""
-    # These are technically control characters but we count them as whitespace
-    # characters.
-    if char == "\t" or char == "\n" or char == "\r":
-        return False
-    cat = unicodedata.category(char)
-    if cat in ("Cc", "Cf"):
-        return True
-    return False
-
-
-def _is_punctuation(char):
-    """Checks whether `chars` is a punctuation character."""
-    cp = ord(char)
-    # We treat all non-letter/number ASCII as punctuation.
-    # Characters such as "^", "$", and "`" are not in the Unicode
-    # Punctuation class but we treat them as punctuation anyways, for
-    # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-        return True
-    cat = unicodedata.category(char)
-    if cat.startswith("P"):
-        return True
-    return False
diff --git a/megatron/training/tokenizer/gpt2_tokenization.py b/megatron/training/tokenizer/gpt2_tokenization.py
deleted file mode 100644
index 55b95b8ed9a..00000000000
--- a/megatron/training/tokenizer/gpt2_tokenization.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization classes for OpenAI GPT."""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import sys
-import json
-import logging
-import os
-import regex as re
-from io import open
-
-try:
-    from functools import lru_cache
-except ImportError:
-    # Just a dummy decorator to get the checks to run on python2
-    # because honestly I don't want to support a byte-level unicode BPE
-    # tokenizer on python 2 right now.
-    def lru_cache():
-        return lambda func: func
-
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
-    'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
-}
-PRETRAINED_MERGES_ARCHIVE_MAP = {
-    'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
-}
-PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
-    'gpt2': 1024,
-}
-VOCAB_NAME = 'vocab.json'
-MERGES_NAME = 'merges.txt'
-SPECIAL_TOKENS_NAME = 'special_tokens.txt'
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    _chr = unichr if sys.version_info[0] == 2 else chr
-    bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \
-        list(range(ord("®"), ord("ÿ") + 1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [_chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class GPT2Tokenizer(object):
-    """
-    GPT-2 BPE tokenizer. Peculiarities:
-        - Byte-level BPE
-    """
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
-        """
-        Instantiate a PreTrainedBertModel from a pre-trained model file.
-        Download and cache the pre-trained model file if needed.
-        """
-        if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
-            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
-            merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
-            special_tokens_file = None
-        else:
-            vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
-            merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
-            special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
-            if not os.path.exists(special_tokens_file):
-                special_tokens_file = None
-            else:
-                logger.info("loading special tokens file {}".format(special_tokens_file))
-        # redirect to the cache, if necessary
-        try:
-            from .file_utils import cached_path
-            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
-            resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
-        except EnvironmentError:
-            logger.error(
-                "Model name '{}' was not found in model name list ({}). "
-                "We assumed '{}' was a path or url but couldn't find files {} and {} "
-                "at this path or url.".format(
-                    pretrained_model_name_or_path,
-                    ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
-                    pretrained_model_name_or_path,
-                    vocab_file, merges_file))
-            return None
-        if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
-            logger.info("loading vocabulary file {}".format(vocab_file))
-            logger.info("loading merges file {}".format(merges_file))
-        else:
-            logger.info("loading vocabulary file {} from cache at {}".format(
-                vocab_file, resolved_vocab_file))
-            logger.info("loading merges file {} from cache at {}".format(
-                merges_file, resolved_merges_file))
-        if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
-            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
-            # than the number of positional embeddings
-            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
-            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
-        # Instantiate tokenizer.
-        if special_tokens_file and 'special_tokens' not in kwargs:
-            with open(special_tokens_file, encoding='utf-8') as f:
-                special_tokens = f.read().split('\n')[:-1]
-        else:
-            special_tokens = kwargs.pop('special_tokens', [])
-        tokenizer = cls(
-            resolved_vocab_file,
-            resolved_merges_file,
-            special_tokens=special_tokens,
-            *inputs,
-            **kwargs)
-        return tokenizer
-
-    def __init__(self, vocab_file, merges_file, errors='replace',
-                 special_tokens=None, max_len=None):
-        self.max_len = max_len if max_len is not None else int(1e12)
-        with open(vocab_file) as f:
-            self.encoder = json.load(f)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        with open(merges_file, encoding='utf-8') as f:
-            bpe_data = f.read().split('\n')[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-
-        # Should haved added re.IGNORECASE so BPE merges can happen for
-        # capitalized versions of contractions
-        self.pat = re.compile(
-            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-        self.special_tokens = {}
-        self.special_tokens_decoder = {}
-        self.set_special_tokens(special_tokens)
-
-    def __len__(self):
-        return len(self.encoder) + len(self.special_tokens)
-
-    def set_special_tokens(self, special_tokens):
-        """ Add a list of additional tokens to the encoder.
-            The additional tokens are indexed starting from the last index of the
-            current vocabulary in the order of the `special_tokens` list.
-        """
-        if not special_tokens:
-            self.special_tokens = {}
-            self.special_tokens_decoder = {}
-            return
-        self.special_tokens = dict((tok, len(self.encoder) + i)
-                                   for i, tok in enumerate(special_tokens))
-        self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
-        logger.info("Special tokens {}".format(self.special_tokens))
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except Exception:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def tokenize(self, text):
-        """ Tokenize a string. """
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            if sys.version_info[0] == 2:
-                token = ''.join(self.byte_encoder[ord(b)] for b in token)
-            else:
-                token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def convert_tokens_to_ids(self, tokens):
-        """ Converts a sequence of tokens into ids using the vocab. """
-        ids = []
-        if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
-            if tokens in self.special_tokens:
-                return self.special_tokens[tokens]
-            else:
-                return self.encoder.get(tokens, 0)
-        for token in tokens:
-            if token in self.special_tokens:
-                ids.append(self.special_tokens[token])
-            else:
-                ids.append(self.encoder.get(token, 0))
-        if len(ids) > self.max_len:
-            logger.warning(
-                "Token indices sequence length is longer than the specified maximum "
-                " sequence length for this OpenAI GPT model ({} > {}). Running this"
-                " sequence through the model will result in indexing errors".format(
-                    len(ids), self.max_len)
-            )
-        return ids
-
-    def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
-        """Converts a sequence of ids in BPE tokens using the vocab."""
-        tokens = []
-        for i in ids:
-            if i in self.special_tokens_decoder:
-                if not skip_special_tokens:
-                    tokens.append(self.special_tokens_decoder[i])
-            else:
-                tokens.append(self.decoder[i])
-        return tokens
-
-    def encode(self, text):
-        return self.convert_tokens_to_ids(self.tokenize(text))
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
-        return text
-
-    def save_vocabulary(self, vocab_path):
-        """Save the tokenizer vocabulary and merge files to a directory."""
-        if not os.path.isdir(vocab_path):
-            logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
-            return
-        vocab_file = os.path.join(vocab_path, VOCAB_NAME)
-        merge_file = os.path.join(vocab_path, MERGES_NAME)
-        special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
-
-        with open(vocab_file, 'w', encoding='utf-8') as f:
-            f.write(json.dumps(self.encoder, ensure_ascii=False))
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write(u'#version: 0.2\n')
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
-                                   " Please check that the tokenizer is not corrupted!".format(merge_file))
-                    index = token_index
-                writer.write(' '.join(bpe_tokens) + u'\n')
-                index += 1
-
-        index = len(self.encoder)
-        with open(special_tokens_file, 'w', encoding='utf-8') as writer:
-            for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
-                                   " Please check that the tokenizer is not corrupted!".format(special_tokens_file))
-                    index = token_index
-                writer.write(token + u'\n')
-                index += 1
-
-        return vocab_file, merge_file, special_tokens_file
diff --git a/megatron/training/tokenizer/tokenizer.py b/megatron/training/tokenizer/tokenizer.py
deleted file mode 100644
index 17df57ddacb..00000000000
--- a/megatron/training/tokenizer/tokenizer.py
+++ /dev/null
@@ -1,945 +0,0 @@
-# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
-
-"""Megatron tokenizers."""
-
-import base64
-import json
-import math
-import types
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Dict, List, Optional
-
-from megatron.core.datasets.megatron_tokenizer import MegatronLegacyTokenizer
-
-from .bert_tokenization import FullTokenizer as FullBertTokenizer
-from .gpt2_tokenization import GPT2Tokenizer
-from megatron.training.tokenizer.multimodal_tokenizer import MultimodalTokenizer
-from megatron.training.tokenizer.sft_tokenizer import SFTTokenizer
-
-
-def build_tokenizer(args, **kwargs):
-    """Initialize tokenizer."""
-    from megatron.training.utils import print_rank_0
-    print_rank_0('> building {} tokenizer ...'.format(args.tokenizer_type))
-
-    # Select and instantiate the tokenizer.
-    if args.tokenizer_type == 'BertWordPieceLowerCase':
-        assert args.vocab_file is not None
-        tokenizer = _BertWordPieceTokenizer(
-            vocab_file=args.vocab_file, lower_case=True, vocab_extra_ids=args.vocab_extra_ids
-        )
-    elif args.tokenizer_type == 'BertWordPieceCase':
-        assert args.vocab_file is not None
-        tokenizer = _BertWordPieceTokenizer(
-            vocab_file=args.vocab_file, lower_case=False, vocab_extra_ids=args.vocab_extra_ids
-        )
-    elif args.tokenizer_type == 'GPT2BPETokenizer':
-        assert args.vocab_file is not None
-        assert args.merge_file is not None
-        tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file)
-    elif args.tokenizer_type == 'SentencePieceTokenizer':
-        assert args.tokenizer_model is not None
-        tokenizer = _SentencePieceTokenizer(
-            args.tokenizer_model, vocab_extra_ids=args.vocab_extra_ids
-        )
-    elif args.tokenizer_type == 'GPTSentencePieceTokenizer':
-        assert args.tokenizer_model is not None
-        tokenizer = _GPTSentencePieceTokenizer(args.tokenizer_model)
-    elif args.tokenizer_type == 'HuggingFaceTokenizer':
-        tokenizer = _HuggingFaceTokenizer(
-            args.tokenizer_model, trust_remote_code=args.trust_remote_code, **kwargs
-        )
-    elif args.tokenizer_type == 'Llama2Tokenizer':
-        assert args.tokenizer_model is not None
-        tokenizer = _Llama2Tokenizer(args.tokenizer_model)
-    elif args.tokenizer_type == 'TikTokenizer':
-        assert args.tokenizer_model is not None
-        assert args.tiktoken_pattern is not None
-        assert args.tiktoken_pattern in {"v1", "v2"}
-        pattern = PATTERN_TIKTOKEN if args.tiktoken_pattern == "v1" else PATTERN_TIKTOKEN_V2
-        tokenizer = CustomTikTokenizer(
-            path=args.tokenizer_model,
-            pattern=pattern,
-            vocab_size=args.vocab_size,
-            num_special_tokens=args.tiktoken_num_special_tokens,
-            special_tokens=args.tokenizer_special_tokens,
-        )
-    elif args.tokenizer_type == 'NullTokenizer':
-        assert args.vocab_size is not None
-        tokenizer = _NullTokenizer(args.vocab_size)
-    elif args.tokenizer_type == "MultimodalTokenizer":
-        try:
-            import transformers
-        except ImportError:
-            raise ImportError(
-                "MultimodalTokenizer currently requires transformers library to be installed"
-            )
-
-        kwargs = dict()
-        if args.tokenizer_prompt_format == "nvlm-yi-34b":
-            kwargs = {"from_slow": True, "legacy": False, "add_bos_token": True}
-
-        # Currently, only HuggingFace tokenizers are supported.
-        underlying_tokenizer = transformers.AutoTokenizer.from_pretrained(
-            pretrained_model_name_or_path=args.tokenizer_model, **kwargs
-        )
-
-        tokenizer = MultimodalTokenizer(
-            underlying_tokenizer,
-            args.tokenizer_prompt_format,
-            args.special_tokens,
-            args.image_tag_type,
-            args.force_system_message,
-        )
-    elif args.tokenizer_type == "SFTTokenizer":
-        tokenizer = SFTTokenizer(args.tokenizer_model, args.sft_tokenizer_prompt_format)
-    elif args.tokenizer_type == 'NullMultimodalTokenizer':
-        assert args.vocab_size is not None
-        tokenizer = _NullMultimodalTokenizer(args.vocab_size)
-    else:
-        raise NotImplementedError('{} tokenizer is not ' 'implemented.'.format(args.tokenizer_type))
-
-    # Add vocab size (if not already set from a checkpoint).
-    if getattr(args, "padded_vocab_size", None) is None:
-        args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args)
-
-    return tokenizer
-
-
-def _vocab_size_with_padding(orig_vocab_size, args, logging_enabled=True):
-    """Pad vocab size so it is divisible by model parallel size and
-    still having GPU friendly size."""
-
-    after = orig_vocab_size
-    multiple = args.make_vocab_size_divisible_by * args.tensor_model_parallel_size
-    after = int(math.ceil(after / multiple) * multiple)
-    if args.rank == 0 and logging_enabled:
-        print(
-            ' > padded vocab (size: {}) with {} dummy tokens '
-            '(new size: {})'.format(orig_vocab_size, after - orig_vocab_size, after),
-            flush=True,
-        )
-    return after
-
-
-class _HuggingFaceTokenizer(MegatronLegacyTokenizer):
-    def __init__(self, pretrained_model_name_or_path, trust_remote_code=False, **kwargs):
-        super().__init__(pretrained_model_name_or_path, **kwargs)
-        try:
-            import transformers
-        except ImportError:
-            raise EnvironmentError(
-                f"The transformers library must be installed to use huggingface_tokenizer_provider"
-            )
-
-        # TODO(bnorick): download tokenizer once to lustre and use force offline to make sure all tasks read it from there
-        self._tokenizer = transformers.AutoTokenizer.from_pretrained(
-            pretrained_model_name_or_path=pretrained_model_name_or_path,
-            trust_remote_code=trust_remote_code,
-            **kwargs,
-        )
-        self._vocab = self._tokenizer.get_vocab()
-        self._inv_vocab = {token_id: token for token, token_id in self._vocab.items()}
-
-    @property
-    def vocab_size(self):
-        return len(self._tokenizer)
-
-    @property
-    def vocab(self):
-        """Dictionary from vocab text token to id token."""
-        return self._vocab
-
-    @property
-    def inv_vocab(self):
-        """Dictionary from vocab id token to text token."""
-        return self._inv_vocab
-
-    @property
-    def decoder(self):
-        return self._inv_vocab
-
-    def tokenize(self, text, **kwargs):
-        return self._tokenizer(text, **kwargs).input_ids
-
-    def detokenize(self, token_ids, **kwargs):
-        return self._tokenizer.decode(token_ids, **kwargs)
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        retok_ids: "transformers.BatchEncoding" = self._tokenizer(text)
-        offsets, next_start_idx = [], 0
-        for i in range(len(ids)):
-            span = retok_ids.token_to_chars(i)
-            if span is not None:
-                offsets.append(span.start)
-                next_start_idx = span.end
-            else:
-                offsets.append(next_start_idx)
-        return offsets
-
-    @property
-    def eod(self):
-        return self._tokenizer.eos_token_id
-
-    @property
-    def bos(self):
-        return self._tokenizer.bos_token_id
-
-    @property
-    def pad(self):
-        return self._tokenizer.pad_token_id
-
-
-class _BertWordPieceTokenizer(MegatronLegacyTokenizer):
-    """Original BERT wordpiece tokenizer."""
-
-    def __init__(self, vocab_file, lower_case=True, vocab_extra_ids=0):
-        super().__init__(vocab_file, lower_case=lower_case, vocab_extra_ids=vocab_extra_ids)
-        self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case)
-        self.cls_id = self.tokenizer.vocab['[CLS]']
-        self.sep_id = self.tokenizer.vocab['[SEP]']
-        self.pad_id = self.tokenizer.vocab['[PAD]']
-        self.mask_id = self.tokenizer.vocab['[MASK]']
-        self._additional_special_tokens = []
-
-        # (dsachan) Add BOS and EOS tokens
-        SPECIAL_TOKENS = {'eos_token': '[EOS]', 'bos_token': '[BOS]'}
-        self._bos_token = '[BOS]'
-        self.add_token(self._bos_token)
-        self._bos_token_id = self.vocab.get(self._bos_token)
-
-        self._eos_token = '[EOS]'
-        self.add_token(self._eos_token)
-        self._eos_token_id = self.vocab.get(self._eos_token)
-
-        # (dsachan) Add additional special tokens
-        # These can be used as sentinel tokens in T5 model inputs
-        additional_special_tokens = []
-        additional_special_tokens.extend(
-            ["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)]
-        )
-        self.add_additional_special_tokens(additional_special_tokens)
-
-    def add_token(self, token):
-        if token not in self.vocab:
-            self.inv_vocab[self.vocab_size] = token
-            # self.vocab_size comes from len(vocab)
-            # and it will increase as we add elements
-            self.vocab[token] = self.vocab_size
-
-    def add_additional_special_tokens(self, tokens_list):
-        setattr(self, "additional_special_tokens", tokens_list)
-        for value in tokens_list:
-            self.add_token(value)
-
-    @property
-    def vocab_size(self):
-        return self.tokenizer.vocab_size()
-
-    @property
-    def vocab(self):
-        return self.tokenizer.vocab
-
-    @property
-    def inv_vocab(self):
-        return self.tokenizer.inv_vocab
-
-    def tokenize(self, text):
-        text_tokens = self.tokenizer.tokenize(text)
-        return self.tokenizer.convert_tokens_to_ids(text_tokens)
-
-    def decode(self, ids):
-        tokens = self.tokenizer.convert_ids_to_tokens(ids)
-        return self.tokenizer.convert_tokens_to_string(tokens)
-
-    def detokenize(self, token_ids):
-        """Copy of decode() method for inference pipeline compatibility"""
-        return self.decode(token_ids)
-
-    def decode_token_ids(self, token_ids):
-        tokens = self.tokenizer.convert_ids_to_tokens(token_ids)
-        exclude_list = ['[PAD]', '[CLS]']
-        non_pads = [t for t in tokens if t not in exclude_list]
-
-        result = ""
-        for s in non_pads:
-            if s.startswith("##"):
-                result += s[2:]
-            else:
-                result += " " + s
-
-        return result
-
-    @property
-    def cls(self):
-        return self.cls_id
-
-    @property
-    def sep(self):
-        return self.sep_id
-
-    @property
-    def pad(self):
-        return self.pad_id
-
-    @property
-    def mask(self):
-        return self.mask_id
-
-    @property
-    def bos(self):
-        """Id of the beginning of sentence token in the vocabulary."""
-        return self._bos_token_id
-
-    @property
-    def eos(self):
-        """Id of the end of sentence token in the vocabulary."""
-        return self._eos_token_id
-
-    @property
-    def eod(self):
-        """Copy of eod property for inference pipeline compatibility"""
-        return self.eos
-
-    @property
-    def bos_token(self):
-        """Beginning of sentence token id"""
-        return self._bos_token
-
-    @property
-    def eos_token(self):
-        """End of sentence token id"""
-        return self._eos_token
-
-    @property
-    def additional_special_tokens(self):
-        """All the additional special tokens you may want to use (list of strings)."""
-        return self._additional_special_tokens
-
-    @property
-    def additional_special_tokens_ids(self):
-        """Ids of all the additional special tokens in the vocabulary (list of integers)."""
-        return [self.vocab.get(token) for token in self._additional_special_tokens]
-
-    @additional_special_tokens.setter
-    def additional_special_tokens(self, value):
-        self._additional_special_tokens = value
-
-
-class _GPT2BPETokenizer(MegatronLegacyTokenizer):
-    """Original GPT2 BPE tokenizer."""
-
-    def __init__(self, vocab_file, merge_file):
-        super().__init__(vocab_file, merge_file)
-
-        self.tokenizer = GPT2Tokenizer(
-            vocab_file, merge_file, errors='replace', special_tokens=[], max_len=None
-        )
-        self.eod_id = self.tokenizer.encoder['<|endoftext|>']
-
-    @property
-    def vocab_size(self):
-        return len(self.tokenizer.encoder)
-
-    @property
-    def vocab(self):
-        return self.tokenizer.encoder
-
-    @property
-    def inv_vocab(self):
-        return self.tokenizer.decoder
-
-    def tokenize(self, text):
-        return self.tokenizer.encode(text)
-
-    def detokenize(self, token_ids):
-        return self.tokenizer.decode(token_ids)
-
-    @property
-    def eod(self):
-        return self.eod_id
-
-    @property
-    def eos(self):
-        return self.eod_id
-
-
-class _SentencePieceTokenizer(MegatronLegacyTokenizer):
-    """SentencePieceTokenizer-Megatron wrapper"""
-
-    def __init__(self, model_file, vocab_extra_ids=0):
-        super().__init__(model_file, vocab_extra_ids=vocab_extra_ids)
-
-        import sentencepiece
-
-        self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file)
-        self._initalize(vocab_extra_ids)
-
-    def _populate_vocab(self):
-        self._vocab = {}
-        self._inv_vocab = {}
-
-        for i in range(len(self.tokenizer)):
-            t = self.tokenizer.id_to_piece(i)
-            self._inv_vocab[i] = t
-            self._vocab[t] = i
-
-    def _initalize(self, vocab_extra_ids):
-        self._populate_vocab()
-        self._special_tokens = {}
-        self._inv_special_tokens = {}
-
-        self._t5_tokens = []
-
-        def _add_special_token(t):
-            if t not in self._vocab:
-                next_id = len(self._vocab)
-                self._vocab[t] = next_id
-                self._inv_vocab[next_id] = t
-            self._special_tokens[t] = self._vocab[t]
-            self._inv_special_tokens[self._vocab[t]] = t
-
-        _add_special_token('<CLS>')
-        self._cls_id = self._vocab['<CLS>']
-        _add_special_token('<SEP>')
-        self._sep_id = self._vocab['<SEP>']
-        _add_special_token('<EOD>')
-        self._eod_id = self._vocab['<EOD>']
-        _add_special_token('<MASK>')
-        self._mask_id = self._vocab['<MASK>']
-
-        pad_id = self.tokenizer.pad_id()
-        try:
-            pad_token = self.tokenizer.id_to_piece(pad_id)
-        except IndexError:
-            pad_token = '<PAD>'
-        _add_special_token(pad_token)
-        self._pad_id = self._vocab[pad_token]
-
-        bos_id = self.tokenizer.bos_id()
-        try:
-            bos_token = self.tokenizer.id_to_piece(bos_id)
-        except IndexError:
-            bos_token = '<BOS>'
-        _add_special_token(bos_token)
-        self._bos_id = self._vocab[bos_token]
-
-        eos_id = self.tokenizer.eos_id()
-        try:
-            eos_token = self.tokenizer.id_to_piece(eos_id)
-        except IndexError:
-            eos_token = '<EOS>'
-        _add_special_token(eos_token)
-        self._eos_id = self._vocab[eos_token]
-
-        for i in range(vocab_extra_ids):
-            t = "<extra_id_{}>".format(i)
-            _add_special_token(t)
-            self._t5_tokens += [t]
-
-    @property
-    def vocab_size(self):
-        return len(self._vocab)
-
-    @property
-    def vocab(self):
-        return self._vocab
-
-    @property
-    def inv_vocab(self):
-        return self._inv_vocab
-
-    @property
-    def decoder(self):
-        return self._inv_vocab
-
-    @property
-    def encoder(self):
-        return self._vocab
-
-    # From:
-    # https://github.com/NVIDIA/NeMo/blob/c8fa217e811d60d11d014827c7f3845ff6c99ae7/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py#L89
-    def tokenize(self, text):
-        ids = []
-        idx = 0
-
-        while 1:
-            indices = {}
-            for token in self._special_tokens:
-                try:
-                    indices[token] = text[idx:].index(token)
-                except ValueError:
-                    continue
-            if len(indices) == 0:
-                break
-
-            next_token = min(indices, key=indices.get)
-            next_idx = idx + indices[next_token]
-
-            ids.extend(self.tokenizer.encode_as_ids(text[idx:next_idx]))
-            ids.append(self._special_tokens[next_token])
-            idx = next_idx + len(next_token)
-
-        ids.extend(self.tokenizer.encode_as_ids(text[idx:]))
-        return ids
-
-    # From:
-    # https://github.com/NVIDIA/NeMo/blob/c8fa217e811d60d11d014827c7f3845ff6c99ae7/nemo/collections/common/tokenizers/sentencepiece_tokenizer.py#L125
-    def detokenize(self, ids):
-        text = ""
-        last_i = 0
-
-        for i, id in enumerate(ids):
-            if id in self._inv_special_tokens:
-                text += self.tokenizer.decode_ids(ids[last_i:i]) + " "
-                text += self._inv_special_tokens[id] + " "
-                last_i = i + 1
-
-        text += self.tokenizer.decode_ids(ids[last_i:])
-        return text
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        return [p.begin for p in self.tokenizer.decode_ids_as_immutable_proto(ids).pieces]
-
-    @property
-    def cls(self):
-        return self._cls_id
-
-    @property
-    def sep(self):
-        return self._sep_id
-
-    @property
-    def pad(self):
-        return self._pad_id
-
-    @property
-    def bos(self):
-        return self._bos_id
-
-    @property
-    def eod(self):
-        return self._eod_id
-
-    @property
-    def eos(self):
-        return self._eos_id
-
-    @property
-    def mask(self):
-        return self._mask_id
-
-    @property
-    def additional_special_tokens_ids(self):
-        return [self.vocab[k] for k in self._t5_tokens]
-
-
-class _GPTSentencePieceTokenizer(_SentencePieceTokenizer):
-    """SentencePieceTokenizer-Megatron wrapper"""
-
-    def __init__(self, model_file):
-        super().__init__(model_file, vocab_extra_ids=0)
-
-    def _initalize(self, vocab_extra_ids):
-        self._populate_vocab()
-
-        self._pad_id = self.tokenizer.pad_id()
-        self._bos_id = self.tokenizer.bos_id()
-        self._eos_id = self.tokenizer.eos_id()
-
-    def tokenize(self, text):
-        return self.tokenizer.encode_as_ids(text)
-
-    def detokenize(self, ids):
-        return self.tokenizer.decode_ids(ids)
-
-    @property
-    def cls(self):
-        return -1
-
-    @property
-    def sep(self):
-        return -1
-
-    @property
-    def mask(self):
-        return -1
-
-    @property
-    def eod(self):
-        return self._eos_id
-
-    @property
-    def eos(self):
-        return self._eos_id
-
-    @property
-    def additional_special_tokens_ids(self):
-        return None
-
-
-class _Llama2Tokenizer(_SentencePieceTokenizer):
-    """SentencePieceTokenizer-Megatron wrapper"""
-
-    def __init__(self, model_file):
-        super().__init__(model_file, vocab_extra_ids=0)
-
-    def _initalize(self, vocab_extra_ids):
-        self._populate_vocab()
-
-        # BOS / EOS token IDs
-        self.n_words: int = self.tokenizer.vocab_size()
-        self.bos_id: int = self.tokenizer.bos_id()
-        self.eos_id: int = self.tokenizer.eos_id()
-        self.pad_id: int = self.tokenizer.pad_id()
-        assert self.tokenizer.vocab_size() == self.tokenizer.get_piece_size()
-
-    def tokenize(self, s: str, bos=True, eos=False):
-        '''Default args for text completion, not chat/dialog.'''
-        assert type(s) is str
-        t = self.tokenizer.encode(s)
-        if bos:
-            t = [self.bos_id] + t
-        if eos:
-            t = t + [self.eos_id]
-        return t
-
-    def detokenize(self, ids):
-        return self.tokenizer.decode_ids(ids)
-
-    @property
-    def cls(self):
-        return -1
-
-    @property
-    def sep(self):
-        return -1
-
-    @property
-    def mask(self):
-        return -1
-
-    @property
-    def eod(self):
-        return self.eos_id
-
-    @property
-    def eos(self):
-        return self.eos_id
-
-    @property
-    def additional_special_tokens_ids(self):
-        return None
-
-
-def reload_mergeable_ranks(path: str, max_vocab: Optional[int] = None) -> Dict[bytes, int]:
-    """
-    Reload our tokenizer JSON file and convert it to Tiktoken format.
-    """
-    from ..utils import print_rank_0  # To prevent circular import.
-
-    assert path.endswith(".json")
-
-    # reload vocab
-    with open(path, "r") as f:
-        vocab = json.load(f)
-    assert isinstance(vocab, list)
-    print_rank_0(f"Vocab size: {len(vocab)}")
-    if max_vocab is not None:
-        vocab = vocab[:max_vocab]
-        print_rank_0(f"Cutting vocab to first {len(vocab)} tokens.")
-
-    # build ranks
-    ranks: Dict[bytes, int] = {}
-    for i, x in enumerate(vocab):
-        assert x.keys() == {"rank", "token_bytes", "token_str"}
-        assert x["rank"] == i
-        merge = base64.b64decode(x["token_bytes"])
-        assert i >= 256 or merge == bytes([i])
-        ranks[merge] = x["rank"]
-
-    # sanity check
-    assert len(ranks) == len(vocab)
-    assert set(ranks.values()) == set(range(len(ranks)))
-
-    return ranks
-
-
-PATTERN_TIKTOKEN = (
-    r"[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+"
-)
-PATTERN_TIKTOKEN_V2 = "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
-
-
-class CustomTikTokenizer(MegatronLegacyTokenizer):
-    def __init__(
-        self,
-        path: str,
-        pattern: str,
-        vocab_size: Optional[int],
-        num_special_tokens: int,
-        special_tokens: Optional[List[str]],
-    ):
-        super().__init__(
-            path,
-            pattern=pattern,
-            vocab_size=vocab_size,
-            num_special_tokens=num_special_tokens,
-            special_tokens=special_tokens,
-        )
-        import tiktoken
-
-        from .. import print_rank_0  # To prevent circular import.
-
-        if vocab_size is None:
-            vocab_size = 2**17  # Fallback vocab size is 131072.
-        self._vocab_size = vocab_size
-
-        SPECIAL_TOKENS = ["<unk>", "<s>", "</s>"]
-        if special_tokens is None:
-            special_tokens = SPECIAL_TOKENS.copy()
-        assert len(special_tokens) == len(
-            set(special_tokens)
-        ), f"Special tokens should be unique: {special_tokens}"
-        assert len(special_tokens) <= num_special_tokens < self._vocab_size
-        assert set(SPECIAL_TOKENS) <= set(
-            special_tokens
-        ), f"Custom special tokens should include {SPECIAL_TOKENS}"
-
-        special_filler = [
-            "<SPECIAL_{id}>".format(id=i) for i in range(len(special_tokens), num_special_tokens)
-        ]
-        if special_filler:
-            print_rank_0(f"Adding special tokens {special_filler[0]}, ..., {special_filler[-1]}")
-        special_tokens = special_tokens + special_filler
-        assert len(set(special_tokens)) == len(special_tokens) == num_special_tokens, special_tokens
-        inner_vocab_size = self._vocab_size - num_special_tokens
-
-        token_to_id_without_special_tokens = reload_mergeable_ranks(
-            path, max_vocab=inner_vocab_size
-        )
-        # Create space for special tokens.
-        token_to_id_without_special_tokens = {
-            t: i + num_special_tokens for t, i in token_to_id_without_special_tokens.items()
-        }
-
-        special_tokens = {t: i for i, t in enumerate(special_tokens)}
-        self._unk_id = special_tokens["<unk>"]
-        self._bos_id = special_tokens["<s>"]
-        self._eos_id = special_tokens["</s>"]
-
-        # Create tiktoken model.
-        self._model = tiktoken.Encoding(
-            name=Path(path).parent.name,
-            pat_str=pattern,
-            mergeable_ranks=token_to_id_without_special_tokens,
-            special_tokens=special_tokens,
-        )
-
-        # Create final _id_to_token and _token_to_id data structures with special tokens inserted
-        # into appropriate locations.
-        assert set(token_to_id_without_special_tokens.keys()).isdisjoint(set(special_tokens.keys()))
-        self._token_to_id = token_to_id_without_special_tokens.copy()
-        self._token_to_id.update(special_tokens)
-        self._id_to_token = {v: k for k, v in self._token_to_id.items()}
-        assert set(range(self._vocab_size)) == set(self._id_to_token.keys())
-
-    @property
-    def bos(self) -> int:
-        return self._bos_id
-
-    @property
-    def eos(self) -> int:
-        return self._eos_id
-
-    @property
-    def pad(self) -> int:
-        return self._pad_id
-
-    @property
-    def unk(self) -> int:
-        return self._unk_id
-
-    @property
-    def eod(self) -> int:
-        return self._eos_id
-
-    @property
-    def vocab(self):
-        return self._token_to_id
-
-    @property
-    def inv_vocab(self):
-        return self._id_to_token
-
-    def tokenize(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
-        tokens = self._model.encode_ordinary(s)
-        if bos:
-            tokens = [self.bos, *tokens]
-        if eos:
-            tokens = [*tokens, self.eos]
-
-        return tokens
-
-    def detokenize(self, tokens: List[int]) -> str:
-        return self._model.decode(tokens)
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        try:
-            return self._model.decode_with_offsets(ids)[1]
-        except UnicodeDecodeError:
-            # Tiktoken has an unnecessary check that raises UnicodeDecodeError
-            # from `text = b"".join(token_bytes).decode("utf-8", errors="strict")`
-            # which is not needed for our use case. So we re-implement it, without
-            # the check.
-
-            token_bytes = self._model.decode_tokens_bytes(ids)
-            text_len = 0
-            offsets = []
-            for token in token_bytes:
-                offsets.append(max(0, text_len - (0x80 <= token[0] < 0xC0)))
-                text_len += sum(1 for c in token if not 0x80 <= c < 0xC0)
-            return offsets
-
-    @property
-    def vocab_size(self) -> int:
-        return self._vocab_size
-
-    @property
-    def encoder(self):
-        return self._token_to_id
-
-    @property
-    def decoder(self):
-        return self._id_to_token
-
-
-class _NullTokenizer(MegatronLegacyTokenizer):
-    def __init__(self, vocab_size):
-        super().__init__(None, vocab_size=vocab_size)
-        self._vocab_size_without_eod = int(vocab_size)
-        self._eod_id = self._vocab_size_without_eod
-
-    def tokenize(self, text):
-        return [int(x) for x in text.split(' ')]
-
-    def detokenize(self, ids):
-        text = [str(x) for x in ids]
-        return ' '.join(text)
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        offsets, start_idx = [], 0
-        for id_ in ids:
-            offsets.append(start_idx)
-            start_idx += 1 + len(str(id_))
-        return offsets
-
-    @property
-    def vocab_size(self):
-        return self._vocab_size_without_eod + 1
-
-    @property
-    def vocab(self):
-        raise NotImplementedError
-
-    @property
-    def inv_vocab(self):
-        raise NotImplementedError
-
-    @property
-    def cls(self):
-        return -1
-
-    @property
-    def sep(self):
-        return -1
-
-    @property
-    def mask(self):
-        return -1
-
-    @property
-    def eod(self):
-        return self._eod_id
-
-    @property
-    def eos(self):
-        return self._eod_id
-
-    @property
-    def additional_special_tokens_ids(self):
-        return None
-
-
-class _NullMultimodalTokenizer(MegatronLegacyTokenizer):
-    def __init__(self, vocab_size, image_token=None, image_token_id=None):
-        super().__init__(None, vocab_size=vocab_size)
-        self._vocab_size_without_eod = int(vocab_size)
-        self._eod_id = self._vocab_size_without_eod
-
-        from megatron.core.models.multimodal.llava_model import (
-            DEFAULT_IMAGE_TOKEN_INDEX,
-            IMAGE_TOKEN,
-        )
-
-        self._image_token = image_token if image_token is not None else IMAGE_TOKEN
-        self._image_token_id = (
-            image_token_id if image_token_id is not None else DEFAULT_IMAGE_TOKEN_INDEX
-        )
-
-    def tokenize(self, text):
-        return [int(x) for x in text.split(' ')]
-
-    def detokenize(self, ids):
-        text = [str(x) for x in ids]
-        return ' '.join(text)
-
-    def offsets(self, ids: list[int], text: str) -> list[int]:
-        offsets, start_idx = [], 0
-        for id_ in ids:
-            offsets.append(start_idx)
-            start_idx += 1 + len(str(id_))
-        return offsets
-
-    def convert_tokens_to_ids(self, tokens):
-        ids = [
-            (int(t) if t != self._image_token else self._image_token_id) for t in tokens.split('  ')
-        ]
-        return ids if len(ids) > 1 else ids[0]
-
-    @property
-    def vocab_size(self):
-        return self._vocab_size_without_eod + 1
-
-    @property
-    def vocab(self):
-        raise NotImplementedError
-
-    @property
-    def inv_vocab(self):
-        raise NotImplementedError
-
-    @property
-    def cls(self):
-        return -1
-
-    @property
-    def sep(self):
-        return -1
-
-    @property
-    def mask(self):
-        return -1
-
-    @property
-    def eod(self):
-        return self._eod_id
-
-    @property
-    def eos(self):
-        return self._eod_id
-
-    @property
-    def additional_special_tokens_ids(self):
-        return None
diff --git a/megatron/training/training.py b/megatron/training/training.py
index e9736ac085c..0c33206ba8b 100644
--- a/megatron/training/training.py
+++ b/megatron/training/training.py
@@ -43,11 +43,13 @@ def set_startup_timestamps(program_start=None, main_entry=None):
 import os
 import sys
 from contextlib import nullcontext
+from pathlib import Path
 from typing import Any, Optional, Dict
 
 import torch.distributed
 
 from megatron.core.optimizer.distrib_optimizer import DistributedOptimizer
+from megatron.core.optimizer_param_scheduler import get_canonical_lr_for_logging
 from .log_handler import CustomHandler
 
 # Make default logging level INFO, but filter out all log messages not from MCore.
@@ -152,6 +154,13 @@ def set_startup_timestamps(program_start=None, main_entry=None):
 from megatron.core.inference.unified_memory import create_unified_mempool
 from megatron.core.resharding.refit import swap_model_weights
 
+try:
+    from torch_memory_saver import torch_memory_saver
+    torch_memory_saver.hook_mode = "torch"
+    HAVE_TORCH_MEMORY_SAVER = True
+except ImportError:
+    HAVE_TORCH_MEMORY_SAVER = False
+
 from megatron.core.pipeline_parallel import get_forward_backward_func
 from megatron.core.num_microbatches_calculator import (
     destroy_num_microbatches_calculator,
@@ -220,10 +229,20 @@ def num_floating_point_operations(args, batch_size):
     def calculate_layer_counts():
         """Calculate the number of attention, Mamba, and MLP layers."""
         if args.hybrid_override_pattern:
-            counts = {'M': 0, '*': 0, '-': 0, 'E':0}
-            for layer_type in args.hybrid_override_pattern:
-                if layer_type in counts:
-                    counts[layer_type] += 1
+            from megatron.core.ssm.mamba_hybrid_layer_allocation import parse_hybrid_pattern
+            # Parse unified pattern to separate main and MTP components
+            parsed = parse_hybrid_pattern(args.hybrid_override_pattern)
+            counts = {'M': 0, '*': 0, '-': 0, 'E': 0}
+            # Count main decoder layers
+            if parsed.main_pattern:
+                for layer_type in parsed.main_pattern:
+                    if layer_type in counts:
+                        counts[layer_type] += 1
+            # Count MTP layers (pattern repeated mtp_num_depths times)
+            if parsed.mtp_pattern and parsed.mtp_num_depths > 0:
+                for layer_type in parsed.mtp_pattern:
+                    if layer_type in counts:
+                        counts[layer_type] += parsed.mtp_num_depths
             return counts['*'], counts['M'], counts['-'], counts['E']
         else:
             num_attn_layers = round(args.num_layers * args.hybrid_attention_ratio)
@@ -300,7 +319,7 @@ def hybrid_flops(batch_size, seq_len, hidden_size,
                      mlp_expansion=4.0, swiglu=False,
                      moe_latent_size=None,
                      moe_ffn_hidden_size=2048, shared_expert_ffn_hidden_size=2048, num_experts_routed_to=1,
-                     vocab_size=256000):
+                     vocab_size=256000, mtp_num_layers=0):
         """Calculate total FLOPs for the hybrid model."""
         flops_fwd = (
                 num_attn_layers * attn_layer_flops(batch_size, seq_len, hidden_size,
@@ -313,7 +332,7 @@ def hybrid_flops(batch_size, seq_len, hidden_size,
                 num_moe_layers * moe_layer_flops(batch_size, seq_len, hidden_size, moe_ffn_hidden_size,
                                                  shared_expert_ffn_hidden_size, num_experts_routed_to,
                                                  moe_latent_size, swiglu) +
-                (2 * batch_size * seq_len * hidden_size * vocab_size)  # logits computation
+                (2 * batch_size * seq_len * hidden_size * vocab_size * (1 + mtp_num_layers))  # logits computation
         )
         return flops_fwd * 3
 
@@ -364,6 +383,7 @@ def transformer_flops():
             if args.moe_ffn_hidden_size is not None
             else args.ffn_hidden_size
         )
+        moe_latent_size = args.moe_latent_size
         shared_expert_ffn_hidden_size = (
             0
             if args.moe_shared_expert_intermediate_size is None
@@ -545,7 +565,20 @@ def transformer_flops():
                     (args.ffn_hidden_size * ffn_expansion_factor)
                     * num_dense_layers
                     # routed experts
-                    + (moe_ffn_hidden_size * num_experts_routed_to * ffn_expansion_factor)
+                    + (
+                        (moe_ffn_hidden_size * num_experts_routed_to * ffn_expansion_factor)
+                        if moe_latent_size is None
+                        else (
+                            (
+                                moe_ffn_hidden_size
+                                * num_experts_routed_to
+                                * ffn_expansion_factor
+                                * moe_latent_size
+                                / args.hidden_size
+                            )  # Routed experts run on moe_latent_size.
+                            + 2 * moe_latent_size  # Up proj and down proj.
+                        )
+                    )
                     * num_moe_layers
                     # Shared Experts.
                     + (shared_expert_ffn_hidden_size * ffn_expansion_factor)
@@ -578,6 +611,9 @@ def transformer_flops():
         # Calculate the number of each type of layer.
         num_attn_layers, num_mamba_layers, num_mlp_layers, num_moe_layers = calculate_layer_counts()
 
+        mtp_num_layers = args.mtp_num_layers
+        if mtp_num_layers is None:
+            mtp_num_layers = 0
         # Compute hybrid model FLOPs.
         return hybrid_flops(
             batch_size=batch_size,
@@ -604,6 +640,7 @@ def transformer_flops():
                                            else args.moe_shared_expert_intermediate_size),
             num_experts_routed_to=args.moe_router_topk,
             vocab_size=args.padded_vocab_size,
+            mtp_num_layers=mtp_num_layers,
         )
     else:
         # Compute standard Transformer model FLOPs.
@@ -739,8 +776,14 @@ def pretrain(
             to it. It is used for programs to add their own arguments.
         args_defaults: a dictionary from argument-name to argument-value. It
             to set already parse arguments.
-        get_embedding_ranks (TODO):
-        get_position_embedding_ranks (TODO):
+        get_embedding_ranks: a function that takes a list of ranks for a pipeline
+            group and returns those ranks that should have word embeddings.
+            For most models, these are the first and last pipeline stages.
+            If None, defaults to returning the first and last pipeline stages.
+        get_position_embedding_ranks: a function that takes a list of ranks for
+            a pipeline group and returns those ranks that should have position
+            embeddings. For most models, this is only the first pipeline stage.
+            If None, defaults to returning only the first pipeline stage.
         non_loss_data_func (callable): A custom function to call during evaluation.
             It can run e.g. benchmarks.
         store: an optional instance of torch.distributed.Store, to be used by
@@ -953,15 +996,30 @@ def pretrain(
 
             # Optionally allocate the RL inference model weights from a unified virtual memory (UVM)
             # mempool so we can prefetch weights to CPU when idle while keeping CUDA-graph-safe pointers.
+            # Alternatively, use torch_memory_saver to offload the weights to CPU when idle.
             uvm_mempool = None
             uvm_level = args.rl_inference_model_unified_memory_level
             if uvm_level and uvm_level > 0:
                 uvm_mempool = create_unified_mempool()
 
-            mempool_ctx = (
-                torch.cuda.use_mem_pool(uvm_mempool) if uvm_mempool is not None else nullcontext()
+            # Determine which context manager to use for model allocation
+            # Use torch_memory_saver if offloading is requested but UVM is not enabled
+            use_torch_saver_for_inference_model = (
+                args.rl_offload_inference_model_weights_when_idle
+                and uvm_level == 0
+                and HAVE_TORCH_MEMORY_SAVER
             )
-            with mempool_ctx:
+            if use_torch_saver_for_inference_model:
+                # Use torch_memory_saver for offloading - allocate within a tagged region
+                model_alloc_ctx = torch_memory_saver.region(
+                    tag="rl_inference_model", enable_cpu_backup=True
+                )
+            elif uvm_mempool is not None:
+                model_alloc_ctx = torch.cuda.use_mem_pool(uvm_mempool)
+            else:
+                model_alloc_ctx = nullcontext()
+
+            with model_alloc_ctx:
                 inference_model = get_model(
                     model_provider,
                     model_type,
@@ -971,7 +1029,12 @@ def pretrain(
                 )
             inference_model[0].eval()
 
-
+        # Validate: offloading flag requires a separate inference model
+        if args.rl_offload_inference_model_weights_when_idle and inference_model is None:
+            raise ValueError(
+                "--rl-offload-inference-model-weights-when-idle requires a separate inference model. "
+                "This flag is only useful when doing refit since the weights are shared with the training model."
+            )
 
     # Data stuff.
     app_metrics['app_build_dataiters_start_time'] = one_logger_utils.get_timestamp_in_ms()
@@ -1070,6 +1133,7 @@ def pretrain(
         prefix = f'iteration {iteration} on validation set'
         if getattr(args, 'perform_rl_step', False):
             rl_eval_model = model
+            rl_training_model = None
             if inference_model is not None:
                 inf_core = unwrap_model(inference_model[0])
                 # If separate inference and training models, swap training weights
@@ -1077,11 +1141,14 @@ def pretrain(
                 rl_utils._maybe_prefetch_separate_inference_model_weights(inf_core, to_cpu=False)
                 swap_model_weights(model, inference_model, args.refit_method)
                 rl_eval_model = inference_model
+                rl_training_model = model
             rl_utils.evaluate_and_print_results_rl(
                 valid_data_iterator,
                 rl_eval_model,
                 optimizer,
-                iteration, write_to_tensorboard=not args.skip_train
+                iteration,
+                write_to_tensorboard=not args.skip_train,
+                training_model=rl_training_model,
             )
         else:
             evaluate_and_print_results(
@@ -1788,7 +1855,7 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch
 def training_log(
     loss_dict,
     total_loss_dict,
-    learning_rate,
+    learning_rate: float | None,
     iteration,
     loss_scale,
     report_memory_flag,
@@ -1889,15 +1956,16 @@ def training_log(
     total_iterations = total_loss_dict[advanced_iters_key] + total_loss_dict[skipped_iters_key]
 
     # learning rate will be None on ranks without trainable params, so we must gather across mp ranks
-    learning_rate = reduce_max_stat_across_model_parallel_group(learning_rate)
+    learning_rate: float | None = reduce_max_stat_across_model_parallel_group(learning_rate)
     # Tensorboard values.
     if writer and (iteration % args.tensorboard_log_interval == 0):
         if wandb_writer:
             wandb_writer.log({'samples vs steps': args.consumed_train_samples}, iteration)
-        writer.add_scalar('learning-rate', learning_rate, iteration)
-        writer.add_scalar('learning-rate vs samples', learning_rate, args.consumed_train_samples)
-        if wandb_writer:
-            wandb_writer.log({'learning-rate': learning_rate}, iteration)
+        if learning_rate is not None:
+            writer.add_scalar('learning-rate', learning_rate, iteration)
+            writer.add_scalar('learning-rate vs samples', learning_rate, args.consumed_train_samples)
+            if wandb_writer:
+                wandb_writer.log({'learning-rate': learning_rate}, iteration)
         if args.skipped_train_samples > 0:
             writer.add_scalar('skipped-train-samples', args.skipped_train_samples, iteration)
             if wandb_writer:
@@ -2298,11 +2366,14 @@ def post_training_step_callbacks(
     if (
         args.profile
         and iteration == args.profile_step_end
-        and torch.distributed.get_rank() in args.profile_ranks
+        and (len(args.profile_ranks) == 0 or
+             torch.distributed.get_rank() in args.profile_ranks)
     ):
         if args.use_pytorch_profiler:
             assert prof is not None
             prof.stop()
+            if prof.execution_trace_observer is not None:
+                prof.execution_trace_observer.unregister_callback()
         else:
             torch.cuda.check_error(torch.cuda.cudart().cudaProfilerStop())
             if nsys_nvtx_context is not None:
@@ -2663,9 +2734,20 @@ def get_e2e_base_metrics():
     nsys_nvtx_context = None # reference to context for nsys profiling, so it can be cleaned up
     if (
         args.profile
-        and torch.distributed.get_rank() in args.profile_ranks
+        and (len(args.profile_ranks) == 0 or
+             torch.distributed.get_rank() in args.profile_ranks)
         and args.use_pytorch_profiler
     ):
+        if args.pytorch_profiler_collect_chakra:
+            et_dir = Path(f"{args.tensorboard_dir}/../chakra")
+            et_dir.mkdir(parents=True, exist_ok=True)
+            et = torch.profiler.ExecutionTraceObserver().register_callback(f"{et_dir}/rank-{torch.distributed.get_rank()}.json.gz")
+        else:
+            et = None
+        def trace_handler(p):
+            profile_dir = Path(f"{args.tensorboard_dir}/../torch_profile")
+            profile_dir.mkdir(parents=True, exist_ok=True)
+            p.export_chrome_trace(f"{profile_dir}/rank-{torch.distributed.get_rank()}.json.gz")
         prof = torch.profiler.profile(
             schedule=torch.profiler.schedule(
                 wait=max(args.profile_step_start - 1, 0),
@@ -2673,9 +2755,10 @@ def get_e2e_base_metrics():
                 active=args.profile_step_end - args.profile_step_start,
                 repeat=1,
             ),
-            on_trace_ready=torch.profiler.tensorboard_trace_handler(args.tensorboard_dir),
-            record_shapes=True,
-            with_stack=True,
+            on_trace_ready=trace_handler,
+            record_shapes=args.pytorch_profiler_collect_shapes,
+            with_stack=args.pytorch_profiler_collect_callstack,
+            execution_trace_observer=et,
         )
         prof.start()
 
@@ -2711,7 +2794,9 @@ def get_e2e_base_metrics():
     # Run training iterations till done.
     buffered_rollouts = None
     while iteration < args.train_iters:
-        if args.profile and torch.distributed.get_rank() in args.profile_ranks:
+        if (args.profile 
+            and (len(args.profile_ranks) == 0 or
+                 torch.distributed.get_rank() in args.profile_ranks)):
             if args.use_pytorch_profiler:
                 prof.step()
             elif iteration == args.profile_step_start:
@@ -2797,8 +2882,15 @@ def get_e2e_base_metrics():
 
         if getattr(args, 'perform_rl_step', False):
             with torch.no_grad():
-                train_data_iterator = rl_utils.setup_grpo_data_iterator(
-                    model, inference_model, optimizer, iteration, ref_state_dict, buffered_rollouts
+                train_data_iterator = rl_utils.get_grpo_data_iterator(
+                    model, inference_model, optimizer, iteration, ref_state_dict,
+                    grpo_iterations=args.grpo_iterations,
+                    grpo_prompts_per_step=args.grpo_prompts_per_step,
+                    grpo_group_size=args.grpo_group_size,
+                    global_batch_size=args.global_batch_size,
+                    sequence_packing=args.rl_use_sequence_packing,
+                    buffered_rollouts=buffered_rollouts,
+                    is_correction=args.rl_inference_logprobs_is_correction,
                 )
                 # Buffered rollouts are used as a state container for setups when
                 # we use previously-generated data for an update.
@@ -2877,7 +2969,10 @@ def get_e2e_base_metrics():
         if getattr(args, 'perform_rl_step', False) and args.rl_use_sequence_packing:
             iteration_sequences = rl_utils.get_iteration_sequence_count(args)
             # Track bins separately for packed mode
-            rl_utils.update_sequence_packing_metrics(args)
+            bin_count = (
+                mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches()
+            )
+            args.consumed_train_bins += bin_count
         else:
             batch_size = (
                 mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches()
@@ -2911,12 +3006,7 @@ def get_e2e_base_metrics():
 
         if args.log_params_norm:
             params_norm = calc_params_l2_norm(model)
-        learning_rate = None
-        for param_group in optimizer.param_groups:
-            if len(param_group['params']) == 0:
-                continue
-            if param_group['default_config']:
-                learning_rate = param_group['lr']
+        learning_rate = get_canonical_lr_for_logging(optimizer.param_groups)
         report_memory_flag = training_log(
             loss_dict,
             total_loss_dict,
@@ -2949,6 +3039,7 @@ def get_e2e_base_metrics():
             timers('eval-time', log_level=0).start(barrier=True)
             if getattr(args, 'perform_rl_step', False):
                 rl_eval_model = model
+                rl_training_model = None
                 # If separate inference and training models, swap training weights
                 # back to the inference model for RL evaluation.
                 if inference_model is not None:
@@ -2958,12 +3049,14 @@ def get_e2e_base_metrics():
                     )
                     swap_model_weights(model, inference_model, args.refit_method)
                     rl_eval_model = inference_model
+                    rl_training_model = model
                 rl_utils.evaluate_and_print_results_rl(
                     valid_data_iterator,
                     rl_eval_model,
                     optimizer,
                     iteration,
                     write_to_tensorboard=True,
+                    training_model=rl_training_model,
                 )
             else:
                 evaluate_and_print_results(prefix, forward_step_func,
@@ -3095,6 +3188,17 @@ def evaluate(
     if args.cuda_graph_impl == "local" and CudaGraphScope.full_iteration in args.cuda_graph_scope:
         forward_backward_func = FullCudaGraphWrapper(forward_backward_func, cuda_graph_warmup_steps=args.cuda_graph_warmup_steps)
 
+    if has_nvidia_modelopt:
+        # [ModelOpt]: Pipeline-parallel Distillation stacks student and teacher tensors
+        adjust_tensor_shapes_fn = get_tensor_shapes_adjust_fn_for_distillation(
+            model,
+            seq_length=args.seq_length,
+            micro_batch_size=args.micro_batch_size,
+            decoder_seq_length=args.decoder_seq_length,
+        )
+    else:
+        adjust_tensor_shapes_fn = None
+
     if eval_iters is None:
         eval_iters = args.eval_iters
 
@@ -3119,6 +3223,7 @@ def evaluate(
                 micro_batch_size=args.micro_batch_size,
                 decoder_seq_length=args.decoder_seq_length,
                 forward_only=True,
+                adjust_tensor_shapes_fn=adjust_tensor_shapes_fn,
             )
             ft_integration.on_eval_step_end()
             config.timers = get_timers()
diff --git a/megatron/training/training_config.py b/megatron/training/training_config.py
index 27c3f384c2f..526a2e7ee59 100644
--- a/megatron/training/training_config.py
+++ b/megatron/training/training_config.py
@@ -449,6 +449,25 @@ class CheckpointConfig:
     ckpt_fully_parallel_load: bool = False
     """Apply full load parallelization across DP for distributed checkpoints."""
 
+    ckpt_fully_parallel_load_exchange_algo: Literal["broadcast", "gather_rounds", "gather_object"] = "broadcast"
+    """Algorithm for fully parallel load of distributed checkpoints.
+    "broadcast"(default): Broadcast the checkpoint from rank 0 to all other ranks.
+    "gather_rounds": Gather the checkpoint from all ranks in rounds.
+    "gather_object": Gather the checkpoint from all ranks in a single operation.
+    """
+
+    ckpt_fully_parallel_save_process_group: Literal["dp", "ep_dp"] = "dp"
+    """Process group for fully parallel save of distributed checkpoints.
+    "dp"(default): Data parallel process group.
+    "ep_dp": Expert data parallel process group.
+    """
+
+    ckpt_fully_parallel_load_process_group: Literal["dp", "ep_dp"] = "dp"
+    """Process group for fully parallel load of distributed checkpoints.
+    "dp"(default): Data parallel process group.
+    "ep_dp": Expert data parallel process group.
+    """
+
     ckpt_assume_constant_structure: bool = False
     """Assume the checkpoint structure is constant across saves to enable optimizations."""
 
diff --git a/megatron/training/utils.py b/megatron/training/utils.py
index 7709f651391..54d69f3b150 100644
--- a/megatron/training/utils.py
+++ b/megatron/training/utils.py
@@ -241,7 +241,7 @@ def average_losses_across_data_parallel_group(losses):
     return averaged_losses
 
 
-def reduce_max_stat_across_model_parallel_group(stat: float) -> float:
+def reduce_max_stat_across_model_parallel_group(stat: float) -> float | None:
     """
     Ranks without an optimizer will have no grad_norm or num_zeros_in_grad stats.
     We need to ensure the logging and writer rank has those values.
@@ -256,6 +256,7 @@ def reduce_max_stat_across_model_parallel_group(stat: float) -> float:
         stat, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group()
     )
     if stat.item() == -1.0:
+        # No rank has a valid stat, so return None to indicate that it is None across all ranks.
         return None
     else:
         return stat.item()
@@ -602,25 +603,6 @@ def _broadcast_cu_seqlens(cu_seqlens):
             _broadcast(batch['loss_mask'])
             _broadcast(batch['attention_mask'])
 
-        def _broadcast_cu_seqlens(cu_seqlens):
-            dev = torch.cuda.current_device()
-
-            n = 0 if cu_seqlens is None else int(cu_seqlens.numel())
-            n_tensor = torch.tensor(n, dtype=torch.int64, device=dev)
-            _broadcast(n_tensor)
-
-            if n == 0:
-                buf = torch.empty(0, dtype=torch.int32, device=dev)
-            else:
-                assert isinstance(cu_seqlens, torch.Tensor)
-                assert cu_seqlens.dtype == torch.int32
-                assert cu_seqlens.shape[0] == 1, "micro-batch-size must be 1 for packing"
-                buf = cu_seqlens.to(device=dev, non_blocking=True).contiguous()
-            _broadcast(buf)
-
-        _broadcast_cu_seqlens(batch['cu_seqlens'])
-        _broadcast(batch['max_seqlen'])
-
     else:
         if args.hybrid_context_parallel:
             seq_len = torch.tensor(0, dtype=torch.int32, device=torch.cuda.current_device())
@@ -659,7 +641,7 @@ def _broadcast_cu_seqlens(cu_seqlens):
             device=torch.cuda.current_device(),
         )
         cu_seqlens = None
-        if args.sft:
+        if args.hybrid_context_parallel or args.sft:
             max_seqlen = torch.empty(
                 1,
                 dtype=torch.int32,
@@ -667,13 +649,7 @@ def _broadcast_cu_seqlens(cu_seqlens):
             )
         else:
             max_seqlen = None
-
-        cu_seqlens = None
-        max_seqlen = torch.empty(
-            1,
-            dtype=torch.int32,
-            device=torch.cuda.current_device(),
-        ) if args.hybrid_context_parallel else None
+        
         local_cp_size = torch.empty(
             1,
             dtype=torch.int32,
@@ -728,24 +704,6 @@ def _broadcast_cu_seqlens():
             _broadcast(loss_mask)
             _broadcast(attention_mask)
 
-        def _broadcast_cu_seqlens():
-            dev = torch.cuda.current_device()
-
-            n = torch.empty((), dtype=torch.int64, device=dev)
-            _broadcast(n)
-            n = int(n.item())
-
-            if n == 0:
-                cu_seqlens = torch.empty(0, dtype=torch.int32, device=dev)
-            else:
-                cu_seqlens = torch.empty((args.micro_batch_size, n), dtype=torch.int32, device=dev)
-            _broadcast(cu_seqlens)
-
-            return cu_seqlens if n > 0 else None
-
-        cu_seqlens = _broadcast_cu_seqlens()
-        _broadcast(max_seqlen)
-
         batch = {
             'tokens': tokens,
             'labels': labels,
diff --git a/megatron/training/yaml_arguments.py b/megatron/training/yaml_arguments.py
index 70ccac4402c..db90949082d 100644
--- a/megatron/training/yaml_arguments.py
+++ b/megatron/training/yaml_arguments.py
@@ -304,20 +304,11 @@ def validate_yaml(args, defaults={}):
     if args.model_parallel.tensor_model_parallel_size == 1:
         args.model_parallel.sequence_parallel = False
 
-    # disable async_tensor_model_parallel_allreduce when
-    # model parallel memory optimization is enabled
-    if args.model_parallel.sequence_parallel:
-        args.model_parallel.async_tensor_model_parallel_allreduce = False
-
     if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
         if args.model_parallel.sequence_parallel:
             raise RuntimeError(
                 "Using sequence parallelism requires setting the environment variable "
                 "CUDA_DEVICE_MAX_CONNECTIONS to 1")
-        if args.model_parallel.async_tensor_model_parallel_allreduce:
-            raise RuntimeError(
-                "Using async gradient all reduce requires setting the environment "
-                "variable CUDA_DEVICE_MAX_CONNECTIONS to 1")
     
     # MoE Spec check
     if args.language_model.num_moe_experts is not None:
diff --git a/model_provider.py b/model_provider.py
index f8f6ccae01c..0c80c54dfdb 100644
--- a/model_provider.py
+++ b/model_provider.py
@@ -50,13 +50,9 @@ def model_provider(
         def oom_observer(device, alloc, device_alloc, device_free):
             # snapshot right after an OOM happened
             print('saving allocated state during OOM')
-            snapshot = torch.cuda.memory._snapshot()
-            from pickle import dump
 
-            dump(
-                snapshot,
-                open(f"oom_rank-{torch.distributed.get_rank()}_{args.memory_snapshot_path}", 'wb'),
-            )
+            filename = f"oom_rank-{torch.distributed.get_rank()}_{args.memory_snapshot_path}"
+            torch.cuda.memory._dump_snapshot(filename)
 
         torch._C._cuda_attach_out_of_memory_observer(oom_observer)
 
diff --git a/pretrain_bert.py b/pretrain_bert.py
index 7cda429a849..9b11908811b 100644
--- a/pretrain_bert.py
+++ b/pretrain_bert.py
@@ -8,7 +8,6 @@
 import torch.nn.functional as F
 
 from megatron.training import get_args
-from megatron.training import get_tokenizer
 from megatron.training import print_rank_0
 from megatron.training import get_timers
 from megatron.core import tensor_parallel
@@ -20,12 +19,11 @@
 from megatron.training.arguments import core_transformer_config_from_args
 from megatron.core.transformer.spec_utils import import_module
 from megatron.core.models.bert.bert_layer_specs import bert_layer_with_transformer_engine_spec, bert_layer_local_spec
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
 from megatron.core.datasets.bert_dataset import BERTMaskedWordPieceDataset, BERTMaskedWordPieceDatasetConfig
 from megatron.core.datasets.utils import get_blend_from_list
 from megatron.core import mpu, tensor_parallel
-from megatron.core.tokenizers import MegatronTokenizer
 
 
 def model_provider(pre_process=True, post_process=True, vp_stage=None, config=None, pg_collection=None):
@@ -147,10 +145,7 @@ def train_valid_test_datasets_provider(train_val_test_num_samples, vp_stage=None
     """Build train, valid, and test datasets."""
     args = get_args()
 
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    tokenizer = build_tokenizer(args)
 
     config = BERTMaskedWordPieceDatasetConfig(
         random_seed=args.seed,
diff --git a/pretrain_gpt.py b/pretrain_gpt.py
index a2d956a3266..e6ce7ac2a48 100644
--- a/pretrain_gpt.py
+++ b/pretrain_gpt.py
@@ -28,13 +28,11 @@
 from megatron.core.enums import ModelType
 from megatron.core.models.gpt import GPTModel
 from megatron.core.rerun_state_machine import get_rerun_state_machine
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.utils import get_attr_wrapped_model, get_thd_batch_on_this_cp_rank, get_batch_on_this_hybrid_cp_rank, StragglerDetector
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
-from megatron.core.transformer.multi_token_prediction import mtp_on_this_rank, get_mtp_ranks
 from megatron.training import (
     get_args,
     get_timers,
-    get_tokenizer,
     inprocess_restart,
     pretrain,
     print_rank_0,
@@ -214,10 +212,7 @@ def is_dataset_built_on_rank(vp_stage=None):
 
 
 def core_gpt_dataset_config_from_args(args):
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    tokenizer = build_tokenizer(args)
 
     # Sometimes --data-path is too long, instead we parse it from a file.
     blend: Optional[Tuple[List[str], Optional[List[float]]]]
diff --git a/pretrain_ict.py b/pretrain_ict.py
deleted file mode 100644
index bf70256bfa1..00000000000
--- a/pretrain_ict.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
-
-"""Pretrain BERT for Inverse Cloze Task"""
-
-from functools import partial
-import math
-
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-
-from megatron.training import get_args
-from megatron.training import print_rank_0
-from megatron.training import get_timers
-from megatron.core import mpu
-from megatron.core.enums import ModelType
-from megatron.legacy.data.biencoder_dataset_utils import get_ict_batch
-from megatron.legacy.data.dataset_utils import build_train_valid_test_datasets
-from megatron.legacy.model.biencoder_model import biencoder_model_provider
-from megatron.training import pretrain
-from megatron.training.utils import average_losses_across_data_parallel_group
-
-
-def pretrain_ict_model_provider(pre_process=True, post_process=True):
-    args = get_args()
-
-    model = biencoder_model_provider(
-                only_context_model=False,
-                only_query_model=False,
-                biencoder_shared_query_context_model=\
-                args.biencoder_shared_query_context_model,
-                pre_process=pre_process, post_process=post_process)
-
-    return model
-
-def get_group_world_size_rank():
-
-    group = mpu.get_data_parallel_group()
-    rank = group.rank()
-    world_size = group.size()
-
-    return group, rank, world_size
-
-
-class AllgatherFromDataParallelRegion(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, input_):
-        assert input_.dim() == 2
-        group, rank, world_size = get_group_world_size_rank()
-
-        tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
-        tensor_list[rank] = input_
-        torch.distributed.all_gather(tensor_list, input_, group=group)
-
-        output = torch.cat(tensor_list, dim=0).contiguous()
-
-        return output
-
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        group, rank, world_size = get_group_world_size_rank()
-
-        assert grad_output.shape[0] % world_size == 0
-        dim_size = grad_output.shape[0] // world_size
-        output_list = torch.split(grad_output, dim_size, dim=0)
-
-        # get chunk from this rank
-        output = output_list[rank].contiguous()
-        return output
-
-def loss_func(output_tensor):
-    args = get_args()
-    query_logits, context_logits = output_tensor
-
-    micro_batch_size = query_logits.shape[0]
-    # recall we assert that tensor_model_parallel_size == 1
-    assert mpu.get_tensor_model_parallel_world_size() == 1, \
-        "Model parallel size > 1 not supported for ICT"
-
-    global_batch_size = dist.get_world_size() * micro_batch_size
-    all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits)
-    all_context_logits = AllgatherFromDataParallelRegion.apply(context_logits)
-
-    # scores are inner products between query and context embeddings
-    retrieval_scores = torch.matmul(all_query_logits,
-                        torch.transpose(all_context_logits, 0, 1))
-    # scaling the retriever scores
-    if args.retriever_score_scaling:
-        retrieval_scores = retrieval_scores / math.sqrt(args.hidden_size)
-
-    softmax_scores = F.log_softmax(retrieval_scores, dim=1)
-    sorted_vals, sorted_indices = torch.topk(softmax_scores,
-                                    k=softmax_scores.shape[1], sorted=True)
-
-    def topk_accuracy(k):
-        return torch.cuda.FloatTensor([sum([int(i in sorted_indices[i, :k]) \
-            for i in range(global_batch_size)]) / global_batch_size])
-
-    topk_accs = [topk_accuracy(int(k)) for k in args.retriever_report_topk_accuracies]
-
-    labels = torch.arange(global_batch_size).long().cuda()
-    loss = F.nll_loss(softmax_scores, labels, reduction='mean')
-    reduced_losses = average_losses_across_data_parallel_group([loss, *topk_accs])
-
-    # Scale the retrieval loss
-    loss = loss * mpu.get_data_parallel_world_size()
-
-    # create stats_dict with retrieval loss and all specified top-k accuracies
-    topk_acc_dict = {'top{}_acc'.format(k): v * 100 for k, v in \
-                        zip(args.retriever_report_topk_accuracies, reduced_losses[1:])}
-    stats_dict = dict(loss=reduced_losses[0], **topk_acc_dict)
-    return loss, stats_dict
-
-
-
-def forward_step(data_iterator, model):
-    """Forward step."""
-    args = get_args()
-    timers = get_timers()
-
-    # Get the batch.
-    timers('batch-generator', log_level=2).start()
-    query_tokens, query_mask, \
-    context_tokens, context_mask, context_indices = get_ict_batch(data_iterator)
-    timers('batch-generator').stop()
-
-    # Query and Context Types
-    query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0)
-    context_types = torch.cuda.LongTensor(*context_tokens.shape).fill_(0)
-
-    # Forward model.
-    output_tensor = model(query_tokens, query_mask, query_types, context_tokens,
-                        context_mask, context_types)
-
-    return output_tensor, partial(loss_func)
-
-def train_valid_test_datasets_provider(train_val_test_num_samples):
-    """Build train, valid and test datasets."""
-    args = get_args()
-    print_rank_0('> building train, validation, and test datasets '
-                 'for BERT ICT...')
-
-    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
-        data_prefix=args.data_path,
-        splits_string=args.split,
-        train_valid_test_num_samples=train_val_test_num_samples,
-        max_seq_length=args.seq_length,
-        masked_lm_prob=args.mask_prob,
-        short_seq_prob=args.short_seq_prob,
-        seed=args.seed,
-        binary_head=False,
-        dataset_type='ict')
-    print_rank_0("> finished creating BERT ICT datasets ...")
-
-    return train_ds, valid_ds, test_ds
-
-
-if __name__ == "__main__":
-    print_rank_0("WARNING : This script is DEPRECATED. Will be removed in mcore release 0.9")
-    pretrain(train_valid_test_datasets_provider,
-             pretrain_ict_model_provider,
-             ModelType.encoder_or_decoder,
-             forward_step,
-             args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})
diff --git a/pretrain_mamba.py b/pretrain_mamba.py
index e1379be63e9..0fecbef2c71 100644
--- a/pretrain_mamba.py
+++ b/pretrain_mamba.py
@@ -32,12 +32,11 @@
 )
 from megatron.core.models.mamba import MambaModel
 from megatron.core.rerun_state_machine import get_rerun_state_machine
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.utils import get_attr_wrapped_model, is_te_min_version, StragglerDetector
 from megatron.training import (
     get_args,
     get_timers,
-    get_tokenizer,
     inprocess_restart,
     pretrain,
     print_rank_0,
@@ -257,6 +256,7 @@ def forward_step(data_iterator, model: MambaModel):
             attention_mask,
             labels=labels,
             packed_seq_params=packed_seq_params,
+            loss_mask=loss_mask
         )
 
     # [ModelOpt]: model is needed to access ModelOpt distillation losses
@@ -273,10 +273,7 @@ def is_dataset_built_on_rank(vp_stage=None, is_packed_sequence=False):
 
 
 def core_gpt_dataset_config_from_args(args):
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    tokenizer = build_tokenizer(args)
 
     # Sometimes --data-path is too long, instead we parse it from a file.
     blend: Optional[Tuple[List[str], Optional[List[float]]]]
diff --git a/pretrain_t5.py b/pretrain_t5.py
index 2badae9d5ee..f849a5098c3 100644
--- a/pretrain_t5.py
+++ b/pretrain_t5.py
@@ -10,7 +10,7 @@
 
 import megatron
 from megatron.core import mpu, tensor_parallel
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
 from megatron.core.datasets.t5_dataset import (
     T5MaskedWordPieceDataset,
@@ -25,9 +25,8 @@
     get_t5_encoder_with_local_block_spec,
     get_t5_encoder_with_transformer_engine_block_spec,
 )
-from megatron.training import get_args, get_tokenizer, get_timers, pretrain, print_rank_0
+from megatron.training import get_args, get_timers, pretrain, print_rank_0
 from megatron.training.arguments import core_transformer_config_from_args
-from megatron.core.tokenizers import MegatronTokenizer
 from pretrain_gpt import loss_func
 
 """
@@ -213,10 +212,7 @@ def train_valid_test_datasets_provider(train_val_test_num_samples: int):
     """
     args = get_args()
 
-    if args.legacy_tokenizer:
-        tokenizer = get_tokenizer()
-    else:
-        tokenizer = build_tokenizer(args)
+    tokenizer = build_tokenizer(args)
 
     config = T5MaskedWordPieceDatasetConfig(
         random_seed=args.seed,
diff --git a/pretrain_vision_classify.py b/pretrain_vision_classify.py
deleted file mode 100644
index 8d9b28baeb9..00000000000
--- a/pretrain_vision_classify.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
-
-"""Pretrain VIT"""
-
-import torch
-import torch.nn.functional as F
-from functools import partial
-from megatron.training import get_args, get_timers, print_rank_0
-from megatron.core.enums import ModelType
-from megatron.legacy.data.vit_dataset import build_train_valid_datasets
-from megatron.legacy.model.vision.classification import VitClassificationModel
-from megatron.legacy.model.vision.classification import MitClassificationModel
-from megatron.training import pretrain
-from megatron.training.utils import average_losses_across_data_parallel_group
-from megatron.training.arguments import core_transformer_config_from_args
-
-
-def model_provider(pre_process=True, post_process=True):
-    """Build the model."""
-
-    args = get_args()
-    config = core_transformer_config_from_args(args)
-    if args.vision_backbone_type == 'vit':
-        print_rank_0("building VIT model ...")
-        model = VitClassificationModel(config=config,
-                                       num_classes=args.num_classes,
-                                       pre_process=pre_process,
-                                       post_process=post_process)
-    elif args.vision_backbone_type == 'mit':
-        print_rank_0("building MIT model ...")
-        model = MitClassificationModel(num_classes=args.num_classes,
-                                       pre_process=pre_process,
-                                       post_process=post_process)
-    else:
-        raise Exception('{} vision backbone is not supported.'.format(
-                              args.vision_backbone_type))
-    return model
-
-
-def get_batch(data_iterator):
-    """Build the batch."""
-    data = next(data_iterator)
-
-    # only data parallelism; no need for broadcast
-    images = data[0].cuda()
-    labels = data[1].cuda()
-
-    return images, labels
-
-
-def loss_func(labels, output_tensor):
-    logits = output_tensor.contiguous().float()
-    loss = F.cross_entropy(logits, labels)
-
-    outputs = torch.argmax(logits, -1)
-    correct = (outputs == labels).float()
-    accuracy = torch.mean(correct)
-
-    averaged_loss = average_losses_across_data_parallel_group([loss, accuracy])
-
-    return loss, {"loss": averaged_loss[0], "accuracy": averaged_loss[1]}
-
-
-def forward_step(data_iterator, model):
-    """Forward step."""
-    timers = get_timers()
-
-    # Get the batch.
-    timers("batch-generator", log_level=2).start()
-    (
-        images,
-        labels,
-    ) = get_batch(data_iterator)
-    timers("batch-generator").stop()
-
-    # Forward model. lm_labels
-    output_tensor = model(images)
-
-    return output_tensor, partial(loss_func, labels)
-
-def train_valid_test_datasets_provider(train_val_test_num_samples):
-    """Build train, valid, and test datasets."""
-    args = get_args()
-
-    print_rank_0(
-        "> building train, validation, and test datasets " "for VIT ..."
-    )
-    train_ds, valid_ds = build_train_valid_datasets(
-        data_path=args.data_path,
-        image_size=(args.img_h, args.img_w)
-    )
-    print_rank_0("> finished creating VIT datasets ...")
-
-    return train_ds, valid_ds, None
-
-
-if __name__ == "__main__":
-
-    pretrain(
-        train_valid_test_datasets_provider,
-        model_provider,
-        ModelType.encoder_or_decoder,
-        forward_step,
-        args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True}
-    )
diff --git a/pretrain_vision_dino.py b/pretrain_vision_dino.py
deleted file mode 100644
index f75280c42d7..00000000000
--- a/pretrain_vision_dino.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-import torch.distributed as dist
-from functools import partial
-from megatron.training import get_args, get_timers, print_rank_0
-from megatron.core.enums import ModelType
-from megatron.legacy.data.vit_dataset import build_train_valid_datasets
-from megatron.legacy.model.vision.dino import DINOPretrainModel
-from megatron.legacy.model.vision.knn_monitor import knn_predict, get_feature_bank
-from megatron.training import pretrain
-from megatron.training.utils import average_losses_across_data_parallel_group, unwrap_model
-from megatron.training.arguments import core_transformer_config_from_args
-
-def model_provider(pre_process=True, post_process=True):
-    """Build the model."""
-    config = core_transformer_config_from_args(get_args())
-    return DINOPretrainModel(config, pre_process=pre_process, post_process=post_process)
-
-def get_batch(data_iterator):
-    """Build the batch."""
-    data = next(data_iterator)
-
-    # only data parallelism; no need for broadcast
-    if isinstance(data[0], list):
-        images = [aug.cuda() for aug in data[0]]
-    else:
-        images = data[0].cuda()
-    labels = data[1].cuda()
-
-    return images, labels
-
-
-def loss_func(model, labels, output_tensor, collect_data=False):
-    args = get_args()
-
-    model = unwrap_model(model)
-    if model.training:
-        student_output, teacher_output = output_tensor
-        loss = model.dino_loss(student_output, teacher_output, args.curr_iteration)
-        averaged_loss = average_losses_across_data_parallel_group([loss])
-        return loss, {"loss": averaged_loss[0]}
-    else:
-        _, teacher_feature = output_tensor
-        feature_bank, feature_labels, classes = get_feature_bank()
-        feature = F.normalize(teacher_feature.float(), dim=1)
-
-        knn_accs = []
-        for k in [10, 20, 100, 200]:
-            pred_labels = knn_predict(feature, feature_bank,
-                                      feature_labels, classes, k, 0.07)
-            knn_acc = (pred_labels[:, 0] == labels).float().mean()
-            knn_accs.append(knn_acc)
-
-        averaged_loss = average_losses_across_data_parallel_group(knn_accs)
-        return 0, {"knn_acc_10": averaged_loss[0],
-                   "knn_acc_20": averaged_loss[1],
-                   "knn_acc_100": averaged_loss[2],
-                   "knn_acc_200": averaged_loss[3]}
-
-
-def forward_step(data_iterator, model):
-    """Forward step."""
-    timers = get_timers()
-
-    # Get the batch.
-    timers("batch-generator", log_level=2).start()
-    (
-        images,
-        labels,
-    ) = get_batch(data_iterator)
-    timers("batch-generator").stop()
-
-    return model(images), partial(loss_func, model, labels)
-
-
-def train_valid_test_datasets_provider(train_val_test_num_samples):
-    """Build train, valid, and test datasets."""
-    args = get_args()
-
-    print_rank_0(
-        "> building train, validation, and test datasets " "for VIT ..."
-    )
-    train_ds, valid_ds = build_train_valid_datasets(
-        data_path=args.data_path,
-        image_size=(args.img_h, args.img_w)
-    )
-    print_rank_0("> finished creating VIT datasets ...")
-
-    return train_ds, valid_ds, None
-
-
-if __name__ == "__main__":
-
-    pretrain(
-        train_valid_test_datasets_provider,
-        model_provider,
-        ModelType.encoder_or_decoder,
-        forward_step,
-        args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True}
-    )
-
diff --git a/pretrain_vision_inpaint.py b/pretrain_vision_inpaint.py
deleted file mode 100644
index 8570baab5b4..00000000000
--- a/pretrain_vision_inpaint.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
-
-"""Pretrain VIT"""
-
-import torch
-import torch.nn.functional as F
-from functools import partial
-from megatron.training import get_args, get_timers, print_rank_0, print_rank_last
-from megatron.core.enums import ModelType
-from megatron.legacy.data.vit_dataset import build_train_valid_datasets
-from megatron.legacy.model.vision.inpainting import VitInpaintingModel
-from megatron.legacy.model.vision.inpainting import MitInpaintingModel
-from megatron.training import pretrain
-from megatron.training.utils import average_losses_across_data_parallel_group
-from tasks.vision.segmentation.metrics import SSIM, PSNR
-from megatron.training.arguments import core_transformer_config_from_args
-
-def model_provider(pre_process=True, post_process=True):
-    """Build the model."""
-    args = get_args()
-    config = core_transformer_config_from_args(args)
-    if args.vision_backbone_type == 'vit':
-        model = VitInpaintingModel(config=config,
-                                   pre_process=pre_process,
-                                   post_process=post_process)
-    elif args.vision_backbone_type == 'mit':
-        model = MitInpaintingModel(config=config,
-                                   pre_process=pre_process,
-                                   post_process=post_process)
-    else:
-        raise Exception('{} vision backbone is not supported.'.format(
-                              args.vision_backbone_type))
-    return model
-
-
-def get_batch(data_iterator):
-    """Build the batch."""
-    data = next(data_iterator)
-
-    # only data parallelism; no need for broadcast
-    images = data[0][0].cuda()
-    masks = data[0][1].cuda()
-    return images, masks
-
-
-def loss_func(images, masks, masked_images, outputs, non_loss_data=False):
-    outputs = outputs.contiguous().float()
-    masks_flip = 1-masks
-    flip_masked_outputs = outputs.masked_fill(masks_flip.bool(), 0)
-    flip_masked_images = images.masked_fill(masks_flip.bool(), 0)
-
-    ssim_fun = SSIM()
-    psnr_fun = PSNR()
-
-    if not non_loss_data:
-        mask_count = torch.count_nonzero(masks)
-        loss = F.mse_loss(
-            flip_masked_outputs,
-            flip_masked_images.float(),
-            reduction="sum"
-        )
-        loss = loss/mask_count
-        ssim = ssim_fun(flip_masked_outputs, flip_masked_images.float())
-        psnr = psnr_fun(flip_masked_outputs, flip_masked_images.float())
-
-        averaged_loss = average_losses_across_data_parallel_group(
-            [loss, psnr, ssim]
-        )
-
-        return loss, {"loss": averaged_loss[0],
-                      "psnr": averaged_loss[1],
-                      'ssim': averaged_loss[2]}
-    else:
-        synth_images = masked_images.float() + flip_masked_outputs
-        ssim = ssim_fun(synth_images, images.float())
-        psnr = psnr_fun(synth_images, images.float())
-        return torch.cat((images, masked_images, synth_images), dim=2), ssim, psnr
-
-
-def forward_step(data_iterator, model):
-    """Forward step."""
-    timers = get_timers()
-
-    # Get the batch.
-    timers("batch-generator", log_level=2).start()
-    (
-        images,
-        masks,
-    ) = get_batch(data_iterator)
-    timers("batch-generator").stop()
-
-    masked_images = images.masked_fill(masks.bool(), 0)
-    outputs = model(masked_images)
-
-    # Forward mode
-    return outputs, partial(loss_func, images, masks, masked_images)
-
-
-def process_non_loss_data(data, iteration, writer):
-    psnr_sum = 0
-    ssim_sum = 0
-    for (output_tb, ssim, psnr) in data:
-        output_tb[output_tb < 0] = 0
-        output_tb[output_tb > 1] = 1
-        writer.add_images("gt-input-output-vald", output_tb,
-                          global_step=iteration, walltime=None,
-                          dataformats='NCHW')
-        psnr_sum = psnr_sum + psnr.item()
-        ssim_sum = ssim_sum + ssim.item()
-    psnr = psnr_sum/len(data)
-    ssim = ssim_sum/len(data)
-    writer.add_scalar('PSNR generate value-validation', psnr, iteration)
-    writer.add_scalar('SSIM generate value-validation', ssim, iteration)
-
-
-def train_valid_test_datasets_provider(train_val_test_num_samples):
-    """Build train, valid, and test datasets."""
-    args = get_args()
-
-    print_rank_0(
-        "> building train, validation, and test datasets " "for VIT ..."
-    )
-    train_ds, valid_ds = build_train_valid_datasets(
-        data_path=args.data_path,
-        image_size=(args.img_h, args.img_w)
-    )
-    print_rank_0("> finished creating VIT datasets ...")
-
-    return train_ds, valid_ds, None
-
-
-if __name__ == "__main__":
-
-    pretrain(
-        train_valid_test_datasets_provider,
-        model_provider,
-        ModelType.encoder_or_decoder,
-        forward_step,
-        process_non_loss_data,
-        args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True}
-    )
diff --git a/pyproject.toml b/pyproject.toml
index 567954ca4a1..9b75fcf3596 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,6 @@ keywords = [
     "gpu",
     "language",
     "learning",
-    "learning",
     "machine",
     "nvidia",
     "pytorch",
@@ -61,10 +60,17 @@ classifiers = [
 
 [project.urls]
 Download = "https://github.com/NVIDIA/Megatron-LM/releases"
-Homepage = "https://github.com/NVIDIA/Megatron-LM/megatron/core"
+Homepage = "https://github.com/NVIDIA/Megatron-LM"
 
 [project.optional-dependencies]
-mlm = ["flask-restful", "sentencepiece", "tiktoken", "wandb", "transformers"]
+mlm = [
+    "flask-restful",
+    "sentencepiece",
+    "tiktoken",
+    "wandb",
+    "transformers",
+    "accelerate",
+]
 
 dev = [
     "nvidia-modelopt[torch]; sys_platform != 'darwin'",
@@ -88,6 +94,9 @@ dev = [
     "fastapi~=0.50",                                          # Forcing a little bit more recent version of fastapi to be compatible with pydantic 2.0
     "datasets",
     "emerging_optimizers",
+    "flask[async]",
+    "hypercorn",
+    "openai",
 ]
 
 lts = [
@@ -179,14 +188,14 @@ override-dependencies = [
 flash_mla = [
     { git = "https://github.com/deepseek-ai/FlashMLA", rev = "9edee0c022cd0938148a18e334203b0aab43aa19" },
 ]
-transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "d9b7fc5770a88af06e2e9c2bd97b550614c3a69f" }
+transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "5671fd3675906cda1ade26c24a65d3dedd88eb89" }
 nemo-run = { git = "https://github.com/NVIDIA-NeMo/Run.git", rev = "01a9a8ba360f7b2908728ad0516e0ad9d936966d" }
 emerging_optimizers = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git", rev = "v0.1.0" }
 
 [tool.isort]
 profile = "black"                                                          # black-compatible
 line_length = 100                                                          # should match black parameters
-py_version = 310                                                           # python 3.8 as a target version
+py_version = 310                                                           # python 3.10 as a target version
 known_first_party = ["megatron"]                                           # FIRSTPARTY section
 known_third_party = ["transformer_engine"]                                 # THIRDPARTY section
 sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"]
@@ -196,7 +205,7 @@ extend_skip = ["setup.py"]
 [tool.black]
 line_length = 100
 skip_string_normalization = true
-# recongized by future versions, disallows to reformat code with incompatible versions
+# recognized by future versions, disallows to reformat code with incompatible versions
 # Matches NeMO version so people working on both codebases don't need two different version of black installed
 required_version = "24"
 skip_magic_trailing_comma = true
diff --git a/tests/README.md b/tests/README.md
new file mode 100644
index 00000000000..b412eb38934
--- /dev/null
+++ b/tests/README.md
@@ -0,0 +1,18 @@
+# Megatron-LM Tests
+
+## Updating Functional Test Golden Values
+
+When adding new functional tests, it may be necessary to update the golden values used to verify if the test is
+passing as expected.
+
+1. Add the new functional test case with the scope set to `mr-github`
+2. Open a PR with the new test. Ensure the label `Run functional tests` is added
+3. Run the PR CI tests
+4. Run the script to download golden values from a Github CI run
+    a. Ensure click, requests, and python-gitlab are installed in your environment
+    b. Ensure a Github access token is set as an environment variable `GITHUB_TOKEN`
+    c. Run the script `python tests/test_utils/python_scripts/download_golden_values.py --source github --pipeline-id <github-workflow-run-id>`
+    d. Optionally pass in `--only-failing` to only download golden values for failing tests only
+    e. Ensure you are only checking-in golden values for tests are you updating
+
+The Github CI infra may not be appropriate for Perf tests. Perf tests may be more appropriate for nightly jobs on other infra.
diff --git a/tests/functional_tests/python_test_utils/test_grpo_training_loop.py b/tests/functional_tests/python_test_utils/test_grpo_training_loop.py
index 6faca9b11b3..b4447f5f761 100644
--- a/tests/functional_tests/python_test_utils/test_grpo_training_loop.py
+++ b/tests/functional_tests/python_test_utils/test_grpo_training_loop.py
@@ -84,9 +84,8 @@ def test_grpo_training_loop(
     with open(model_config_path, 'r') as f:
         model_config = yaml.safe_load(f)
         metrics = model_config["METRICS"]
-        if "THROUGHPUT_TEST_PARAMS" in model_config:
-            throughput_test_params = model_config["THROUGHPUT_TEST_PARAMS"]
-            start_step = throughput_test_params["--start_step"]
+        if "ENV_VARS" in model_config and "THROUGHPUT_START_STEP" in model_config["ENV_VARS"]:
+            start_step = model_config["ENV_VARS"]["THROUGHPUT_START_STEP"]
         else:
             start_step = 1
 
diff --git a/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py b/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
index 346b464b79d..93bfa6e1299 100644
--- a/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
+++ b/tests/functional_tests/python_test_utils/test_inference_regular_pipeline.py
@@ -3,8 +3,12 @@
 import json
 import logging
 import math
+import os
 from statistics import median
 
+import pytest
+import yaml
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -35,11 +39,22 @@ def _bytes_to_gib(num_bytes: float) -> float:
     return float(num_bytes) / (1024.0**3)
 
 
-def test_inference_pipeline(golden_values_path: str, test_values_path: str) -> None:
+def test_inference_pipeline(
+    golden_values_path: str, test_values_path: str, model_config_path: str
+) -> None:
+    if os.getenv("ENABLE_LIGHTWEIGHT_MODE") == "true":
+        pytest.skip("Lightweight mode enabled. Skipping test.")
 
-    with open(golden_values_path, 'r') as f1, open(test_values_path, 'r') as f2:
+    with (
+        open(golden_values_path, 'r') as f1,
+        open(test_values_path, 'r') as f2,
+        open(model_config_path, 'r') as f3,
+    ):
         golden_values_content = f1.read()
         tensorboard_content = f2.read()
+        model_config_content = f3.read()
+
+    metrics = yaml.safe_load(model_config_content)["METRICS"]
 
     output_groundtruth = json.loads(golden_values_content)
 
@@ -68,57 +83,58 @@ def test_inference_pipeline(golden_values_path: str, test_values_path: str) -> N
 
     # Throughput assertions.
     if "throughput" in output_groundtruth.keys():
+        if "throughput" in metrics:
+            # First warmup iteration is excluded from throughput statistics.
+            throughput_sampled = median(output_current["throughput"][1:])
+            throughput_golden = median(output_groundtruth["throughput"][1:])
 
-        # First warmup iteration is excluded from throughput statistics.
-        throughput_sampled = median(output_current["throughput"][1:])
-        throughput_golden = median(output_groundtruth["throughput"][1:])
-
-        # 10% is empirically observed to be within hardware variance.
-        assert (
-            throughput_sampled >= 0.9 * throughput_golden
-        ), f"Throughput is slower than expected! Expected to be within 10% of ~{throughput_golden} tok/s but benchmarked {output_current['throughput']} tok/s"
+            # 10% is empirically observed to be within hardware variance.
+            assert (
+                throughput_sampled >= 0.9 * throughput_golden
+            ), f"Throughput is slower than expected! Expected to be within 10% of ~{throughput_golden} tok/s but benchmarked {output_current['throughput']} tok/s"
 
-        # If throughput is significantly improved (> 20%), update golden values accordingly.
-        assert (
-            throughput_sampled < throughput_golden * 1.2
-        ), f"Throughput has been improved from expected ~{throughput_golden} tok/s to {output_current['throughput']} tok/s. Please update golden values in the functional tests."
+            # If throughput is significantly improved (> 20%), update golden values accordingly.
+            assert (
+                throughput_sampled < throughput_golden * 1.2
+            ), f"Throughput has been improved from expected ~{throughput_golden} tok/s to {output_current['throughput']} tok/s. Please update golden values in the functional tests."
 
         output_groundtruth.pop('throughput')
 
     # Peak memory regression checks (optional: only if present in golden values).
     if "mem-max-allocated-bytes" in output_groundtruth:
-        assert "mem-max-allocated-bytes" in output_current, (
-            f"Golden values include mem-max-allocated-bytes but current output does not. "
-            "Ensure the inference script records memory metrics to the output JSON."
-        )
-        sampled = _median_as_float(output_current["mem-max-allocated-bytes"])
-        golden = _median_as_float(output_groundtruth["mem-max-allocated-bytes"])
-        assert golden > 0, f"Golden mem_max_allocated_bytes must be > 0, got {golden}."
-
-        low = 0.95 * golden
-        high = 1.05 * golden
-
-        if sampled < low:
-            raise AssertionError(
-                f"Memory is too low for mem-max-allocated-bytes: "
-                f"expected within 5% of {golden:.0f} bytes ({_bytes_to_gib(golden):.3f} GiB) "
-                f"but got {sampled:.0f} bytes ({_bytes_to_gib(sampled):.3f} GiB). "
-                "This is >5% lower than expected; please update golden values in the functional tests."
-            )
-        if sampled > high:
-            raise AssertionError(
-                f"Memory is too high for mem-max-allocated-bytes: "
-                f"expected within ±5% of {golden:.0f} bytes ({_bytes_to_gib(golden):.3f} GiB) "
-                f"but got {sampled:.0f} bytes ({_bytes_to_gib(sampled):.3f} GiB). "
-                "This is >5% higher than expected; this is likely a regression."
+        if "mem-max-allocated-bytes" in metrics:
+            assert "mem-max-allocated-bytes" in output_current, (
+                f"Golden values include mem-max-allocated-bytes but current output does not. "
+                "Ensure the inference script records memory metrics to the output JSON."
             )
+            sampled = _median_as_float(output_current["mem-max-allocated-bytes"])
+            golden = _median_as_float(output_groundtruth["mem-max-allocated-bytes"])
+            assert golden > 0, f"Golden mem_max_allocated_bytes must be > 0, got {golden}."
+
+            low = 0.95 * golden
+            high = 1.05 * golden
+
+            if sampled < low:
+                raise AssertionError(
+                    f"Memory is too low for mem-max-allocated-bytes: "
+                    f"expected within 5% of {golden:.0f} bytes ({_bytes_to_gib(golden):.3f} GiB) "
+                    f"but got {sampled:.0f} bytes ({_bytes_to_gib(sampled):.3f} GiB). "
+                    "This is >5% lower than expected; please update golden values in the functional tests."
+                )
+            if sampled > high:
+                raise AssertionError(
+                    f"Memory is too high for mem-max-allocated-bytes: "
+                    f"expected within ±5% of {golden:.0f} bytes ({_bytes_to_gib(golden):.3f} GiB) "
+                    f"but got {sampled:.0f} bytes ({_bytes_to_gib(sampled):.3f} GiB). "
+                    "This is >5% higher than expected; this is likely a regression."
+                )
         output_groundtruth.pop("mem-max-allocated-bytes")
 
     for request_id, groundtruth_results in output_groundtruth.items():
         current_results = output_current[request_id]
 
         at_least_one_test_loop = False
-        if "generated_tokens" in groundtruth_results:
+        if "generated_tokens" in groundtruth_results and "generated_tokens" in metrics:
             at_least_one_test_loop = True
             tokens_groundtruth = groundtruth_results["generated_tokens"]
             tokens_current = current_results["generated_tokens"]
@@ -127,7 +143,7 @@ def test_inference_pipeline(golden_values_path: str, test_values_path: str) -> N
                 tokens_groundtruth == tokens_current
             ), f"Token mismatch:\nGround truth: {tokens_groundtruth}\nCurrent: {tokens_current}"
 
-        if "logprobs" in groundtruth_results:
+        if "logprobs" in groundtruth_results and "logprobs" in metrics:
             at_least_one_test_loop = True
             logprobs_groundtruth = groundtruth_results["logprobs"]
             logprobs_current = current_results["logprobs"]
@@ -141,7 +157,7 @@ def test_inference_pipeline(golden_values_path: str, test_values_path: str) -> N
                     lp1, lp2, abs_tol=0.001
                 ), f"Logprobs differ at index {i}: {lp1:.5f} vs {lp2:.5f}"
 
-        if "generated_text" in groundtruth_results:
+        if "generated_text" in groundtruth_results and "generated_text" in metrics:
             at_least_one_test_loop = True
             generated_text_groundtruth = groundtruth_results["generated_text"]
             generated_text_current = current_results["generated_text"]
@@ -156,5 +172,13 @@ def test_inference_pipeline(golden_values_path: str, test_values_path: str) -> N
                 f"\nCurrent (truncated to {min_len} chars): {generated_text_current[:min_len]}"
             )
 
+        if "routing_indices" in groundtruth_results and "routing_indices" in metrics:
+            at_least_one_test_loop = True
+            routing_indices_groundtruth = groundtruth_results["routing_indices"]
+            routing_indices_current = current_results["routing_indices"]
+            assert (
+                routing_indices_groundtruth == routing_indices_current
+            ), f"Routing indices mismatch:\nGround truth: {routing_indices_groundtruth}\nCurrent: {routing_indices_current}"
+
         if not at_least_one_test_loop:
             raise AssertionError(f"No test performed for output {groundtruth_results}")
diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/model_config.yaml b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/model_config.yaml
index 0e908381456..506d92d6e3e 100644
--- a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/model_config.yaml
+++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp4_vp2/model_config.yaml
@@ -42,5 +42,4 @@ MODEL_ARGS:
   --bf16: true
   --ckpt-format: torch
   --attention-backend: unfused
-  --legacy-tokenizer: true
 TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..2774c3e5d17
--- /dev/null
+++ b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,2042 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 2000,
+        "step_interval": 5,
+        "values": {
+            "1": 10.53448,
+            "5": 10.55424,
+            "10": 10.54124,
+            "15": 10.56241,
+            "20": 10.31493,
+            "25": 9.58079,
+            "30": 9.41269,
+            "35": 9.1462,
+            "40": 9.07276,
+            "45": 8.96068,
+            "50": 8.73394,
+            "55": 8.787,
+            "60": 8.44186,
+            "65": 8.54135,
+            "70": 8.17058,
+            "75": 8.31072,
+            "80": 7.93137,
+            "85": 7.82686,
+            "90": 7.6187,
+            "95": 7.64784,
+            "100": 7.62107,
+            "105": 7.37798,
+            "110": 7.49468,
+            "115": 7.1694,
+            "120": 6.99301,
+            "125": 7.17505,
+            "130": 6.96922,
+            "135": 6.84276,
+            "140": 7.07292,
+            "145": 7.02766,
+            "150": 6.99516,
+            "155": 6.93151,
+            "160": 6.92324,
+            "165": 7.00588,
+            "170": 6.86437,
+            "175": 6.88575,
+            "180": 6.57748,
+            "185": 7.00154,
+            "190": 6.99035,
+            "195": 6.84245,
+            "200": 6.88148,
+            "205": 6.79115,
+            "210": 6.80554,
+            "215": 6.88675,
+            "220": 6.64375,
+            "225": 6.79173,
+            "230": 6.83365,
+            "235": 6.63994,
+            "240": 6.59216,
+            "245": 6.91651,
+            "250": 6.67586,
+            "255": 6.60209,
+            "260": 6.70358,
+            "265": 6.57796,
+            "270": 6.47753,
+            "275": 6.78298,
+            "280": 6.6381,
+            "285": 6.51952,
+            "290": 6.59854,
+            "295": 6.67217,
+            "300": 6.34515,
+            "305": 6.38508,
+            "310": 6.9642,
+            "315": 6.77167,
+            "320": 6.72703,
+            "325": 6.50704,
+            "330": 6.71283,
+            "335": 6.56346,
+            "340": 6.45328,
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+            "390": 6.52748,
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+            "400": 6.59785,
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+            "440": 6.5358,
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+            "485": 6.54425,
+            "490": 6.2002,
+            "495": 6.29248,
+            "500": 6.51036,
+            "505": 6.46719,
+            "510": 6.50521,
+            "515": 6.16865,
+            "520": 6.55076,
+            "525": 6.50012,
+            "530": 6.30562,
+            "535": 6.1301,
+            "540": 6.60926,
+            "545": 6.43131,
+            "550": 6.43096,
+            "555": 6.3928,
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+            "565": 6.32942,
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+            "585": 6.46721,
+            "590": 6.25301,
+            "595": 6.29399,
+            "600": 6.46381,
+            "605": 6.38927,
+            "610": 6.46834,
+            "615": 6.43108,
+            "620": 6.4805,
+            "625": 6.40948,
+            "630": 6.46626,
+            "635": 6.44107,
+            "640": 6.3476,
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+            "650": 6.32175,
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+            "670": 6.42313,
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+            "690": 6.30571,
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+            "700": 6.10697,
+            "705": 6.35112,
+            "710": 6.18143,
+            "715": 6.41562,
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+            "730": 6.36814,
+            "735": 6.49001,
+            "740": 6.64567,
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+            "840": 6.15736,
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+            "850": 6.44205,
+            "855": 6.5091,
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+            "875": 6.23986,
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+            "960": 6.32113,
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+            "970": 6.36659,
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+            "1025": 6.35203,
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+            "1040": 6.0006,
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+            "1050": 6.33992,
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+            "1060": 6.14803,
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+            "1245": 6.09411,
+            "1250": 5.9596,
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+            "1260": 6.16148,
+            "1265": 6.05007,
+            "1270": 5.95618,
+            "1275": 6.09964,
+            "1280": 5.89321,
+            "1285": 5.91914,
+            "1290": 6.11843,
+            "1295": 6.09592,
+            "1300": 6.24849,
+            "1305": 5.95425,
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+            "1345": 6.05588,
+            "1350": 5.91771,
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+            "1360": 6.32761,
+            "1365": 6.07375,
+            "1370": 6.05339,
+            "1375": 6.29624,
+            "1380": 6.13949,
+            "1385": 5.91549,
+            "1390": 6.02222,
+            "1395": 5.99713,
+            "1400": 6.22208,
+            "1405": 5.99961,
+            "1410": 6.12987,
+            "1415": 6.33419,
+            "1420": 6.16179,
+            "1425": 6.1483,
+            "1430": 5.97615,
+            "1435": 6.36577,
+            "1440": 6.32583,
+            "1445": 6.28949,
+            "1450": 6.07941,
+            "1455": 5.78591,
+            "1460": 6.21217,
+            "1465": 5.94392,
+            "1470": 5.99029,
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diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..481cd5d6f2c
--- /dev/null
+++ b/tests/functional_tests/test_cases/bert/bert_release_sm/golden_values_dev_dgx_h100.json
@@ -0,0 +1,2042 @@
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+            "1935": "nan",
+            "1940": "nan",
+            "1945": "nan",
+            "1950": "nan",
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+            "1960": "nan",
+            "1965": "nan",
+            "1970": "nan",
+            "1975": "nan",
+            "1980": "nan",
+            "1985": "nan",
+            "1990": "nan",
+            "1995": "nan",
+            "2000": 0.26296
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml b/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml
new file mode 100644
index 00000000000..60ef765cf15
--- /dev/null
+++ b/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml
@@ -0,0 +1,57 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: "1"
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1"
+  NON_DETERMINSTIC_RESULTS: 1
+TEST_TYPE: "release"
+MODEL_ARGS:
+  # Bert model args
+  --num-layers: 24
+  --hidden-size: 1024
+  --num-attention-heads: 16
+  --seq-length: 512
+  --max-position-embeddings: 512
+  # Training args
+  --micro-batch-size: 4
+  --global-batch-size: 32
+  --train-iters: 20000
+  --weight-decay: 1e-2
+  --clip-grad: 1.0
+  --fp16: true
+  --lr: 0.0001
+  --lr-decay-style: linear
+  --min-lr: 1.0e-5
+  --lr-warmup-fraction: .01
+  --bert-no-binary-head: true
+  # Model parallel
+  --tensor-model-parallel-size: 8
+  --pipeline-model-parallel-size: 8
+  # Data args
+  --data-path: ${DATA_BLEND}
+  --vocab-file: ${DATA_PATH}/text/the_pile/bert_shard00/vocab.txt
+  --split: 949,50,1
+  --data-cache-path: ${DATA_CACHE_PATH}
+  # EVAL_AND_LOGGING_ARGS
+  --log-interval: 100
+  --save-interval: 2000
+  --save-retain-interval: 10000
+  --eval-interval: 1000
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --eval-iters: 10
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --log-timers-to-tensorboard: true
+  --log-memory-to-tensorboard: true
+  --log-num-zeros-in-grad: true
+  --log-params-norm: true
+  --log-validation-ppl-to-tensorboard: true
+  --wandb-project: megatron-core-release-runs
+  --wandb-entity: adlr
+  --wandb-exp-name: ${WANDB_EXPERIMENT}
+  --attention-backend: unfused
+  --exit-interval: 2000
+  --wandb-save-dir: ${WANDB_SAVE_PATH}
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/common/ckpt_converter/__main__.py b/tests/functional_tests/test_cases/common/ckpt_converter/__main__.py
index 62d86a29358..543ddd36a6d 100644
--- a/tests/functional_tests/test_cases/common/ckpt_converter/__main__.py
+++ b/tests/functional_tests/test_cases/common/ckpt_converter/__main__.py
@@ -8,7 +8,6 @@
 import types
 import typing as T
 from collections import namedtuple
-from copy import deepcopy
 from functools import partial
 
 import numpy as np
@@ -19,14 +18,10 @@
 from megatron.core import parallel_state
 from megatron.core.datasets.gpt_dataset import _get_ltor_masks_and_position_ids
 from megatron.core.enums import ModelType
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
-from megatron.core.models.multimodal.llava_model import DEFAULT_IMAGE_TOKEN_INDEX, LLaVAModel
-from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec
+from megatron.core.models.multimodal.llava_model import DEFAULT_IMAGE_TOKEN_INDEX
 from megatron.core.pipeline_parallel import get_forward_backward_func
 from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
-from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.utils import get_attr_wrapped_model
 from megatron.training import get_args, get_tokenizer
 from megatron.training.arguments import parse_args, validate_args
 from megatron.training.checkpointing import load_checkpoint as _load_checkpoint
diff --git a/tests/functional_tests/test_cases/common/moe_perf/__main__.py b/tests/functional_tests/test_cases/common/moe_perf/__main__.py
index ace44c7ca4f..f1dea5f93c1 100644
--- a/tests/functional_tests/test_cases/common/moe_perf/__main__.py
+++ b/tests/functional_tests/test_cases/common/moe_perf/__main__.py
@@ -8,9 +8,8 @@
 import os
 import statistics
 from contextlib import nullcontext
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Dict, Iterable, Mapping, Optional, Sequence, cast
+from typing import Any, Dict, Mapping, Sequence, cast
 
 import pytest  # type: ignore[import]
 import torch
@@ -18,8 +17,7 @@
 from megatron.core.config import set_experimental_flag
 from megatron.core.fp8_utils import get_fp8_context
 from megatron.core.models.gpt.gpt_layer_specs import (
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.transformer.moe.fused_a2a import HAVE_DEEP_EP, HAVE_HYBRIDEP
 from megatron.core.transformer.moe.moe_layer import MoELayer
@@ -89,10 +87,9 @@ def _build_transformer_config(case: MoEPerformanceCase) -> TransformerConfig:
 
 # NOTE: Only TE backend is covered in this test.
 def _resolve_moe_submodules(case: MoEPerformanceCase):
-    layer_spec = get_gpt_layer_with_transformer_engine_spec(
+    return get_gpt_layer_with_transformer_engine_submodules(
         num_experts=case.model.num_experts, moe_grouped_gemm=True
-    )
-    return layer_spec.submodules.mlp.submodules
+    ).mlp.submodules
 
 
 def _load_baselines() -> Dict[str, Dict[str, float]]:
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml
index 692e3882e02..65e1a2548bc 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml
@@ -70,8 +70,8 @@ MODEL_ARGS:
   # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
   --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 5000
-  --save-retain-interval: 10000
+  --save-interval: 1000
+  --save-retain-interval: 5000
   # Add initialization args
   --init-method-std: 0.0134
   # Add logging args
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..11918b97283
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,13042 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 13000,
+        "step_interval": 5,
+        "values": {
+            "1": 13.05008,
+            "5": 13.01204,
+            "10": 12.1655,
+            "15": 11.32234,
+            "20": 10.41894,
+            "25": 10.0578,
+            "30": 9.71991,
+            "35": 9.42902,
+            "40": 9.23153,
+            "45": 9.0416,
+            "50": 8.84482,
+            "55": 8.66178,
+            "60": 8.60539,
+            "65": 8.47775,
+            "70": 8.43752,
+            "75": 8.34325,
+            "80": 8.18407,
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+            "90": 7.96741,
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+            "100": 7.84521,
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+            "110": 7.56897,
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+            "120": 7.42069,
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+            "140": 7.20033,
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+            "150": 7.14295,
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+            "200": 6.63261,
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+            "12570": "nan",
+            "12575": "nan",
+            "12580": "nan",
+            "12585": "nan",
+            "12590": "nan",
+            "12595": "nan",
+            "12600": 0.92251,
+            "12605": "nan",
+            "12610": "nan",
+            "12615": "nan",
+            "12620": "nan",
+            "12625": "nan",
+            "12630": "nan",
+            "12635": "nan",
+            "12640": "nan",
+            "12645": "nan",
+            "12650": "nan",
+            "12655": "nan",
+            "12660": "nan",
+            "12665": "nan",
+            "12670": "nan",
+            "12675": "nan",
+            "12680": "nan",
+            "12685": "nan",
+            "12690": "nan",
+            "12695": "nan",
+            "12700": 0.91943,
+            "12705": "nan",
+            "12710": "nan",
+            "12715": "nan",
+            "12720": "nan",
+            "12725": "nan",
+            "12730": "nan",
+            "12735": "nan",
+            "12740": "nan",
+            "12745": "nan",
+            "12750": "nan",
+            "12755": "nan",
+            "12760": "nan",
+            "12765": "nan",
+            "12770": "nan",
+            "12775": "nan",
+            "12780": "nan",
+            "12785": "nan",
+            "12790": "nan",
+            "12795": "nan",
+            "12800": "nan",
+            "12805": "nan",
+            "12810": "nan",
+            "12815": "nan",
+            "12820": "nan",
+            "12825": "nan",
+            "12830": "nan",
+            "12835": "nan",
+            "12840": "nan",
+            "12845": "nan",
+            "12850": "nan",
+            "12855": "nan",
+            "12860": "nan",
+            "12865": "nan",
+            "12870": "nan",
+            "12875": "nan",
+            "12880": "nan",
+            "12885": "nan",
+            "12890": "nan",
+            "12895": "nan",
+            "12900": "nan",
+            "12905": "nan",
+            "12910": "nan",
+            "12915": "nan",
+            "12920": "nan",
+            "12925": "nan",
+            "12930": "nan",
+            "12935": "nan",
+            "12940": "nan",
+            "12945": "nan",
+            "12950": "nan",
+            "12955": "nan",
+            "12960": "nan",
+            "12965": "nan",
+            "12970": "nan",
+            "12975": "nan",
+            "12980": "nan",
+            "12985": "nan",
+            "12990": "nan",
+            "12995": "nan",
+            "13000": "nan"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml
index b7fb9d7d661..4ec8797a90a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml
@@ -69,9 +69,8 @@ MODEL_ARGS:
   --eval-interval: 2000
   # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 5000
-  --save-retain-interval: 10000
+  --save-interval: 1000
+  --save-retain-interval: 5000
   # Add initialization args
   --init-method-std: 0.0134
   # Add logging args
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
index 4770792474b..0174aaf4684 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp1_pp4_memory_speed/golden_values_dev_dgx_gb200.json
@@ -133,29 +133,29 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
-            "2": 5.8109,
+            "2": 3.93489,
             "3": "nan",
-            "4": 0.8316,
+            "4": 0.83885,
             "5": "nan",
-            "6": 0.83072,
+            "6": 0.86101,
             "7": "nan",
-            "8": 0.82637,
+            "8": 0.82617,
             "9": "nan",
-            "10": 0.823,
+            "10": 0.8264,
             "11": "nan",
-            "12": 0.82386,
+            "12": 0.82456,
             "13": "nan",
-            "14": 0.82343,
+            "14": 0.82414,
             "15": "nan",
-            "16": 0.82487,
+            "16": 0.82604,
             "17": "nan",
-            "18": 0.82227,
+            "18": 0.83002,
             "19": "nan",
-            "20": 0.82121,
+            "20": 0.8234,
             "21": "nan",
-            "22": 0.82248,
+            "22": 0.82298,
             "23": "nan",
-            "24": 0.81939,
+            "24": 0.82311,
             "25": "nan"
         }
     }
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
index 5c13c9d624f..ca51cd1bcb3 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_gb200.json
@@ -133,29 +133,29 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
-            "2": 3.75605,
+            "2": 2.88247,
             "3": "nan",
-            "4": 1.05448,
+            "4": 0.98359,
             "5": "nan",
-            "6": 1.24087,
+            "6": 0.91373,
             "7": "nan",
-            "8": 0.89299,
+            "8": 1.07044,
             "9": "nan",
-            "10": 0.89376,
+            "10": 0.91309,
             "11": "nan",
-            "12": 0.8965,
+            "12": 0.91579,
             "13": "nan",
-            "14": 0.89831,
+            "14": 0.90609,
             "15": "nan",
-            "16": 0.89733,
+            "16": 0.90906,
             "17": "nan",
-            "18": 1.02538,
+            "18": 0.91134,
             "19": "nan",
-            "20": 0.89305,
+            "20": 0.90623,
             "21": "nan",
-            "22": 0.89255,
+            "22": 0.91236,
             "23": "nan",
-            "24": 0.91075,
+            "24": 0.9145,
             "25": "nan"
         }
     }
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
index 7650494228d..bac18297ae6 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json
@@ -232,56 +232,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 5.51862,
-            "2": 0.11775,
-            "3": 0.11561,
-            "4": 0.1042,
-            "5": 0.10208,
-            "6": 0.09598,
-            "7": 0.09542,
-            "8": 0.095,
-            "9": 0.09404,
-            "10": 0.09596,
-            "11": 0.09825,
-            "12": 0.09507,
-            "13": 0.0943,
-            "14": 0.09595,
-            "15": 0.09454,
-            "16": 0.09354,
-            "17": 0.09423,
-            "18": 0.09638,
-            "19": 0.09698,
-            "20": 0.09656,
-            "21": 0.09629,
-            "22": 0.09731,
-            "23": 0.09913,
-            "24": 0.09535,
-            "25": 0.09314,
-            "26": 0.09324,
-            "27": 0.09374,
-            "28": 0.0992,
-            "29": 0.09647,
-            "30": 0.11416,
-            "31": 0.09524,
-            "32": 0.09418,
-            "33": 0.09544,
-            "34": 0.09428,
-            "35": 0.09432,
-            "36": 0.09584,
-            "37": 0.096,
-            "38": 0.09539,
-            "39": 0.09482,
-            "40": 0.09568,
-            "41": 0.09682,
-            "42": 0.0964,
-            "43": 0.09675,
-            "44": 0.09583,
-            "45": 0.09482,
-            "46": 0.09426,
-            "47": 0.09537,
-            "48": 0.09383,
-            "49": 0.09397,
-            "50": 0.09592
+            "1": "nan",
+            "2": 2.46491,
+            "3": 0.11014,
+            "4": 0.09722,
+            "5": 0.09703,
+            "6": 0.09705,
+            "7": 0.09593,
+            "8": 0.09584,
+            "9": 0.09505,
+            "10": 0.0949,
+            "11": 0.09504,
+            "12": 0.09589,
+            "13": 0.09506,
+            "14": 0.09425,
+            "15": 0.09404,
+            "16": 0.09465,
+            "17": 0.09237,
+            "18": 0.09201,
+            "19": 0.09159,
+            "20": 0.09124,
+            "21": 0.09092,
+            "22": 0.09028,
+            "23": 0.08966,
+            "24": 0.08893,
+            "25": 0.09042,
+            "26": 0.09055,
+            "27": 0.08889,
+            "28": 0.08857,
+            "29": 0.0884,
+            "30": 0.08807,
+            "31": 0.08777,
+            "32": 0.08747,
+            "33": 0.0876,
+            "34": 0.08733,
+            "35": 0.0886,
+            "36": 0.08828,
+            "37": 0.08789,
+            "38": 0.08768,
+            "39": 0.08819,
+            "40": 0.08922,
+            "41": 0.08797,
+            "42": 0.0876,
+            "43": 0.0868,
+            "44": 0.08693,
+            "45": 0.08661,
+            "46": 0.08657,
+            "47": 0.08769,
+            "48": 0.08644,
+            "49": 0.08681,
+            "50": 0.08702
         }
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
index 0405b9dc312..8bcd3aa91d5 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_gb200.json
@@ -232,56 +232,56 @@
         "end_step": 50,
         "step_interval": 1,
         "values": {
-            "1": 5.87663,
-            "2": 0.11967,
-            "3": 0.10376,
-            "4": 0.09966,
-            "5": 0.0967,
-            "6": 0.09666,
-            "7": 0.09702,
-            "8": 0.09962,
-            "9": 0.10053,
-            "10": 0.10019,
-            "11": 0.09818,
-            "12": 0.37487,
-            "13": 0.10166,
-            "14": 0.10015,
-            "15": 0.10189,
-            "16": 0.09883,
-            "17": 0.10229,
-            "18": 0.09859,
-            "19": 0.09957,
-            "20": 0.09987,
-            "21": 0.09747,
-            "22": 0.09678,
-            "23": 0.09865,
-            "24": 0.09988,
-            "25": 0.11712,
-            "26": 0.11559,
-            "27": 0.11626,
-            "28": 0.11634,
-            "29": 0.11701,
-            "30": 0.13544,
-            "31": 0.13258,
-            "32": 0.12643,
-            "33": 0.12858,
-            "34": 0.18682,
-            "35": 0.12702,
-            "36": 0.09639,
-            "37": 0.09478,
-            "38": 0.09349,
-            "39": 0.09417,
-            "40": 0.09272,
-            "41": 0.09563,
-            "42": 0.09369,
-            "43": 0.09427,
-            "44": 0.09501,
-            "45": 0.09141,
-            "46": 0.09367,
-            "47": 0.0929,
-            "48": 0.09322,
-            "49": 0.09223,
-            "50": 0.0936
+            "1": "nan",
+            "2": 2.34829,
+            "3": 0.10511,
+            "4": 0.09797,
+            "5": 0.09705,
+            "6": 0.09665,
+            "7": 0.09616,
+            "8": 0.09616,
+            "9": 0.0968,
+            "10": 0.09708,
+            "11": 0.09598,
+            "12": 0.09533,
+            "13": 0.09549,
+            "14": 0.09665,
+            "15": 0.09303,
+            "16": 0.0963,
+            "17": 0.10058,
+            "18": 0.09955,
+            "19": 0.10067,
+            "20": 0.10221,
+            "21": 0.09941,
+            "22": 0.09872,
+            "23": 0.09975,
+            "24": 0.10322,
+            "25": 0.09837,
+            "26": 0.09834,
+            "27": 0.09843,
+            "28": 0.09692,
+            "29": 0.09907,
+            "30": 0.09889,
+            "31": 0.10064,
+            "32": 0.09748,
+            "33": 0.09927,
+            "34": 0.09831,
+            "35": 0.09862,
+            "36": 0.09852,
+            "37": 0.09869,
+            "38": 0.09941,
+            "39": 0.09945,
+            "40": 0.10014,
+            "41": 0.09934,
+            "42": 0.10081,
+            "43": 0.10148,
+            "44": 0.09766,
+            "45": 0.09746,
+            "46": 0.09842,
+            "47": 0.09924,
+            "48": 0.09864,
+            "49": 0.09829,
+            "50": 0.09685
         }
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
index b280d123468..eb5a06ac1fc 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
@@ -432,106 +432,106 @@
         "end_step": 100,
         "step_interval": 1,
         "values": {
-            "1": 5.16302,
-            "2": 0.10393,
-            "3": 0.10318,
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-            "25": 0.08861,
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-            "28": 0.08976,
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_gb200.json
index ccf3054dcf0..a084bf35662 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute/golden_values_dev_dgx_gb200.json
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200.json
index 5470d60dcdb..684fd4831fe 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..f92f1c4672e
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings/golden_values_dev_dgx_gb200_2nd.json
@@ -0,0 +1,537 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
index 4143efc2988..63c74381364 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear/golden_values_dev_dgx_gb200.json
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
index 110646cd819..10988c85257 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
index 641a00e237a..a34edb3389a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr/golden_values_dev_dgx_gb200.json
@@ -432,106 +432,106 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
index 56bb24659d2..0758fd3a8cf 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..f7efd011023
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss/golden_values_dev_dgx_gb200_2nd.json
@@ -0,0 +1,537 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
index 3d38faf23fc..28843c12217 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
@@ -262,10 +262,10 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
index f4999e7c2dd..8a90b6fb7df 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
index 605457b437c..da72109d85d 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline/golden_values_dev_dgx_gb200.json
@@ -232,56 +232,56 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split/golden_values_dev_dgx_gb200.json
index d3f4ebb9b68..28de0d56b1b 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split/golden_values_dev_dgx_gb200.json
@@ -232,56 +232,56 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
index 2de96fdc0a6..a5fecfacf8f 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
@@ -6,104 +6,104 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json
index 8b51d66847b..52d95069cff 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_gb200.json
index 0c5b41565c8..9066ef0c241 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss/golden_values_dev_dgx_gb200.json
index b9bbabe5437..fea64911d52 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic/golden_values_dev_dgx_gb200.json
index 8175fe3e6be..bbc822686c6 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_gb200.json
index aa1e18f88cb..724f3a7c2b8 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
index 8858c8ab59e..bb38c983224 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last/golden_values_dev_dgx_gb200.json
index 746c6b2ba10..4e8e9932015 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
index cef90be5674..35348e75b0f 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
index 4b1a17aa98b..08b446921f5 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
index 529bad10ded..7fa492bd7eb 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..49a5e4f8a21
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
index 941c681adde..caa1e54ee64 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
index 500fc1be7cf..bffbd713b20 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..1bf0169b170
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
index c8639e2d542..04cd8d66a75 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..558ad752f07
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
index a321d71dac5..27eb21de0f0 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute/golden_values_dev_dgx_gb200.json
index 49fb0cee006..ff9932eb6ca 100644
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute/golden_values_dev_dgx_gb200.json
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new file mode 100644
index 00000000000..5a291771fe9
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200.json
index 8325c3b9e5b..e00e8181bf7 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..bb062f69f88
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone/golden_values_dev_dgx_gb200_2nd.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
index b3990651f36..7dd5b31f34f 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
index 3264336647e..85eca8a168b 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
index 4302879367b..c4a7b5d2ff0 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200_2nd.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200_2nd.json
new file mode 100644
index 00000000000..e8ad4bfea94
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce/golden_values_dev_dgx_gb200_2nd.json
@@ -0,0 +1,537 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode/golden_values_dev_dgx_gb200.json
index 4200e3b38a8..65fc98f8dd4 100644
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone/golden_values_dev_dgx_gb200.json
index 5b22c8f244c..771860e086a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone/golden_values_dev_dgx_gb200.json
+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone/golden_values_dev_dgx_gb200.json
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp2/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp2/golden_values_dev_dgx_gb200.json
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_gb200.json
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4_resume_torch_dist/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp1_pp4_resume_torch_dist/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp4_pp1_resume_torch/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp4_pp1_resume_torch/golden_values_dev_dgx_gb200.json
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+++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp4_pp1_resume_torch/golden_values_dev_dgx_gb200.json
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new file mode 100644
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_gb200.json
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
deleted file mode 100644
index 048256c3504..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 32
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 2
-  --sequence-parallel: true
-  --tp-comm-overlap: true
-  --tp-comm-overlap-cfg: tests/functional_tests/test_cases/gpt/gpt3_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-recipe: tensorwise
-  --fp8-param-gather: true
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-  --normalization: RMSNorm
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml
deleted file mode 100644
index 9c23cb7938f..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml
+++ /dev/null
@@ -1,57 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 32
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --sequence-parallel: true
-  --use-megatron-fsdp: true
-  --data-parallel-sharding-strategy: optim_grads_params
-  --no-gradient-accumulation-fusion: true
-  --use-distributed-optimizer: true
-  --deterministic-mode: true
-  --fp8-format: hybrid
-  --fp8-recipe: tensorwise
-  --first-last-layers-bf16: true
-  --fp8-param-gather: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
deleted file mode 100644
index 4727007ffe2..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
+++ /dev/null
@@ -1,57 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 32
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 4
-  --context-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-recipe: tensorwise
-  --fp8-param-gather: true
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-optim-fully-reshardable: true
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-  --normalization: RMSNorm
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_mxfp8_tp_sp_cp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_mxfp8_tp_sp_cp/model_config.yaml
deleted file mode 100644
index bba1f1ad19e..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_mxfp8_tp_sp_cp/model_config.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 32
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 4
-  --context-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-recipe: mxfp8
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
deleted file mode 100644
index 28f0a8a7b53..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 32
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 4
-  --context-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --fp8-param-gather: true
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json
deleted file mode 100644
index c759ae47565..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
-    "lm loss": {
-        "start_step": 0,
-        "end_step": 2000,
-        "step_interval": 5,
-        "values": [
-            10.8833,
-            10.90244,
-            10.88662,
-            10.83318,
-            10.6762,
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-        "end_step": 2000,
-        "step_interval": 5,
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json
deleted file mode 100644
index 18ec1c2a173..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/model_config.yaml
deleted file mode 100644
index 0e70965cb2b..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_nondet_tp1_pp1_fp8_no_model_parallel/model_config.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 1
-  --pipeline-model-parallel-size: 1
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-optim-fully-reshardable: true
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_dev_dgx_a100.json
deleted file mode 100644
index c7f6bc8588d..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1,1220 +0,0 @@
-{
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_lts_dgx_a100.json
deleted file mode 100644
index fdeaa49aa1b..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1 +0,0 @@
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1925.0, 2038.0, 2028.0, 2476.0, 2015.0, 2201.0, 2215.0, 2438.0, 3135.0, 2444.0, 2806.0, 2540.0, 2188.0, 2052.0, 2885.0, 2408.0, 3553.0, 2417.0, 2497.0, 2486.0, 3667.0, 2116.0, 2243.0, 2127.0, 2649.0, 3818.0, 2985.0, 2311.0, 2810.0, 2580.0, 2214.0, 2672.0, 2502.0, 2376.0, 2941.0, 3128.0, 2507.0, 2600.0, 2152.0, 2790.0, 3240.0, 2769.0, 2720.0, 2392.0, 3522.0, 2236.0, 2883.0, 2397.0, 2586.0, 2219.0, 3154.0, 2799.0, 2803.0, 2345.0, 2563.0, 2171.0, 2874.0, 2837.0, 2656.0, 3389.0, 2526.0, 2817.0, 2625.0, 3000.0, 2814.0, 2754.0, 2414.0, 3081.0, 2380.0, 2876.0, 2737.0, 2780.0, 2271.0, 2333.0, 2839.0, 2519.0, 3210.0, 2404.0, 2291.0, 2433.0, 2383.0, 2435.0, 1919.0, 2351.0, 2585.0, 2779.0, 2221.0, 2014.0, 2114.0, 1881.0, 2304.0, 2397.0, 2309.0, 2239.0, 2116.0, 2239.0, 2377.0, 2323.0, 2496.0, 2298.0, 2773.0, 2696.0, 1952.0, 2435.0, 2042.0, 2813.0, 2452.0, 2068.0, 2032.0, 2127.0, 2176.0, 2056.0, 2569.0, 2495.0, 2156.0, 2202.0, 2372.0, 2368.0, 2313.0, 1956.0, 2287.0, 2471.0, 2251.0, 2132.0, 1626.0, 2076.0, 2288.0, 2009.0, 1987.0, 2433.0, 1651.0, 2033.0, 2061.0, 1927.0, 2837.0, 2589.0, 2063.0, 1738.0, 1964.0, 2334.0, 1899.0, 2516.0, 2136.0, 2214.0, 1965.0, 1875.0, 2415.0, 1921.0, 2352.0, 2174.0, 1887.0, 2165.0, 2616.0, 1911.0, 1825.0, 1959.0, 1908.0, 1822.0, 1574.0, 1545.0, 2160.0, 1942.0, 2081.0, 1733.0, 2008.0, 2010.0, 2212.0, 1875.0, 1390.0, 1972.0, 2540.0, 1825.0, 2152.0, 1632.0, 2232.0, 1792.0, 1887.0, 1971.0, 2046.0, 1779.0, 2139.0, 2024.0, 1999.0, 1614.0, 1985.0, 1902.0, 2128.0, 2445.0, 2671.0, 2214.0, 2029.0, 2081.0, 2209.0, 2226.0, 1957.0, 2210.0, 2419.0, 2685.0, 2294.0, 1932.0, 2118.0, 1963.0, 1818.0, 1841.0, 2149.0, 2110.0, 2155.0, 1868.0, 2220.0, 2120.0, 2379.0, 1886.0, 2361.0, 1763.0, 2055.0, 1972.0, 2155.0, 1934.0, 2167.0, 1959.0, 1882.0, 1705.0, 1826.0, 1964.0, 2224.0, 1818.0, 1883.0, 1743.0, 2488.0, 2393.0, 2103.0, 2005.0, 2728.0, 2142.0, 2054.0, 1951.0, 1819.0, 2038.0, 2170.0, 2265.0, 1808.0, 2431.0, 1807.0, 2184.0, 2053.0, 1687.0, 1931.0, 2549.0, 2587.0, 1986.0, 2273.0, 2103.0, 2063.0, 2204.0, 2021.0, 2110.0, 2428.0, 2484.0, 2060.0, 2244.0, 2025.0, 1999.0, 1965.0, 1906.0, 2137.0, 2024.0, 2234.0, 1998.0, 2022.0, 1943.0, 2254.0, 2008.0, 1619.0, 1850.0, 2446.0, 2316.0, 1952.0, 2008.0, 2201.0, 2018.0, 2191.0, 1856.0, 2363.0, 2138.0, 2632.0, 1897.0, 2331.0, 1915.0, 2017.0, 2347.0, 2073.0, 2221.0, 2341.0, 1910.0, 1944.0, 2197.0, 2136.0, 2140.0, 2057.0, 2254.0, 1992.0, 2377.0, 1829.0, 2323.0, 2256.0, 2248.0, 2664.0, 2091.0, 2351.0, 2363.0, 2417.0, 1953.0, 2010.0, 2111.0, 2082.0, 2141.0, 2449.0, 2394.0, 2165.0, 2019.0, 2307.0, 2446.0, 2932.0, 2123.0, 2428.0, 2294.0, 2499.0, 2597.0, 2391.0, 2142.0, 2085.0, 2112.0, 2498.0, 2172.0, 2546.0, 2086.0, 2278.0, 2000.0, 2060.0, 2222.0, 2327.0, 2377.0, 2181.0, 1943.0, 2370.0, 2170.0, 2277.0, 2360.0, 2822.0, 2306.0, 2709.0, 2210.0, 2127.0, 2321.0, 2202.0, 2780.0, 2249.0, 2312.0, 2033.0, 2114.0, 2287.0, 2292.0, 2301.0, 2735.0, 2674.0, 2246.0, 2584.0, 2280.0, 2624.0, 2634.0, 2653.0, 2502.0, 2748.0, 2256.0, 2492.0, 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2397.0, 2309.0, 2239.0, 2116.0, 2239.0, 2377.0, 2323.0, 2496.0, 2298.0, 2773.0, 2696.0, 1952.0, 2435.0, 2042.0, 2813.0, 2452.0, 2068.0, 2032.0, 2127.0, 2176.0, 2056.0, 2569.0, 2495.0, 2156.0, 2202.0, 2372.0, 2368.0, 2313.0, 1956.0, 2287.0, 2471.0, 2251.0, 2132.0, 1626.0, 2076.0, 2288.0, 2009.0, 1987.0, 2433.0, 1651.0, 2033.0, 2061.0, 1927.0, 2837.0, 2589.0, 2063.0, 1738.0, 1964.0, 2334.0, 1899.0, 2516.0, 2136.0, 2214.0, 1965.0, 1875.0, 2415.0, 1921.0, 2352.0, 2174.0, 1887.0, 2165.0, 2616.0, 1911.0, 1825.0, 1959.0, 1908.0, 1822.0, 1574.0, 1545.0, 2160.0, 1942.0, 2081.0, 1733.0, 2008.0, 2010.0, 2212.0, 1875.0, 1390.0, 1972.0, 2540.0, 1825.0, 2152.0, 1632.0, 2232.0, 1792.0, 1887.0, 1971.0, 2046.0, 1779.0, 2139.0, 2024.0, 1999.0, 1614.0, 1985.0, 1902.0, 2128.0, 2445.0, 2671.0, 2214.0, 2029.0, 2081.0, 2209.0, 2226.0, 1957.0, 2210.0, 2419.0, 2685.0, 2294.0, 1932.0, 2118.0, 1963.0, 1818.0, 1841.0, 2149.0, 2110.0, 2155.0, 1868.0, 2220.0, 2120.0, 2379.0, 1886.0, 2361.0, 1763.0, 2055.0, 1972.0, 2155.0, 1934.0, 2167.0, 1959.0, 1882.0, 1705.0, 1826.0, 1964.0, 2224.0, 1818.0, 1883.0, 1743.0, 2488.0, 2393.0, 2103.0, 2005.0, 2728.0, 2142.0, 2054.0, 1951.0, 1819.0, 2038.0, 2170.0, 2265.0, 1808.0, 2431.0, 1807.0, 2184.0, 2053.0, 1687.0, 1931.0, 2549.0, 2587.0, 1986.0, 2273.0, 2103.0, 2063.0, 2204.0, 2021.0, 2110.0, 2428.0, 2484.0, 2060.0, 2244.0, 2025.0, 1999.0, 1965.0, 1906.0, 2137.0, 2024.0, 2234.0, 1998.0, 2022.0, 1943.0, 2254.0, 2008.0, 1619.0, 1850.0, 2446.0, 2316.0, 1952.0, 2008.0, 2201.0, 2018.0, 2191.0, 1856.0, 2363.0, 2138.0, 2632.0, 1897.0, 2331.0, 1915.0, 2017.0, 2347.0, 2073.0, 2221.0, 2341.0, 1910.0, 1944.0, 2197.0, 2136.0, 2140.0, 2057.0, 2254.0, 1992.0, 2377.0, 1829.0, 2323.0, 2256.0, 2248.0, 2664.0, 2091.0, 2351.0, 2363.0, 2417.0, 1953.0, 2010.0, 2111.0, 2082.0, 2141.0, 2449.0, 2394.0, 2165.0, 2019.0, 2307.0, 2446.0, 2932.0, 2123.0, 2428.0, 2294.0, 2499.0, 2597.0, 2391.0, 2142.0, 2085.0, 2112.0, 2498.0, 2172.0, 2546.0, 2086.0, 2278.0, 2000.0, 2060.0, 2222.0, 2327.0, 2377.0, 2181.0, 1943.0, 2370.0, 2170.0, 2277.0, 2360.0, 2822.0, 2306.0, 2709.0, 2210.0, 2127.0, 2321.0, 2202.0, 2780.0, 2249.0, 2312.0, 2033.0, 2114.0, 2287.0, 2292.0, 2301.0, 2735.0, 2674.0, 2246.0, 2584.0, 2280.0, 2624.0, 2634.0, 2653.0, 2502.0, 2748.0, 2256.0, 2492.0, 2276.0, 2217.0, 1995.0, 2408.0, 2306.0, 2584.0, 2373.0]}, "lm loss validation": {"start_step": 0, "end_step": 4, "step_interval": 5, "values": [5.62692]}, "lm loss validation vs samples": {"start_step": 0, "end_step": 4, "step_interval": 5, "values": [5.62692]}, "lm loss validation ppl": {"start_step": 0, "end_step": 4, "step_interval": 5, "values": [277.80627]}, "lm loss validation ppl vs samples": {"start_step": 0, "end_step": 4, "step_interval": 5, "values": [277.80627]}}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/model_config.yaml
deleted file mode 100644
index db5dea3ae6e..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_bf16_baseline/model_config.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: local
-  --tensor-model-parallel-size: 1
-  --pipeline-model-parallel-size: 1
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --fp16: true
-  --apply-query-key-layer-scaling: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json
deleted file mode 100644
index f2cc2651bba..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json
deleted file mode 100644
index 01ae9fa603f..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/model_config.yaml
deleted file mode 100644
index 12063418adf..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp1_fp8_no_model_parallel/model_config.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 1
-  --pipeline-model-parallel-size: 1
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-  --legacy-tokenizer: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_dev_dgx_a100.json
deleted file mode 100644
index 0b03b850b4b..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_lts_dgx_a100.json
deleted file mode 100644
index 0a4099a0f99..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
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-        "end_step": 2000,
-        "step_interval": 5,
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-}
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/model_config.yaml
deleted file mode 100644
index 51a2f6cfc7c..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp1_pp2_fp8_pp/model_config.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 1
-  --pipeline-model-parallel-size: 2
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-optim-fully-reshardable: true
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
deleted file mode 100644
index 9e26dfeeb6e..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
deleted file mode 100644
index 9aec16146aa..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-qkv_fprop:
-  method: ring_exchange
-  aggregate: 1
-
-proj_fprop:
-  method: pipeline
-  num_sm: 4
-  num_splits: 4
-  set_sm_margin: 0
-
-fc1_fprop:
-  method: ring_exchange
-  aggregate: 0
-
-fc2_fprop:
-  method: ring_exchange
-  aggregate: 0
-
-fc2_dgrad:
-  method: ring_exchange
-  aggregate: 0
-
-fc1_dgrad:
-  method: bulk
-  num_sm: 2
-  set_sm_margin: 0
-
-fc1_wgrad:
-  method: bulk
-  num_sm: 2
-  set_sm_margin: 0
-
-proj_dgrad:
-  method: ring_exchange
-  aggregate: 1
-
-qkv_dgrad:
-  method: bulk
-  num_sm: 2
-  set_sm_margin: 0
-
-qkv_wgrad:
-  method: bulk
-  num_sm: 2
-  set_sm_margin: 0
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json
deleted file mode 100644
index 178565f517f..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
-    "lm loss": {
-        "start_step": 0,
-        "end_step": 2000,
-        "step_interval": 5,
-        "values": [
-            10.84434,
-            10.87343,
-            10.85057,
-            10.81084,
-            10.64478,
-            10.63856,
-            10.42829,
-            10.13529,
-            9.9354,
-            9.83536,
-            9.58562,
-            9.84798,
-            9.88582,
-            9.63128,
-            9.79015,
-            9.51139,
-            9.45969,
-            9.65541,
-            9.38989,
-            9.33926,
-            9.24938,
-            9.15128,
-            9.18196,
-            9.0045,
-            9.19833,
-            9.06658,
-            9.16104,
-            9.16968,
-            9.30055,
-            8.98918,
-            8.92952,
-            9.05033,
-            9.04653,
-            8.66027,
-            8.72522,
-            8.75656,
-            8.69485,
-            8.74326,
-            8.66685,
-            8.7728,
-            8.67074,
-            8.86153,
-            8.8433,
-            8.50914,
-            8.39911,
-            8.43859,
-            8.49596,
-            8.39384,
-            8.44083,
-            8.59281,
-            8.37629,
-            8.2001,
-            8.23362,
-            8.23015,
-            8.27548,
-            7.92086,
-            8.10003,
-            7.89799,
-            8.25216,
-            8.23462,
-            8.01021,
-            7.97597,
-            7.9264,
-            7.74459,
-            7.748,
-            7.65018,
-            7.52046,
-            7.91112,
-            7.70254,
-            7.456,
-            7.74697,
-            7.77483,
-            7.54415,
-            7.3027,
-            7.45591,
-            7.34318,
-            7.46577,
-            7.22819,
-            7.63648,
-            7.28207,
-            7.34835,
-            7.21309,
-            7.21075,
-            7.41924,
-            7.17318,
-            7.28141,
-            6.99426,
-            7.00286,
-            7.03961,
-            7.13676,
-            6.822,
-            6.9855,
-            7.08945,
-            6.99871,
-            6.87487,
-            6.75719,
-            6.99117,
-            7.06005,
-            6.70456,
-            6.58452,
-            6.72787,
-            6.74473,
-            6.73373,
-            6.7382,
-            6.6584,
-            6.40648,
-            6.63688,
-            6.61955,
-            6.44576,
-            6.62788,
-            6.74244,
-            6.61006,
-            6.72544,
-            6.69264,
-            6.62569,
-            6.50572,
-            6.59635,
-            6.40504,
-            6.66311,
-            6.24639,
-            6.25134,
-            6.30293,
-            6.39011,
-            6.3472,
-            6.45168,
-            6.29229,
-            6.33985,
-            6.23688,
-            6.20384,
-            6.40017,
-            6.32742,
-            6.32422,
-            6.16691,
-            6.16021,
-            6.24067,
-            6.38468,
-            6.20364,
-            6.15286,
-            6.18196,
-            6.11784,
-            6.06616,
-            6.07804,
-            6.26273,
-            6.41356,
-            6.26419,
-            6.30289,
-            6.10616,
-            6.18152,
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-            6.03597,
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-            6.25362,
-            6.19475,
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-            6.1312,
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-            5.79121,
-            6.16545,
-            6.14698,
-            6.08542,
-            5.92808,
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-            5.4109,
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-            5.47792,
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-            5.71472,
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-            5.63428,
-            5.23158,
-            5.26784,
-            5.39219,
-            5.39546,
-            5.32677,
-            5.49847,
-            5.18449,
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-            5.4427,
-            5.53544,
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-            5.43162,
-            5.34057,
-            5.07742,
-            5.3105,
-            5.2513,
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-            5.10864,
-            5.27348,
-            5.26261,
-            5.47314,
-            5.15993,
-            5.26482,
-            5.20655,
-            5.3524,
-            4.98067,
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-            5.39056,
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-            5.16075,
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-        ]
-    },
-    "num-zeros": {
-        "start_step": 0,
-        "end_step": 2000,
-        "step_interval": 5,
-        "values": [
-            65.0,
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json
deleted file mode 100644
index f822a205e19..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/model_config.yaml
deleted file mode 100644
index 8d85af9b7d4..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp/model_config.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 2
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json
deleted file mode 100644
index 0c3d0a67e66..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json
deleted file mode 100644
index a35e26a051e..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/model_config.yaml
deleted file mode 100644
index 54237309642..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_fp8_tp_pp_sp/model_config.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json
deleted file mode 100644
index 392f14d9abc..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json
deleted file mode 100644
index f451bade907..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/model_config.yaml
deleted file mode 100644
index a5903e51b6c..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_native_fp8_tp_pp_sp/model_config.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --pipeline-model-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --fp8-param-gather: true
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-optim-fully-reshardable: true
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml
deleted file mode 100644
index ac5ff6cfbbf..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp/model_config.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-ENV_VARS:
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 2
-  --sequence-parallel: true
-  --use-megatron-fsdp: true
-  --data-parallel-sharding-strategy: optim_grads_params
-  --no-gradient-accumulation-fusion: true
-  --use-distributed-optimizer: true
-  --deterministic-mode: true
-  --fp8-format: hybrid
-  --fp8-recipe: tensorwise
-  --first-last-layers-bf16: true
-  --fp8-param-gather: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
deleted file mode 100644
index 0322fc3db23..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
+++ /dev/null
@@ -1,2042 +0,0 @@
-{
-    "lm loss": {
-        "start_step": 1,
-        "end_step": 2000,
-        "step_interval": 5,
-        "values": {
-            "1": 10.84749,
-            "5": 10.85081,
-            "10": 10.84524,
-            "15": 10.77884,
-            "20": 10.6881,
-            "25": 10.55833,
-            "30": 10.26993,
-            "35": 10.23479,
-            "40": 10.06687,
-            "45": 10.00575,
-            "50": 9.94171,
-            "55": 9.73746,
-            "60": 9.64473,
-            "65": 9.59425,
-            "70": 9.42245,
-            "75": 9.23951,
-            "80": 9.35863,
-            "85": 9.39876,
-            "90": 9.41238,
-            "95": 9.41795,
-            "100": 9.35646,
-            "105": 9.37485,
-            "110": 9.36517,
-            "115": 9.32133,
-            "120": 9.08081,
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-            "130": 9.13834,
-            "135": 8.88684,
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-            "145": 9.10362,
-            "150": 9.00925,
-            "155": 9.13594,
-            "160": 8.89055,
-            "165": 8.92906,
-            "170": 9.04618,
-            "175": 8.70851,
-            "180": 8.74263,
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-            "190": 8.53632,
-            "195": 8.59356,
-            "200": 8.73544,
-            "205": 8.22364,
-            "210": 8.58241,
-            "215": 8.56524,
-            "220": 8.54641,
-            "225": 8.57187,
-            "230": 8.50022,
-            "235": 8.56346,
-            "240": 8.67349,
-            "245": 8.59931,
-            "250": 8.426,
-            "255": 8.29932,
-            "260": 8.24366,
-            "265": 8.3358,
-            "270": 8.26862,
-            "275": 8.01255,
-            "280": 7.75947,
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-            "1905": 7.00112,
-            "1910": 7.02281,
-            "1915": 6.78654,
-            "1920": 6.8245,
-            "1925": 6.78352,
-            "1930": 6.82028,
-            "1935": 7.04035,
-            "1940": 6.84142,
-            "1945": 6.82102,
-            "1950": 6.78053,
-            "1955": 6.80661,
-            "1960": "nan",
-            "1965": "nan",
-            "1970": "nan",
-            "1975": "nan",
-            "1980": "nan",
-            "1985": "nan",
-            "1990": "nan",
-            "1995": "nan",
-            "2000": "nan"
-        }
-    }
-}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
deleted file mode 100644
index ddb34ad850b..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
+++ /dev/null
@@ -1,61 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NON_DETERMINSTIC_RESULTS: 1
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-  NCCL_ALGO: Ring
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 4
-  --context-parallel-size: 2
-  --sequence-parallel: true
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --fp8-param-gather: true
-  --use-distributed-optimizer: true
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
-METRICS:
-  - iteration-time
-  - lm loss
-  - "mem-allocated-bytes"
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json
deleted file mode 100644
index 410ce0432c2..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_dev_dgx_a100.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json
deleted file mode 100644
index 0463c4d01dc..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/golden_values_lts_dgx_a100.json
+++ /dev/null
@@ -1,1223 +0,0 @@
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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/model_config.yaml
deleted file mode 100644
index cf9f6b6ceb8..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_pp2_fp8_tp_pp/model_config.yaml
+++ /dev/null
@@ -1,54 +0,0 @@
-ENV_VARS:
-  CUDA_DEVICE_MAX_CONNECTIONS: 1
-  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
-  NVTE_FUSED_ATTN: 0
-  NCCL_ALGO: Ring
-  CUBLAS_WORKSPACE_CONFIG: :4096:8
-MODEL_ARGS:
-  --num-layers: 12
-  --hidden-size: 512
-  --num-attention-heads: 8
-  --log-params-norm: true
-  --log-num-zeros-in-grad: true
-  --log-validation-ppl-to-tensorboard: true
-  --log-timers-to-tensorboard: true
-  --tensorboard-dir: ${TENSORBOARD_PATH}
-  --micro-batch-size: 2
-  --global-batch-size: 128
-  --seq-length: 1024
-  --max-position-embeddings: 1024
-  --train-iters: 2000
-  --timing-log-level: 0
-  --lr-decay-iters: 320000
-  --save: ${CHECKPOINT_SAVE_PATH}
-  --load: ${CHECKPOINT_LOAD_PATH}
-  --data-path: ${DATA_PATH}/text/the_pile/shard00/my-gpt3_00_text_document
-  --vocab-file: ${DATA_PATH}/text/the_pile/shard00/bpe/vocab.json
-  --merge-file: ${DATA_PATH}/text/the_pile/shard00/bpe/merges.txt
-  --split: 949,50,1
-  --distributed-backend: nccl
-  --lr: 0.00015
-  --lr-decay-style: cosine
-  --min-lr: 1.0e-5
-  --weight-decay: 1e-2
-  --clip-grad: 1.0
-  --lr-warmup-fraction: .01
-  --log-interval: 1
-  --save-interval: 10000
-  --eval-interval: 1000
-  --eval-iters: 10
-  --transformer-impl: transformer_engine
-  --tensor-model-parallel-size: 4
-  --pipeline-model-parallel-size: 2
-  --deterministic-mode: true
-  --no-gradient-accumulation-fusion: true
-  --fp8-format: hybrid
-  --fp8-amax-history-len: 1024
-  --fp8-amax-compute-algo: max
-  --attention-softmax-in-fp32: true
-  --ckpt-format: torch_dist
-  --dist-ckpt-strictness: log_all # backward compatibility for TE changes
-  --data-cache-path: ${DATA_CACHE_PATH}
-  --bf16: true
-  --log-memory-to-tensorboard: true
-TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..701a1e8451e
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,10037 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 2000,
+        "step_interval": 1,
+        "values": {
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..8353ea30868
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/golden_values_dev_dgx_h100.json
@@ -0,0 +1,10037 @@
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+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
similarity index 94%
rename from tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
rename to tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
index 15ac9782df5..ae8a2368c59 100644
--- a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/model_config.yaml
@@ -42,7 +42,7 @@ MODEL_ARGS:
   --pipeline-model-parallel-size: 2
   --sequence-parallel: true
   --tp-comm-overlap: true
-  --tp-comm-overlap-cfg: tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
+  --tp-comm-overlap-cfg: tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
   --deterministic-mode: true
   --no-gradient-accumulation-fusion: true
   --fp8-format: hybrid
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
similarity index 100%
rename from tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
rename to tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap/tp_comm_overlap_cfg.yaml
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..477c5e1e929
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,10037 @@
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+            "2000": "nan"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..a5003a9a581
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/golden_values_dev_dgx_h100.json
@@ -0,0 +1,10037 @@
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+            "1804": 5.15838,
+            "1805": 5.14636,
+            "1806": 5.1295,
+            "1807": 5.08206,
+            "1808": 5.45997,
+            "1809": 5.45327,
+            "1810": 5.06671,
+            "1811": 5.07095,
+            "1812": 5.07707,
+            "1813": 5.07262,
+            "1814": 5.07022,
+            "1815": 5.06214,
+            "1816": 5.06912,
+            "1817": 5.43075,
+            "1818": 5.42966,
+            "1819": 5.07143,
+            "1820": 5.07984,
+            "1821": 5.07226,
+            "1822": 5.06933,
+            "1823": 5.42747,
+            "1824": 5.06705,
+            "1825": 5.07382,
+            "1826": 5.0721,
+            "1827": 5.4045,
+            "1828": 5.07771,
+            "1829": 5.07055,
+            "1830": 5.40891,
+            "1831": 5.07156,
+            "1832": 5.25378,
+            "1833": 5.07615,
+            "1834": 5.0672,
+            "1835": 5.07176,
+            "1836": 5.06938,
+            "1837": 5.48088,
+            "1838": 5.06781,
+            "1839": 5.07661,
+            "1840": 5.06533,
+            "1841": 5.06765,
+            "1842": 5.06904,
+            "1843": 5.0773,
+            "1844": 5.06886,
+            "1845": 5.07763,
+            "1846": 5.07622,
+            "1847": 5.06669,
+            "1848": 5.06576,
+            "1849": 5.07058,
+            "1850": 5.0606,
+            "1851": 5.06931,
+            "1852": 5.0673,
+            "1853": 5.07348,
+            "1854": 5.87052,
+            "1855": 5.07533,
+            "1856": 5.4281,
+            "1857": 5.46793,
+            "1858": 5.0622,
+            "1859": 5.06196,
+            "1860": 5.06425,
+            "1861": 5.05655,
+            "1862": 5.06202,
+            "1863": 5.0669,
+            "1864": 5.06323,
+            "1865": 5.05668,
+            "1866": 5.05432,
+            "1867": 5.07603,
+            "1868": 5.41613,
+            "1869": 5.08419,
+            "1870": 5.07021,
+            "1871": 5.06977,
+            "1872": 5.39161,
+            "1873": 5.07573,
+            "1874": 5.07542,
+            "1875": 5.4012,
+            "1876": 5.06952,
+            "1877": 5.07303,
+            "1878": 5.06937,
+            "1879": 5.07305,
+            "1880": 5.07802,
+            "1881": 5.07534,
+            "1882": 5.06634,
+            "1883": 5.47628,
+            "1884": 5.08256,
+            "1885": 5.06709,
+            "1886": 5.06237,
+            "1887": 5.12976,
+            "1888": 5.06308,
+            "1889": 5.0508,
+            "1890": 5.05491,
+            "1891": 5.06096,
+            "1892": 5.05391,
+            "1893": 5.05913,
+            "1894": 5.41551,
+            "1895": 5.06629,
+            "1896": 5.41654,
+            "1897": 5.0681,
+            "1898": 5.07182,
+            "1899": 5.05154,
+            "1900": 5.45369,
+            "1901": 5.44573,
+            "1902": 5.07088,
+            "1903": 5.06024,
+            "1904": 5.07707,
+            "1905": 5.05626,
+            "1906": 5.05725,
+            "1907": 5.06171,
+            "1908": 5.06713,
+            "1909": 5.0525,
+            "1910": 5.08074,
+            "1911": 5.06403,
+            "1912": 5.05465,
+            "1913": 5.04894,
+            "1914": 5.05309,
+            "1915": 5.40228,
+            "1916": 5.07171,
+            "1917": 5.05823,
+            "1918": 5.39099,
+            "1919": 5.05159,
+            "1920": 5.05778,
+            "1921": 5.38511,
+            "1922": 5.07076,
+            "1923": 5.07063,
+            "1924": 5.14236,
+            "1925": 5.05948,
+            "1926": 5.06056,
+            "1927": 5.05815,
+            "1928": 5.06681,
+            "1929": 5.07359,
+            "1930": 5.48554,
+            "1931": 5.05767,
+            "1932": 5.0521,
+            "1933": 5.41718,
+            "1934": 5.07001,
+            "1935": 5.41834,
+            "1936": 5.07154,
+            "1937": 5.06933,
+            "1938": 5.06182,
+            "1939": 5.05946,
+            "1940": 5.06601,
+            "1941": 5.06052,
+            "1942": 5.0554,
+            "1943": 5.05652,
+            "1944": 5.05613,
+            "1945": 5.0524,
+            "1946": 5.83783,
+            "1947": 5.07128,
+            "1948": 5.07209,
+            "1949": 5.23523,
+            "1950": 5.077,
+            "1951": 5.07113,
+            "1952": 5.05952,
+            "1953": 5.05624,
+            "1954": 5.05609,
+            "1955": 5.06821,
+            "1956": 5.05633,
+            "1957": 5.063,
+            "1958": 5.09276,
+            "1959": 5.06044,
+            "1960": 5.05582,
+            "1961": 5.3945,
+            "1962": 5.35795,
+            "1963": 5.06903,
+            "1964": 5.40823,
+            "1965": 5.08139,
+            "1966": 5.07607,
+            "1967": 5.07692,
+            "1968": 5.41012,
+            "1969": 5.08771,
+            "1970": 5.09622,
+            "1971": 5.07575,
+            "1972": 5.4471,
+            "1973": 5.43483,
+            "1974": 5.08967,
+            "1975": 5.09622,
+            "1976": 5.09101,
+            "1977": 5.48877,
+            "1978": 5.07838,
+            "1979": 5.0851,
+            "1980": 5.07579,
+            "1981": 5.08598,
+            "1982": 5.07966,
+            "1983": 5.08149,
+            "1984": 5.08582,
+            "1985": 5.08557,
+            "1986": 5.08421,
+            "1987": 5.09746,
+            "1988": 5.10839,
+            "1989": 5.2448,
+            "1990": 5.08292,
+            "1991": 5.47048,
+            "1992": 5.45932,
+            "1993": 5.0934,
+            "1994": 5.1096,
+            "1995": 5.08606,
+            "1996": 5.09155,
+            "1997": 5.0958,
+            "1998": 5.08849,
+            "1999": 5.09272,
+            "2000": 5.09949
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
similarity index 100%
rename from tests/functional_tests/test_cases/gpt/gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
rename to tests/functional_tests/test_cases/gpt/gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap/model_config.yaml
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/env_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/env_config.yaml
similarity index 100%
rename from tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/env_config.yaml
rename to tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/env_config.yaml
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/model_config.yaml
similarity index 58%
rename from tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/model_config.yaml
rename to tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/model_config.yaml
index b74417a898b..0143a39f017 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/model_config.yaml
@@ -3,82 +3,97 @@ ENV_VARS:
   NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
+  SKIP_PYTEST: 1
 TEST_TYPE: frozen-start
 MODE: rl
 MODEL_ARGS:
-  --tiktoken-pattern: v2
-  --use-mcore-models: true
-  --tokenizer-type: TikTokenizer
-  --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/mcore_mistral/nemo_minitron-0.5b/v1/multiMixV8.gpt4o_nc_sd.500000.128k.vocab.json
-  --load: ${CHECKPOINT_LOAD_PATH}/model/mcore_mistral/nemo_minitron-0.5b/v1/
-  --auto-detect-ckpt-format: true
-  --max-tokens-to-oom: 3600000
-  --inference-max-seq-length: 1024
-  --attention-backend: flash
-  --mock-data: true
-  --micro-batch-size: 1
-  --no-load-optim: true
-  --no-use-tokenizer-model-from-checkpoint-args: true
-  --timing-log-level: 0
-  --distributed-backend: nccl
-  --log-interval: 1
-  --log-progress: true
-  --transformer-impl: transformer_engine
   --tensor-model-parallel-size: 2
   --pipeline-model-parallel-size: 4
+  --rl-inference-tensor-model-parallel-size: 1
+  --rl-inference-pipeline-model-parallel-size: 2
+  --refit-method: gloo
+  --inference-dynamic-batching-num-cuda-graphs: 1
+  --inference-dynamic-batching-unified-memory-level: 1
+  --inference-dynamic-batching-buffer-size-gb: 20
   --ckpt-format: torch_dist
+  --seq-length: 1024
+  --inference-max-seq-length: 1024
+  --load: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist
+  --untie-embeddings-and-output-weights: true
+  --num-layers: 36
+  --hidden-size: 4096
+  --ffn-hidden-size: 12288
+  --num-attention-heads: 32
+  --kv-channels: 128
+  --max-position-embeddings: 1024
+  --group-query-attention: true
+  --num-query-groups: 8
+  --normalization: RMSNorm
+  --norm-epsilon: 0.000001
+  --qk-layernorm: true
+  --position-embedding-type: rope
+  --rotary-percent: 1.0
+  --rotary-base: 1000000
+  --use-rotary-position-embeddings: true
+  --swiglu: true
+  --disable-bias-linear: true
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --no-masked-softmax-fusion: true
+  --attention-softmax-in-fp32: true
+  --tokenizer-type: HuggingFaceTokenizer
+  --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
+  --vocab-size: 151936
+  --make-vocab-size-divisible-by: 128
+  --optimizer: adam
+  --adam-beta1: 0.9
+  --adam-beta2: 0.999
+  --adam-eps: 0.00000001
+  --lr: 0.000001
+  --min-lr: 0.0000001
+  --lr-warmup-samples: 0
+  --clip-grad: 1.0
+  --weight-decay: 0.01
+  --use-mcore-models: true
   --bf16: true
   --log-memory-to-tensorboard: true
   --log-num-zeros-in-grad: true
   --log-validation-ppl-to-tensorboard: true
   --log-timers-to-tensorboard: true
-  --num-layers: 24
-  --hidden-size: 1152
-  --num-attention-heads: 16
-  --max-position-embeddings: 1024
-  --seq-length: 1024
   --timing-log-option: minmax
   --log-throughput: true
   --no-create-attention-mask-in-dataloader: true
   --straggler-minmax-count: 16
   --tensorboard-log-interval: 1
+  --log-interval: 1
+  --log-progress: true
   --empty-unused-memory-level: 2
-  --langrl-inference-server-type: inplace_megatron
   --seed: 42
   --calculate-per-token-loss: true
   --rl-use-sequence-packing: true
   --rl-sequence-packing-algo: fifo
   --rl-offload-optimizer-during-inference: true
   --timing-log-level: 1
-  --log-timers-to-tensorboard: true
   --cuda-graph-impl: local
   --micro-batch-size: 1
-  --global-batch-size: 16
+  --global-batch-size: 4
   --grpo-group-size: 2
-  --grpo-prompts-per-step: 8
+  --grpo-prompts-per-step: 2
   --grpo-iterations: 1
   --grpo-clamp-eps-lower: 0.2
   --grpo-clamp-eps-upper: 0.2
   --grpo-kl-beta: 0.0
   --grpo-entropy-term-weight: 0.0
-  --langrl-env-config: tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest/env_config.yaml
+  --langrl-env-config: tests/functional_tests/test_cases/gpt/gpt_grpo_basic_function/env_config.yaml
   --rl-partial-rollouts: true
-  --lr: 0.000001
-  --lr-warmup-samples: 0
-  --clip-grad: 1.0
   --use-checkpoint-args: true
   --dist-ckpt-strictness: log_unexpected
   --perform-rl-step: true
   --train-samples: 48828125
-  --exit-interval: 50
+  --exit-interval: 3
   --tensorboard-dir: ${TENSORBOARD_PATH}
   --save-interval: 1000000
   --eval-interval: 1000000
   --finetune: true
   --inference-logging-step-interval: 1
-  --rl-inference-tensor-model-parallel-size: 1
-  --rl-inference-pipeline-model-parallel-size: 2
-  --refit-method: gloo
-METRICS:
-  - "mem-allocated-bytes"
-  - "mem-max-allocated-bytes"
+METRICS: []
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest/model_config.yaml
index b12911358f0..4f9be214289 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest/model_config.yaml
@@ -43,7 +43,6 @@ MODEL_ARGS:
   --straggler-minmax-count: 16
   --tensorboard-log-interval: 1
   --empty-unused-memory-level: 2
-  --langrl-inference-server-type: inplace_megatron
   --seed: 42
   --calculate-per-token-loss: true
   --rl-use-sequence-packing: true
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest_github/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest_github/model_config.yaml
index bff55aea7fe..c8fa19d0500 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest_github/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest_github/model_config.yaml
@@ -43,7 +43,6 @@ MODEL_ARGS:
   --straggler-minmax-count: 16
   --tensorboard-log-interval: 1
   --empty-unused-memory-level: 2
-  --langrl-inference-server-type: inplace_megatron
   --seed: 42
   --calculate-per-token-loss: true
   --rl-use-sequence-packing: true
@@ -78,3 +77,13 @@ MODEL_ARGS:
   --inference-logging-step-interval: 1
   --rl-inference-tensor-model-parallel-size: 2
   --refit-method: gloo
+
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "num-zeros"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
+
+THROUGHPUT_TEST_PARAMS:
+  --start_step: 1
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/golden_values_dev_dgx_h100.json
deleted file mode 100644
index d985f671cab..00000000000
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest/golden_values_dev_dgx_h100.json
+++ /dev/null
@@ -1,173 +0,0 @@
-{
-    "mem-allocated-bytes": {
-        "start_step": 1,
-        "end_step": 50,
-        "step_interval": 1,
-        "values": {
-            "1": 48967716864.0,
-            "2": 48973631488.0,
-            "3": 48974528512.0,
-            "4": 48971538432.0,
-            "5": 48974340096.0,
-            "6": 48974143488.0,
-            "7": 48977002496.0,
-            "8": 48975851520.0,
-            "9": 48974036992.0,
-            "10": 48973709312.0,
-            "11": 48973262848.0,
-            "12": 48973705216.0,
-            "13": 48973598720.0,
-            "14": 48976703488.0,
-            "15": 48975118336.0,
-            "16": 48977072128.0,
-            "17": 48976465920.0,
-            "18": 48976470016.0,
-            "19": 48976478208.0,
-            "20": 48976654336.0,
-            "21": 48976793600.0,
-            "22": 48976052224.0,
-            "23": 48976277504.0,
-            "24": 48974708736.0,
-            "25": 48973062144.0,
-            "26": 48976236544.0,
-            "27": 48975970304.0,
-            "28": 48976711680.0,
-            "29": 48975593472.0,
-            "30": 48977321984.0,
-            "31": 48977506304.0,
-            "32": 48976646144.0,
-            "33": 48976072704.0,
-            "34": 48973631488.0,
-            "35": 48976650240.0,
-            "36": 48975650816.0,
-            "37": 48974950400.0,
-            "38": 48972750848.0,
-            "39": 48976617472.0,
-            "40": 48979308544.0,
-            "41": 48978587648.0,
-            "42": 48975626240.0,
-            "43": 48975089664.0,
-            "44": 48973688832.0,
-            "45": 48975327232.0,
-            "46": 48975159296.0,
-            "47": 48975372288.0,
-            "48": 48973856768.0,
-            "49": 48973377536.0,
-            "50": 48975568896.0
-        }
-    },
-    "mem-max-allocated-bytes": {
-        "start_step": 1,
-        "end_step": 50,
-        "step_interval": 1,
-        "values": {
-            "1": 49090379776.0,
-            "2": 49937022976.0,
-            "3": 49938366464.0,
-            "4": 49938366464.0,
-            "5": 49938366464.0,
-            "6": 49938698240.0,
-            "7": 49939156992.0,
-            "8": 49939156992.0,
-            "9": 49939156992.0,
-            "10": 49939156992.0,
-            "11": 49939156992.0,
-            "12": 49939156992.0,
-            "13": 49939156992.0,
-            "14": 49940287488.0,
-            "15": 49940287488.0,
-            "16": 49940287488.0,
-            "17": 49941729280.0,
-            "18": 49941733376.0,
-            "19": 49941741568.0,
-            "20": 49941778432.0,
-            "21": 49941778432.0,
-            "22": 49941778432.0,
-            "23": 49941778432.0,
-            "24": 49941778432.0,
-            "25": 49941778432.0,
-            "26": 49941778432.0,
-            "27": 49941934080.0,
-            "28": 49941934080.0,
-            "29": 49941934080.0,
-            "30": 49941934080.0,
-            "31": 49942675456.0,
-            "32": 49942675456.0,
-            "33": 49942675456.0,
-            "34": 49942675456.0,
-            "35": 49942675456.0,
-            "36": 49942675456.0,
-            "37": 49942675456.0,
-            "38": 49942675456.0,
-            "39": 49942675456.0,
-            "40": 49944379392.0,
-            "41": 49944379392.0,
-            "42": 49944379392.0,
-            "43": 49944379392.0,
-            "44": 49944379392.0,
-            "45": 49944379392.0,
-            "46": 49944379392.0,
-            "47": 49944379392.0,
-            "48": 49944379392.0,
-            "49": 49944379392.0,
-            "50": 49944379392.0
-        }
-    },
-    "iteration-time": {
-        "start_step": 1,
-        "end_step": 50,
-        "step_interval": 1,
-        "values": {
-            "1": 63.07516,
-            "2": 4.36236,
-            "3": 3.83222,
-            "4": 3.85784,
-            "5": 3.74494,
-            "6": 3.82661,
-            "7": 4.05458,
-            "8": 3.76622,
-            "9": 3.90518,
-            "10": 4.09283,
-            "11": 3.96358,
-            "12": 3.85778,
-            "13": 3.84546,
-            "14": 3.85497,
-            "15": 4.35749,
-            "16": 3.7861,
-            "17": 3.8896,
-            "18": 3.6267,
-            "19": 3.76463,
-            "20": 3.6953,
-            "21": 3.63427,
-            "22": 3.66652,
-            "23": 3.60379,
-            "24": 3.57701,
-            "25": 3.57327,
-            "26": 3.71371,
-            "27": 3.69626,
-            "28": 3.89285,
-            "29": 3.62405,
-            "30": 3.58297,
-            "31": 3.56993,
-            "32": 3.75257,
-            "33": 3.72279,
-            "34": 3.48095,
-            "35": 3.60831,
-            "36": 3.74971,
-            "37": 3.72155,
-            "38": 3.51054,
-            "39": 3.64562,
-            "40": 3.66038,
-            "41": 3.86018,
-            "42": 3.58341,
-            "43": 3.82647,
-            "44": 3.85728,
-            "45": 3.62416,
-            "46": 3.59141,
-            "47": 3.74512,
-            "48": 3.61762,
-            "49": 3.57079,
-            "50": 3.66209
-        }
-    }
-}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/env_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/env_config.yaml
new file mode 100644
index 00000000000..329246987bf
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/env_config.yaml
@@ -0,0 +1,5 @@
+- agent_type: examples.rl.environments.countdown.countdown_agent.CountdownAgent
+  agent_args:
+    dataset_file: "/mnt/artifacts/rl_environments/Jiayi-Pan___countdown-tasks-3to4"
+    split: "train"
+  weight: 1.0
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..42c13292446
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json
@@ -0,0 +1,83 @@
+{
+    "mem-allocated-bytes": {
+        "start_step": 1,
+        "end_step": 20,
+        "step_interval": 1,
+        "values": {
+            "1": 60922068992.0,
+            "2": 60922068992.0,
+            "3": 60922331136.0,
+            "4": 60922073088.0,
+            "5": 60922331136.0,
+            "6": 60922064896.0,
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+            "8": 60922064896.0,
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+            "11": 60922056704.0,
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+            "13": 60922056704.0,
+            "14": 60922318848.0,
+            "15": 60922056704.0,
+            "16": 60922310656.0,
+            "17": 60922052608.0,
+            "18": 60922052608.0,
+            "19": 60922048512.0,
+            "20": 60922044416.0
+        }
+    },
+    "mem-max-allocated-bytes": {
+        "start_step": 1,
+        "end_step": 20,
+        "step_interval": 1,
+        "values": {
+            "1": 60922073088.0,
+            "2": 64156037120.0,
+            "3": 64156037120.0,
+            "4": 64156041216.0,
+            "5": 64156041216.0,
+            "6": 64156041216.0,
+            "7": 64156041216.0,
+            "8": 64156041216.0,
+            "9": 64156041216.0,
+            "10": 64156041216.0,
+            "11": 64156041216.0,
+            "12": 64156041216.0,
+            "13": 64156041216.0,
+            "14": 64156041216.0,
+            "15": 64156041216.0,
+            "16": 64156041216.0,
+            "17": 64156041216.0,
+            "18": 64156041216.0,
+            "19": 64156041216.0,
+            "20": 64156041216.0
+        }
+    },
+    "iteration-time": {
+        "start_step": 1,
+        "end_step": 20,
+        "step_interval": 1,
+        "values": {
+            "1": "nan",
+            "2": 60.37194,
+            "3": 13.25967,
+            "4": 13.01461,
+            "5": 14.04256,
+            "6": 13.53259,
+            "7": 13.3335,
+            "8": 12.72344,
+            "9": 13.64787,
+            "10": 12.66485,
+            "11": 13.15779,
+            "12": 13.01275,
+            "13": 12.72481,
+            "14": 12.67697,
+            "15": 12.7286,
+            "16": 12.65032,
+            "17": 12.86279,
+            "18": 12.71745,
+            "19": 13.4137,
+            "20": 12.75566
+        }
+    }
+}
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/model_config.yaml
new file mode 100644
index 00000000000..f3c0c4ecc5b
--- /dev/null
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/model_config.yaml
@@ -0,0 +1,101 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: 1
+  THROUGHPUT_START_STEP: 10
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
+  NCCL_ALGO: Ring
+  CUBLAS_WORKSPACE_CONFIG: :4096:8
+  N_REPEAT: 1
+TEST_TYPE: frozen-start
+MODE: rl
+MODEL_ARGS:
+  --tensor-model-parallel-size: 4
+  --inference-dynamic-batching-num-cuda-graphs: 1
+  --inference-dynamic-batching-unified-memory-level: 1
+  --inference-dynamic-batching-buffer-size-gb: 20
+  --ckpt-format: torch_dist
+  --seq-length: 1024
+  --inference-max-seq-length: 1024
+  --load: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist
+  --untie-embeddings-and-output-weights: true
+  --num-layers: 36
+  --hidden-size: 4096
+  --ffn-hidden-size: 12288
+  --num-attention-heads: 32
+  --kv-channels: 128
+  --max-position-embeddings: 1024
+  --group-query-attention: true
+  --num-query-groups: 8
+  --normalization: RMSNorm
+  --norm-epsilon: 0.000001
+  --qk-layernorm: true
+  --position-embedding-type: rope
+  --rotary-percent: 1.0
+  --rotary-base: 1000000
+  --use-rotary-position-embeddings: true
+  --swiglu: true
+  --disable-bias-linear: true
+  --attention-dropout: 0.0
+  --hidden-dropout: 0.0
+  --no-masked-softmax-fusion: true
+  --attention-softmax-in-fp32: true
+  --tokenizer-type: HuggingFaceTokenizer
+  --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
+  --vocab-size: 151936
+  --make-vocab-size-divisible-by: 128
+  --optimizer: adam
+  --adam-beta1: 0.9
+  --adam-beta2: 0.999
+  --adam-eps: 0.00000001
+  --lr: 0.000001  
+  --min-lr: 0.0000001
+  --lr-warmup-samples: 0
+  --clip-grad: 1.0
+  --weight-decay: 0.01
+  --deterministic-mode: true
+  --use-mcore-models: true
+  --bf16: true
+  --log-memory-to-tensorboard: true
+  --log-num-zeros-in-grad: true
+  --log-validation-ppl-to-tensorboard: true
+  --log-timers-to-tensorboard: true
+  --timing-log-option: minmax
+  --log-throughput: true
+  --no-create-attention-mask-in-dataloader: true
+  --straggler-minmax-count: 16
+  --tensorboard-log-interval: 1
+  --log-interval: 1
+  --log-progress: true
+  --empty-unused-memory-level: 2
+  --seed: 42
+  --calculate-per-token-loss: true
+  --rl-use-sequence-packing: true
+  --rl-sequence-packing-algo: fifo
+  --rl-offload-optimizer-during-inference: true
+  --timing-log-level: 1
+  --cuda-graph-impl: local
+  --micro-batch-size: 1
+  --global-batch-size: 4
+  --grpo-group-size: 2
+  --grpo-prompts-per-step: 2
+  --grpo-iterations: 1
+  --grpo-clamp-eps-lower: 0.2
+  --grpo-clamp-eps-upper: 0.2
+  --grpo-kl-beta: 0.0
+  --grpo-entropy-term-weight: 0.0
+  --langrl-env-config: tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/env_config.yaml
+  --rl-partial-rollouts: true
+  --use-checkpoint-args: true
+  --dist-ckpt-strictness: log_unexpected
+  --perform-rl-step: true
+  --train-samples: 48828125
+  --exit-interval: 20
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --save-interval: 1000000
+  --eval-interval: 1000000
+  --finetune: true
+  --inference-logging-step-interval: 1
+  --rl-training-cuda-graphs: true
+METRICS:
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
+  - "iteration-time"
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/golden_values_dev_dgx_h100.json
index 4a8586c8e8a..42c13292446 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/golden_values_dev_dgx_h100.json
@@ -4,26 +4,26 @@
         "end_step": 20,
         "step_interval": 1,
         "values": {
-            "1": 60978671616.0,
-            "2": 60979740672.0,
-            "3": 60979740672.0,
-            "4": 60979740672.0,
-            "5": 60979998720.0,
-            "6": 60979732480.0,
-            "7": 60979994624.0,
-            "8": 60979728384.0,
-            "9": 60979986432.0,
-            "10": 60980248576.0,
-            "11": 60979982336.0,
-            "12": 60979720192.0,
-            "13": 60979716096.0,
-            "14": 60979716096.0,
-            "15": 60979716096.0,
-            "16": 60979716096.0,
-            "17": 60979712000.0,
-            "18": 60979970048.0,
-            "19": 60979716096.0,
-            "20": 60979716096.0
+            "1": 60922068992.0,
+            "2": 60922068992.0,
+            "3": 60922331136.0,
+            "4": 60922073088.0,
+            "5": 60922331136.0,
+            "6": 60922064896.0,
+            "7": 60922331136.0,
+            "8": 60922064896.0,
+            "9": 60922322944.0,
+            "10": 60922052608.0,
+            "11": 60922056704.0,
+            "12": 60922318848.0,
+            "13": 60922056704.0,
+            "14": 60922318848.0,
+            "15": 60922056704.0,
+            "16": 60922310656.0,
+            "17": 60922052608.0,
+            "18": 60922052608.0,
+            "19": 60922048512.0,
+            "20": 60922044416.0
         }
     },
     "mem-max-allocated-bytes": {
@@ -31,26 +31,26 @@
         "end_step": 20,
         "step_interval": 1,
         "values": {
-            "1": 60978675712.0,
-            "2": 64214241280.0,
-            "3": 64214241280.0,
-            "4": 64214241280.0,
-            "5": 64214241280.0,
-            "6": 64214241280.0,
-            "7": 64214241280.0,
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-            "12": 64214241280.0,
-            "13": 64214241280.0,
-            "14": 64214241280.0,
-            "15": 64214241280.0,
-            "16": 64214241280.0,
-            "17": 64214241280.0,
-            "18": 64214241280.0,
-            "19": 64214241280.0,
-            "20": 64214241280.0
+            "1": 60922073088.0,
+            "2": 64156037120.0,
+            "3": 64156037120.0,
+            "4": 64156041216.0,
+            "5": 64156041216.0,
+            "6": 64156041216.0,
+            "7": 64156041216.0,
+            "8": 64156041216.0,
+            "9": 64156041216.0,
+            "10": 64156041216.0,
+            "11": 64156041216.0,
+            "12": 64156041216.0,
+            "13": 64156041216.0,
+            "14": 64156041216.0,
+            "15": 64156041216.0,
+            "16": 64156041216.0,
+            "17": 64156041216.0,
+            "18": 64156041216.0,
+            "19": 64156041216.0,
+            "20": 64156041216.0
         }
     },
     "iteration-time": {
@@ -59,25 +59,25 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
-            "2": 37.77975,
-            "3": 15.85042,
-            "4": 14.84801,
-            "5": 14.16031,
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-            "17": 13.58351,
-            "18": 14.3947,
-            "19": 13.78201,
-            "20": 13.44734
+            "2": 60.37194,
+            "3": 13.25967,
+            "4": 13.01461,
+            "5": 14.04256,
+            "6": 13.53259,
+            "7": 13.3335,
+            "8": 12.72344,
+            "9": 13.64787,
+            "10": 12.66485,
+            "11": 13.15779,
+            "12": 13.01275,
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+            "17": 12.86279,
+            "18": 12.71745,
+            "19": 13.4137,
+            "20": 12.75566
         }
     }
-}
\ No newline at end of file
+}
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/model_config.yaml
index 7f6fe4756e3..80664dcdc59 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput/model_config.yaml
@@ -1,5 +1,6 @@
 ENV_VARS:
   CUDA_DEVICE_MAX_CONNECTIONS: 1
+  THROUGHPUT_START_STEP: 10
   NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
@@ -39,8 +40,6 @@ MODEL_ARGS:
   --attention-softmax-in-fp32: true
   --tokenizer-type: HuggingFaceTokenizer
   --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
-  --langrl-inference-server-type: inplace_megatron_chat
-  --langrl-inference-server-conversation-template: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
   --vocab-size: 151936
   --make-vocab-size-divisible-by: 128
   --optimizer: adam
@@ -75,7 +74,7 @@ MODEL_ARGS:
   --timing-log-level: 1
   --cuda-graph-impl: local
   --micro-batch-size: 1
-  --global-batch-size: 2
+  --global-batch-size: 4
   --grpo-group-size: 2
   --grpo-prompts-per-step: 2
   --grpo-iterations: 1
@@ -99,5 +98,3 @@ METRICS:
   - "mem-allocated-bytes"
   - "mem-max-allocated-bytes"
   - "iteration-time"
-THROUGHPUT_TEST_PARAMS:
-  --start_step: 10
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/golden_values_dev_dgx_h100.json
index e0bcb14f29b..d6397eb061c 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/golden_values_dev_dgx_h100.json
@@ -4,26 +4,26 @@
         "end_step": 20,
         "step_interval": 1,
         "values": {
-            "1": 60922077184.0,
-            "2": 60922073088.0,
-            "3": 60922068992.0,
-            "4": 60922073088.0,
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-            "17": 60922089472.0,
-            "18": 60922085376.0,
-            "19": 60922085376.0,
-            "20": 60922089472.0
+            "1": 60921806848.0,
+            "2": 60921806848.0,
+            "3": 60921794560.0,
+            "4": 60921790464.0,
+            "5": 60921782272.0,
+            "6": 60921774080.0,
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+            "11": 60921331712.0,
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+            "18": 60921344000.0,
+            "19": 60921344000.0,
+            "20": 60921344000.0
         }
     },
     "mem-max-allocated-bytes": {
@@ -31,26 +31,26 @@
         "end_step": 20,
         "step_interval": 1,
         "values": {
-            "1": 60922081280.0,
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+            "6": 64155770880.0,
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+            "19": 64155770880.0,
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         }
     },
     "iteration-time": {
@@ -59,25 +59,25 @@
         "step_interval": 1,
         "values": {
             "1": "nan",
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+            "2": 85.15953,
+            "3": 36.37596,
+            "4": 34.46927,
+            "5": 35.70767,
+            "6": 36.40207,
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+            "18": 35.80016,
+            "19": 37.51863,
+            "20": 36.1209
         }
     }
 }
diff --git a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/model_config.yaml
index 456280fdb04..cc25f3ab90e 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_throughput_github/model_config.yaml
@@ -1,5 +1,6 @@
 ENV_VARS:
   CUDA_DEVICE_MAX_CONNECTIONS: 1
+  THROUGHPUT_START_STEP: 10
   NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
@@ -39,8 +40,6 @@ MODEL_ARGS:
   --attention-softmax-in-fp32: true
   --tokenizer-type: HuggingFaceTokenizer
   --tokenizer-model: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
-  --langrl-inference-server-type: inplace_megatron_chat
-  --langrl-inference-server-conversation-template: ${CHECKPOINT_LOAD_PATH}/model/qwen3-8b-dist/tokenizer
   --vocab-size: 151936
   --make-vocab-size-divisible-by: 128
   --optimizer: adam
@@ -99,5 +98,3 @@ METRICS:
   - "mem-allocated-bytes"
   - "mem-max-allocated-bytes"
   - "iteration-time"
-THROUGHPUT_TEST_PARAMS:
-  --start_step: 10
diff --git a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch/model_config.yaml
index 6d63b0e4228..40b45024cb1 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch/model_config.yaml
@@ -76,6 +76,7 @@ MODEL_ARGS:
   --max-tokens-to-oom: 3600000
   --inference-max-seq-length: 4096
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompt-file: ./tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch/test_prompts.jsonl
   --incoming-requests-per-sec: -1 # all requests arrive up front.
   --inference-logging-step-interval: 1
diff --git a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_cudagraphs/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_cudagraphs/model_config.yaml
index 8f54fff0a2f..9a47281703a 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_cudagraphs/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_cudagraphs/model_config.yaml
@@ -48,6 +48,7 @@ MODEL_ARGS:
   --inference-max-requests: 1
   --dist-ckpt-strictness: log_unexpected
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
   --inference-logging-step-interval: 1
diff --git a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs/model_config.yaml
index 1a1195baa2b..99bcc433ad1 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs/model_config.yaml
@@ -53,6 +53,7 @@ MODEL_ARGS:
   --inference-max-requests: 1
   --dist-ckpt-strictness: log_unexpected
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
   --inference-logging-step-interval: 1
diff --git a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_logitsmatch/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_logitsmatch/model_config.yaml
index be00e4b3ce7..1c78b466b1e 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_logitsmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/gpt/gpt_static_inference_tp1_pp1_583m_logitsmatch/model_config.yaml
@@ -44,6 +44,7 @@ MODEL_ARGS:
   --flash-decode: true
   --dist-ckpt-strictness: log_unexpected
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
 METRICS:
diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
index 123e2e98a36..22d0cbaa3bf 100644
--- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
+++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp1_pp1_cp1_dgx_a100_1N8G/model_config.yaml
@@ -57,5 +57,4 @@ MODEL_ARGS:
   --bf16: true
   --attention-backend: unfused
   --log-memory-to-tensorboard: true
-  --legacy-tokenizer: true
 TEST_TYPE: regular
diff --git a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8/model_config.yaml b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8/model_config.yaml
index 2daf74b89a7..e95856e7308 100644
--- a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8/model_config.yaml
+++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8/model_config.yaml
@@ -25,7 +25,6 @@ MODEL_ARGS:
   --load: ${CHECKPOINT_LOAD_PATH}
   --tokenizer-type: HuggingFaceTokenizer
   --tokenizer-model: llava-hf/llava-1.5-7b-hf
-  --legacy-tokenizer: true
   --distributed-backend: nccl
   --lr: 0.001
   --lr-decay-style: cosine
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml
index ced409e5b1e..a77d456506c 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release/model_config.yaml
@@ -134,8 +134,8 @@ MODEL_ARGS:
     # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
   --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 500
-  --save-retain-interval: 10000
+  --save-interval: 250
+  --save-retain-interval: 5000
   --dist-ckpt-strictness: log_all
 
   # Add initialization args
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..5e4de4d06ee
--- /dev/null
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,11492 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 9535,
+        "step_interval": 5,
+        "values": {
+            "1": 13.90637,
+            "5": 13.89465,
+            "10": 13.87132,
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+            "20": 13.75898,
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+            "30": 13.42091,
+            "35": 13.34203,
+            "40": 13.25256,
+            "45": 13.16108,
+            "50": 12.57591,
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+            "100": 10.58355,
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+            "110": 10.25095,
+            "115": 10.04008,
+            "120": 9.91015,
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml
index a6c65afb712..beb3633b510 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_gb_200_release_sm/model_config.yaml
@@ -134,8 +134,8 @@ MODEL_ARGS:
     # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
   --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 500
-  --save-retain-interval: 10000
+  --save-interval: 250
+  --save-retain-interval: 5000
   --dist-ckpt-strictness: log_all
 
   # Add initialization args
@@ -161,9 +161,7 @@ MODEL_ARGS:
 
   # enable experimental
   --enable-experimental: true
-
   --exit-interval: 9536
-
 METRICS:
   - "iteration-time"
   - "lm loss"
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
index 7bc14780fb3..ab618ab915c 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml
@@ -132,8 +132,8 @@ MODEL_ARGS:
     # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
   --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 500
-  --save-retain-interval: 10000
+  --save-interval: 250
+  --save-retain-interval: 5000
   --dist-ckpt-strictness: log_all
 
   # Add initialization args
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
index f486950e5a2..f3a45c077a1 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/golden_values_dev_dgx_h100.json
@@ -4,1914 +4,1914 @@
         "end_step": 9535,
         "step_interval": 5,
         "values": {
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-            "5": 13.89155,
-            "10": 13.85814,
-            "15": 13.84947,
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-            "125": 9.74001,
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-            "140": 9.34895,
-            "145": 9.3363,
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 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
index cc8f2b814c2..ab1df7f1d1e 100644
--- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
+++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml
@@ -132,8 +132,8 @@ MODEL_ARGS:
     # Add checkpointing args
   --save: ${CHECKPOINT_SAVE_PATH}
   --load: ${CHECKPOINT_LOAD_PATH}
-  --save-interval: 500
-  --save-retain-interval: 10000
+  --save-interval: 250
+  --save-retain-interval: 5000
   --dist-ckpt-strictness: log_all
 
   # Add initialization args
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..5c353a70683
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,287 @@
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..51442760296
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic_dp_last/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,287 @@
+{
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+        "step_interval": 1,
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..b804ba57a90
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..3dd007cc9ec
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json
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new file mode 100644
index 00000000000..31167be6de5
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..f62929eef31
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4/golden_values_dev_dgx_gb200.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..113a491b0ba
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..bfea64b8438
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..2bcdb30bc50
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..9a3140994d3
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..883b2c99518
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..3e910ef7869
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_gb200.json
new file mode 100644
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..35841a4eaa1
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..fe8d3f78926
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..64e256f8b57
--- /dev/null
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..dc46db36c72
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200.json
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diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..8710366a4a2
--- /dev/null
+++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,644 @@
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+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
index b239ac96c3d..ab970730d9b 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/golden_values_dev_dgx_h100.json
@@ -34,7 +34,7 @@
             1394,
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+        "latency": 1.972374301403761,
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+                [
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+                [
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+                [
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+                [
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+                    1,
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+                [
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+                    46,
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+                    50
+                ]
+            ]
         ]
     }
 }
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
index edc5fc2eb32..5ab6d37f17e 100644
--- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq/model_config.yaml
@@ -84,7 +84,9 @@ MODEL_ARGS:
   --inference-dynamic-batching-max-requests: 512
   --inference-logging-step-interval: 1
   --sequence-parallel: true
+  --moe-enable-routing-replay: true
 
 METRICS:
   - "generated_tokens"
   - "logprobs"
+  - "routing_indices"
diff --git a/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/model_config.yaml
index ed5d123892e..139c5a82e57 100644
--- a/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/model_config.yaml
@@ -1,5 +1,6 @@
 ENV_VARS:
   CUDA_DEVICE_MAX_CONNECTIONS: 1
+  THROUGHPUT_START_STEP: 1
   NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0
   NCCL_ALGO: Ring
   CUBLAS_WORKSPACE_CONFIG: :4096:8
@@ -89,7 +90,6 @@ MODEL_ARGS:
   --mock-data: true
   --max-tokens-to-oom: 3600000
   --inference-max-seq-length: 256
-  --langrl-inference-server-type: inplace_megatron
   --calculate-per-token-loss: true
   --rl-use-sequence-packing: true
   --rl-sequence-packing-algo: fifo
@@ -137,5 +137,3 @@ METRICS:
   - "mem-allocated-bytes"
   - "mem-max-allocated-bytes"
 
-THROUGHPUT_TEST_PARAMS:
-  --start_step: 1
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
index 549821afc8b..1c22a729f6e 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
@@ -79,6 +79,7 @@ MODEL_ARGS:
   --inference-max-seq-length: 4096
   --inference-max-requests: 1
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1
 METRICS:
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/model_config.yaml
index 4934fe6c913..03895d97ee9 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/model_config.yaml
@@ -72,6 +72,7 @@ MODEL_ARGS:
   --max-tokens-to-oom: 3600000
   --inference-max-seq-length: 4096
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
   --inference-dynamic-batching-buffer-size-gb: 20
diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
index 69c0db980b0..9259d63c9d1 100644
--- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
+++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/model_config.yaml
@@ -74,6 +74,7 @@ MODEL_ARGS:
   --max-tokens-to-oom: 3600000
   --inference-max-seq-length: 4096
   --output-path: ${INFERENCE_OUTPUT_PATH}
+  --use-legacy-static-engine: true
   --prompts: "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies."
   --incoming-requests-per-sec: -1 # all requests arrive up front.
 METRICS:
diff --git a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/model_config.yaml b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/model_config.yaml
index 6be0edcd91b..bccce17cef1 100644
--- a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/model_config.yaml
+++ b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/model_config.yaml
@@ -51,7 +51,6 @@ MODEL_ARGS:
   --deterministic-mode: true
   --ckpt-format: torch_dist
   --log-memory-to-tensorboard: true
-  --legacy-tokenizer: true
 TEST_TYPE: regular
 METRICS:
   - "lm loss"
diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json
new file mode 100644
index 00000000000..f6470962a57
--- /dev/null
+++ b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_gb200.json
@@ -0,0 +1,10042 @@
+{
+    "lm loss": {
+        "start_step": 1,
+        "end_step": 10000,
+        "step_interval": 5,
+        "values": {
+            "1": 10.35422,
+            "5": 10.35245,
+            "10": 10.2685,
+            "15": 9.88229,
+            "20": 9.69037,
+            "25": 9.56791,
+            "30": 9.46222,
+            "35": 9.3914,
+            "40": 9.30692,
+            "45": 9.22623,
+            "50": 9.17628,
+            "55": 9.10985,
+            "60": 9.02747,
+            "65": 8.96632,
+            "70": 8.97188,
+            "75": 8.89048,
+            "80": 8.90106,
+            "85": 8.82105,
+            "90": 8.80103,
+            "95": 8.76113,
+            "100": 8.71072,
+            "105": 8.65308,
+            "110": 8.59599,
+            "115": 8.574,
+            "120": 8.55934,
+            "125": 8.50551,
+            "130": 8.49512,
+            "135": 8.40399,
+            "140": 8.39479,
+            "145": 8.33222,
+            "150": 8.29186,
+            "155": 8.20142,
+            "160": 8.18848,
+            "165": 8.128,
+            "170": 8.10751,
+            "175": 8.07862,
+            "180": 7.94901,
+            "185": 7.98432,
+            "190": 7.89822,
+            "195": 7.84101,
+            "200": 7.79696,
+            "205": 7.74434,
+            "210": 7.68052,
+            "215": 7.65626,
+            "220": 7.59702,
+            "225": 7.51914,
+            "230": 7.45301,
+            "235": 7.437,
+            "240": 7.31011,
+            "245": 7.25757,
+            "250": 7.17667,
+            "255": 7.13144,
+            "260": 7.02879,
+            "265": 6.98496,
+            "270": 6.95865,
+            "275": 6.88942,
+            "280": 6.83967,
+            "285": 6.80686,
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+            "300": 6.67358,
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diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json
new file mode 100644
index 00000000000..91a0cb5d77a
--- /dev/null
+++ b/tests/functional_tests/test_cases/t5/t5_release_sm/golden_values_dev_dgx_h100.json
@@ -0,0 +1,10042 @@
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+            "10000": 0.22092
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml b/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml
new file mode 100644
index 00000000000..7f748273cd3
--- /dev/null
+++ b/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml
@@ -0,0 +1,68 @@
+ENV_VARS:
+  CUDA_DEVICE_MAX_CONNECTIONS: "1"
+  NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1"
+  NON_DETERMINSTIC_RESULTS: 1
+TEST_TYPE: "release"
+MODEL_ARGS:
+  # T5 model args
+  --encoder-num-layers: 12
+  --decoder-num-layers: 12
+  --hidden-size: 768
+  --num-attention-heads: 12
+  --kv-channels: 64
+  --ffn-hidden-size: 3072
+  --encoder-seq-length: 512
+  --decoder-seq-length: 128
+  --max-position-embeddings: 512
+  --init-method-std: 0.015
+  --attention-backend: unfused
+  # Training args
+  --micro-batch-size: 32
+  --global-batch-size: 512
+  --train-iters: 100000
+  --exit-interval: 10000
+  --weight-decay: 1e-2
+  --clip-grad: 1.0
+  --bf16: true
+  --lr: 0.0001
+  --lr-decay-style: linear
+  --min-lr: 1.0e-5
+  --lr-warmup-fraction: .01
+  --distributed-backend: nccl
+  # Transformer Engine args
+  --use-mcore-models: true
+  --transformer-impl: transformer_engine
+  # Model parallel
+  --tensor-model-parallel-size: 4
+  --pipeline-model-parallel-size: 1
+  # Data args
+  --data-path: ${DATA_BLEND}
+  --vocab-file: ${DATA_PATH}/text/the_pile/t5_shard00/bert-large-cased-vocab.txt
+  --tokenizer-type: BertWordPieceCase
+  --split: 99982,9,9
+  --data-cache-path: ${DATA_CACHE_PATH}
+  --vocab-extra-ids: 100
+  # EVAL_AND_LOGGING_ARGS
+  --log-interval: 100
+  --save-interval: 2000
+  --eval-interval: 1000
+  --save-retain-interval: 10000
+  --save: ${CHECKPOINT_SAVE_PATH}
+  --load: ${CHECKPOINT_LOAD_PATH}
+  --eval-iters: 10
+  --tensorboard-dir: ${TENSORBOARD_PATH}
+  --log-timers-to-tensorboard: true
+  --log-memory-to-tensorboard: true
+  --log-num-zeros-in-grad: true
+  --log-params-norm: true
+  --log-validation-ppl-to-tensorboard: true
+  --timing-log-level: 0
+  --wandb-project: megatron-core-release-runs
+  --wandb-entity: adlr
+  --wandb-exp-name: ${WANDB_EXPERIMENT}
+  --wandb-save-dir: ${WANDB_SAVE_PATH}
+METRICS:
+  - "iteration-time"
+  - "lm loss"
+  - "mem-allocated-bytes"
+  - "mem-max-allocated-bytes"
diff --git a/tests/test_utils/python_scripts/auto_reminder_github.py b/tests/test_utils/python_scripts/auto_reminder_github.py
index 94e0de1ddaa..eca1108dcc6 100644
--- a/tests/test_utils/python_scripts/auto_reminder_github.py
+++ b/tests/test_utils/python_scripts/auto_reminder_github.py
@@ -260,7 +260,7 @@ def generate_reminders(self):
             query = (
                 f'repo:"{self.repo.full_name}" '
                 f'milestone:"{milestone.title}" '
-                f'is:open is:pr '
+                f'is:open is:pr -is:draft '
                 f'label:"{self.EXPERT_REVIEW}","{self.FINAL_REVIEW}"'
             )
             try:
diff --git a/tests/test_utils/python_scripts/download_golden_values.py b/tests/test_utils/python_scripts/download_golden_values.py
index 158df867a64..5d3007cb6c5 100644
--- a/tests/test_utils/python_scripts/download_golden_values.py
+++ b/tests/test_utils/python_scripts/download_golden_values.py
@@ -1,33 +1,28 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
+import io
 import logging
 import os
 import pathlib
+import re
 import shutil
 import zipfile
 
 import click
 import gitlab
+import requests
 
 BASE_PATH = pathlib.Path(__file__).parent.resolve()
 PROJECT_ID = int(os.getenv("CI_PROJECT_ID", 19378))
+GITHUB_REPO = os.getenv("GITHUB_REPOSITORY", "NVIDIA/Megatron-LM")
 
 logger = logging.getLogger(__name__)
 
 
-@click.command()
-@click.option("--pipeline-id", required=True, type=int, help="Pipeline ID")
-@click.option(
-    "--only-failing/--no-only-failing",
-    default=False,
-    help="Only download artifacts from failing jobs",
-)
-def main(pipeline_id: int, only_failing: bool):
-    logging.basicConfig(level=logging.INFO)
-    logger.info('Started')
-
-    gitlab_endpoint = os.getenv('GITLAB_ENDPOINT')
-    ro_api_token = os.getenv('RO_API_TOKEN')
+def download_from_gitlab(pipeline_id: int, only_failing: bool):
+    """Download golden values from Gitlab pipeline."""
+    gitlab_endpoint = os.getenv("GITLAB_ENDPOINT")
+    ro_api_token = os.getenv("RO_API_TOKEN")
 
     if not gitlab_endpoint or not ro_api_token:
         raise Exception(
@@ -50,7 +45,7 @@ def main(pipeline_id: int, only_failing: bool):
 
     ASSETS_DIR = pathlib.Path("tmp") / "results" / "iteration=0"
     for pipeline_bridge in pipeline_bridges:
-        functional_pipeline = project.pipelines.get(pipeline_bridge.downstream_pipeline['id'])
+        functional_pipeline = project.pipelines.get(pipeline_bridge.downstream_pipeline["id"])
         environment = pipeline_bridge.name[len("functional:run_") :]
         functional_pipeline_jobs = functional_pipeline.jobs.list(get_all=True)
         logger.info("Starting with pipeline %s", pipeline_bridge.name)
@@ -62,7 +57,7 @@ def main(pipeline_id: int, only_failing: bool):
                 continue
 
             try:
-                file_name = '__artifacts.zip'
+                file_name = "__artifacts.zip"
                 with open(file_name, "wb") as f:
                     job.artifacts(streamed=True, action=f.write)
                 zip = zipfile.ZipFile(file_name)
@@ -99,7 +94,7 @@ def main(pipeline_id: int, only_failing: bool):
                 golden_values_target = (
                     pathlib.Path("tests")
                     / "functional_tests"
-                    / 'test_cases'
+                    / "test_cases"
                     / job.stage
                     / job.name
                     / golden_values_source_name
@@ -122,5 +117,385 @@ def main(pipeline_id: int, only_failing: bool):
     logger.info("beep boop: All done!")
 
 
+def _setup_github_headers(github_token: str) -> dict:
+    """Set up headers for GitHub API requests."""
+    return {
+        "Authorization": f"token {github_token}",
+        "Accept": "application/vnd.github+json",
+        "X-GitHub-Api-Version": "2022-11-28",
+    }
+
+
+def _fetch_workflow_run(workflow_id: int, headers: dict, api_base: str) -> dict:
+    """Fetch workflow run details from GitHub."""
+    logger.info("Fetching workflow run %d from %s", workflow_id, GITHUB_REPO)
+    workflow_run_url = f"{api_base}/repos/{GITHUB_REPO}/actions/runs/{workflow_id}"
+    response = requests.get(workflow_run_url, headers=headers)
+    response.raise_for_status()
+    workflow_run = response.json()
+
+    logger.info(
+        "Workflow run status: %s, conclusion: %s",
+        workflow_run["status"],
+        workflow_run["conclusion"],
+    )
+    return workflow_run
+
+
+def _fetch_all_jobs(workflow_id: int, headers: dict, api_base: str) -> list:
+    """Fetch all jobs from the workflow run with pagination."""
+    logger.info("Fetching all jobs from workflow run...")
+    all_jobs = []
+    jobs_url = f"{api_base}/repos/{GITHUB_REPO}/actions/runs/{workflow_id}/jobs"
+    page = 1
+    per_page = 100
+
+    while True:
+        response = requests.get(
+            jobs_url, headers=headers, params={"page": page, "per_page": per_page}
+        )
+        response.raise_for_status()
+        jobs_data = response.json()
+        jobs = jobs_data["jobs"]
+
+        if not jobs:
+            break
+
+        all_jobs.extend(jobs)
+        logger.info("Fetched page %d with %d jobs", page, len(jobs))
+
+        if len(jobs) < per_page:
+            break
+
+        page += 1
+
+    logger.info("Total jobs fetched: %d", len(all_jobs))
+    return all_jobs
+
+
+def _filter_cicd_jobs(all_jobs: list) -> list:
+    """Filter jobs that belong to cicd-integration-tests-latest."""
+    # Try to filter by workflow_name field
+    cicd_jobs = [
+        job
+        for job in all_jobs
+        if "workflow_name" in job
+        and "cicd-integration-tests-latest" in job.get("workflow_name", "").lower()
+    ]
+
+    # If workflow_name field doesn't exist, try filtering by job labels
+    if not cicd_jobs:
+        logger.info("No jobs found with workflow_name field. Checking job labels...")
+        cicd_jobs = [
+            job
+            for job in all_jobs
+            if "labels" in job
+            and any(
+                "cicd-integration-tests-latest" in str(label).lower() for label in job["labels"]
+            )
+        ]
+
+    # If still no jobs found, use all jobs as fallback
+    if not cicd_jobs:
+        logger.warning(
+            "Could not identify jobs belonging to cicd-integration-tests-latest. "
+            "Using all jobs from workflow run as fallback."
+        )
+        cicd_jobs = all_jobs
+    else:
+        logger.info("Filtered %d jobs belonging to cicd-integration-tests-latest", len(cicd_jobs))
+
+    return cicd_jobs
+
+
+def _create_job_name_map(cicd_jobs: list) -> dict:
+    """Create a mapping of normalized job names to job data for matching with artifacts."""
+    job_name_map = {}
+    for job in cicd_jobs:
+        job_name = job["name"]
+
+        # Extract test name from job name (between '/' and ' - ')
+        if "/" in job_name and " - " in job_name:
+            # Format: "stage/test_name - version"
+            test_name = job_name.split("/", 1)[1].split(" - ", 1)[0]
+        elif "/" in job_name:
+            # Format: "stage/test_name"
+            test_name = job_name.split("/", 1)[1]
+        else:
+            # No stage prefix, use the whole name
+            test_name = job_name.split(" - ", 1)[0] if " - " in job_name else job_name
+
+        # Normalize for matching (lowercase, preserve underscores)
+        normalized_name = test_name.lower()
+        job_name_map[normalized_name] = job
+        logger.info("Job '%s' normalized to '%s'", job["name"], normalized_name)
+
+    return job_name_map
+
+
+def _match_artifact_to_job(artifact: dict, workflow_id: int, job_name_map: dict) -> dict:
+    """Match an artifact to a job from the job name map."""
+    artifact_job_name = artifact["name"].lower().replace("logs-", "").split(f"-{workflow_id}")[0]
+    for normalized_job_name, job in job_name_map.items():
+        if normalized_job_name == artifact_job_name:
+            logger.info("Artifact '%s' matched to job '%s'", artifact["name"], job["name"])
+            return job
+
+    logger.info(
+        "Artifact '%s' does not match any cicd-integration-tests-latest jobs", artifact["name"]
+    )
+    return None
+
+
+def _fetch_and_filter_artifacts(
+    workflow_id: int, job_name_map: dict, only_failing: bool, headers: dict, api_base: str
+) -> list:
+    """Fetch and filter artifacts from the workflow run."""
+    logger.info("Fetching and filtering artifacts from workflow run...")
+    filtered_artifacts = []
+    artifacts_url = f"{api_base}/repos/{GITHUB_REPO}/actions/runs/{workflow_id}/artifacts"
+    page = 1
+    per_page = 100
+
+    while True:
+        response = requests.get(
+            artifacts_url, headers=headers, params={"page": page, "per_page": per_page}
+        )
+        response.raise_for_status()
+        artifacts_data = response.json()
+        artifacts = artifacts_data["artifacts"]
+
+        if not artifacts:
+            break
+
+        logger.info("Fetched artifacts page %d with %d artifacts", page, len(artifacts))
+
+        # Filter artifacts that match cicd jobs
+        for artifact in artifacts:
+            logger.info("Checking artifact: %s", artifact["name"])
+
+            matched_job = _match_artifact_to_job(artifact, workflow_id, job_name_map)
+
+            if matched_job:
+                # Check if we should skip based on only_failing flag
+                if only_failing and matched_job.get("conclusion") == "success":
+                    logger.info(
+                        "Job '%s' succeeded and only-failing is set. Skipping artifact '%s'",
+                        matched_job["name"],
+                        artifact["name"],
+                    )
+                    continue
+
+                artifact["_matched_job"] = matched_job
+                filtered_artifacts.append(artifact)
+
+        if len(artifacts) < per_page:
+            break
+
+        page += 1
+
+    logger.info(
+        "Filtered %d artifacts matching cicd-integration-tests-latest jobs", len(filtered_artifacts)
+    )
+    return filtered_artifacts
+
+
+def _extract_stage_and_test_name(job_name: str) -> tuple:
+    """Extract stage and test name from job name."""
+    if "/" in job_name:
+        # Format: "stage/test_name - version" or "stage/test_name"
+        stage, rest = job_name.split("/", 1)
+        # Remove version suffix if present (e.g., " - latest")
+        test_name = rest.split(" - ", 1)[0] if " - " in rest else rest
+    else:
+        # No stage in job name - this shouldn't happen for integration tests
+        raise ValueError(
+            f"Job name '{job_name}' does not contain a stage (expected format: 'stage/test_name - version')"
+        )
+
+    logger.info(f"Extracted stage: {stage}, test name: {test_name} from job '{job_name}'")
+    return stage, test_name
+
+
+def _download_and_extract_artifact(
+    artifact: dict, headers: dict, api_base: str, file_name: str = "__artifacts.zip"
+) -> list:
+    """Download and extract an artifact, returning list of golden values files."""
+    artifact_download_url = f"{api_base}/repos/{GITHUB_REPO}/actions/artifacts/{artifact['id']}/zip"
+
+    logger.info("Downloading artifact from %s", artifact_download_url)
+    response = requests.get(artifact_download_url, headers=headers, stream=True)
+    response.raise_for_status()
+
+    with open(file_name, "wb") as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+
+    # Extract the artifact
+    zip_file = zipfile.ZipFile(file_name)
+    zip_file.extractall("tmp")
+    logger.info("Downloaded and extracted artifact %s", artifact["name"])
+
+    os.unlink(file_name)
+
+    # For GitHub, golden values are always in the top level of tmp
+    tmp_dir = pathlib.Path("tmp")
+    golden_values_in_tmp = list(tmp_dir.glob("golden_values*.json"))
+
+    if golden_values_in_tmp:
+        logger.info(
+            "Found %d golden values files in artifact %s",
+            len(golden_values_in_tmp),
+            artifact["name"],
+        )
+    else:
+        logger.info("No golden values found in artifact %s. Skipping.", artifact["name"])
+
+    return golden_values_in_tmp
+
+
+def _move_golden_values(golden_values_files: list, stage: str, test_name: str) -> int:
+    """Move golden values files to their target location."""
+    golden_values_moved = 0
+
+    for golden_values_file in golden_values_files:
+        golden_values_target = (
+            pathlib.Path("tests")
+            / "functional_tests"
+            / "test_cases"
+            / stage
+            / test_name
+            / golden_values_file.name
+        )
+
+        pathlib.Path(golden_values_target.parent).mkdir(parents=True, exist_ok=True)
+        logger.info(f"Moving golden values from {golden_values_file} to {golden_values_target}")
+        shutil.move(golden_values_file, golden_values_target)
+        golden_values_moved += 1
+
+    return golden_values_moved
+
+
+def _cleanup_tmp():
+    """Clean up temporary directory."""
+    if pathlib.Path("tmp").exists():
+        shutil.rmtree("tmp")
+
+
+def _process_artifact(artifact: dict, headers: dict, api_base: str) -> int:
+    """Process a single artifact (download, extract, and move golden values)."""
+    matched_job = artifact.get("_matched_job")
+    logger.info(
+        "Processing artifact: %s (matched to job: %s)",
+        artifact["name"],
+        matched_job["name"] if matched_job else "unknown",
+    )
+
+    file_name = "__artifacts.zip"
+
+    try:
+        # Download and extract the artifact
+        golden_values_files = _download_and_extract_artifact(artifact, headers, api_base, file_name)
+
+        if not golden_values_files:
+            _cleanup_tmp()
+            return 0
+
+        # Extract stage and test name from the matched job name
+        stage, test_name = _extract_stage_and_test_name(matched_job["name"])
+
+        # Move golden values to target location
+        golden_values_moved = _move_golden_values(golden_values_files, stage, test_name)
+
+        logger.info(
+            f"Successfully moved {golden_values_moved} golden values for test: {stage}/{test_name}"
+        )
+
+        # Clean up tmp directory after processing this artifact
+        _cleanup_tmp()
+
+        return golden_values_moved
+
+    except Exception as e:
+        logger.error("Failed to download/process artifact %s due to %s", artifact["name"], e)
+        # Clean up on error
+        if os.path.exists(file_name):
+            os.unlink(file_name)
+        _cleanup_tmp()
+        return 0
+
+
+def download_from_github(workflow_id: int, only_failing: bool):
+    """Download golden values from Github workflow run."""
+    github_token = os.getenv("GITHUB_TOKEN")
+
+    if not github_token:
+        raise Exception(
+            "Environment variable {GITHUB_TOKEN} has not been set. ie. GITHUB_TOKEN=<github-token>"
+        )
+
+    api_base = "https://api.github.com"
+    headers = _setup_github_headers(github_token)
+
+    # Fetch workflow run details
+    _fetch_workflow_run(workflow_id, headers, api_base)
+    logger.info("Setting only_failing to %s", only_failing)
+
+    # Fetch and filter jobs
+    all_jobs = _fetch_all_jobs(workflow_id, headers, api_base)
+    cicd_jobs = _filter_cicd_jobs(all_jobs)
+    job_name_map = _create_job_name_map(cicd_jobs)
+
+    # Fetch and filter artifacts
+    artifacts = _fetch_and_filter_artifacts(
+        workflow_id, job_name_map, only_failing, headers, api_base
+    )
+
+    # Process each artifact
+    total_golden_values = 0
+    total_tests_with_golden_values = 0
+
+    for artifact in artifacts:
+        golden_values_moved = _process_artifact(artifact, headers, api_base)
+
+        if golden_values_moved > 0:
+            total_golden_values += golden_values_moved
+            total_tests_with_golden_values += 1
+
+    logger.info(f"Total tests with golden values: {total_tests_with_golden_values}")
+    logger.info(f"Total golden values found: {total_golden_values}")
+    logger.info("beep boop: All done!")
+
+
+@click.command()
+@click.option(
+    "--source",
+    type=click.Choice(["gitlab", "github"], case_sensitive=False),
+    required=True,
+    help="Source platform (gitlab or github)",
+)
+@click.option(
+    "--pipeline-id", type=int, help="Gitlab Pipeline ID or Github Workflow Run ID", required=True
+)
+@click.option(
+    "--only-failing/--no-only-failing",
+    default=False,
+    help="Only download artifacts from failing jobs",
+)
+def main(source: str, pipeline_id: int, only_failing: bool):
+    """Download golden values from Gitlab or Github."""
+    logging.basicConfig(level=logging.INFO)
+    logger.info("Started")
+
+    source = source.lower()
+
+    if source == "gitlab":
+        download_from_gitlab(pipeline_id, only_failing)
+    elif source == "github":
+        download_from_github(pipeline_id, only_failing)
+    else:
+        raise click.UsageError(f"Unknown source: {source}")
+
+
 if __name__ == "__main__":
     main()
diff --git a/tests/test_utils/python_scripts/launch_nemo_run_workload.py b/tests/test_utils/python_scripts/launch_nemo_run_workload.py
index 8d006f70d19..bf4bb37aa20 100644
--- a/tests/test_utils/python_scripts/launch_nemo_run_workload.py
+++ b/tests/test_utils/python_scripts/launch_nemo_run_workload.py
@@ -140,10 +140,10 @@ def main(
         succeeded = str(job_dict["status"]) == "SUCCEEDED"
 
         if succeeded:
-            logger.info(f"Job succeeded with status: {job_dict["status"]}")
+            logger.info(f"Job succeeded with status: {job_dict['status']}")
             sys.exit(0)
 
-        logger.error(f"Job failed with status: {job_dict["status"]}")
+        logger.error(f"Job failed with status: {job_dict['status']}")
         log_file_paths = pathlib.Path(os.getcwd()).glob("assets_dir/logs/*/*/attempt_0/*/std*.log")
         all_ranks_all_logs = []
         for log_file_path in log_file_paths:
diff --git a/tests/test_utils/python_scripts/recipe_parser.py b/tests/test_utils/python_scripts/recipe_parser.py
index d21551c6c46..a04340407e3 100644
--- a/tests/test_utils/python_scripts/recipe_parser.py
+++ b/tests/test_utils/python_scripts/recipe_parser.py
@@ -39,34 +39,25 @@ def resolve_cluster_config(cluster: str) -> str:
 
 def flatten_products(workload_manifest: dotdict) -> dotdict:
     """Flattens a nested dict of products"""
-    expanded_products = []
+    flattened_products = []
+    products = workload_manifest.products or []
 
-    for product in workload_manifest.products or []:
-        # Skip products that don't have nested product specifications
+    for product in products:
         if "products" not in product:
             continue
 
         test_case = product["test_case"][0]
+        for param_dict in product["products"]:
+            # Generate all combinations of parameter values
+            param_combinations = itertools.product(*param_dict.values())
+
+            for value_combination in param_combinations:
+                # Map parameter names to their values
+                flattened = dict(zip(param_dict.keys(), value_combination))
+                flattened["test_case"] = test_case
+                flattened_products.append(flattened)
 
-        # Iterate over each input specification in the product
-        for inp in product["products"]:
-            # Generate all combinations of the input values (Cartesian product)
-            model_config = inp.pop("model_config", None)
-            runtime_config = inp.pop("runtime_config", None)
-            keys = inp.keys()
-            value_combinations = itertools.product(*inp.values())
-
-            # Create a flattened product dict for each combination
-            for values in value_combinations:
-                product_dict = dict(zip(keys, values))
-                product_dict["test_case"] = test_case
-                if model_config:
-                    product_dict["model_config"] = model_config
-                if runtime_config:
-                    product_dict["runtime_config"] = runtime_config
-                expanded_products.append(product_dict)
-
-    workload_manifest.products = expanded_products
+    workload_manifest.products = flattened_products
     return workload_manifest
 
 
@@ -248,14 +239,13 @@ def load_workloads(
 
     workloads: List[dotdict] = []
     build_workloads: List = []
-    for file in list(recipes_dir.glob("*.yaml")) + list(local_dir.glob("*.yaml")):
+    for file in list(recipes_dir.glob("**/*.yaml")) + list(local_dir.glob("**/*.yaml")):
         workloads += load_and_flatten(config_path=str(file))
         if file.stem.startswith("_build"):
             build_workloads.append(load_config(config_path=str(file)))
 
     if scope:
         workloads = filter_by_scope(workload_manifests=workloads, scope=scope)
-
     if workloads and environment:
         workloads = filter_by_environment(workload_manifests=workloads, environment=environment)
 
diff --git a/tests/test_utils/recipes/gpt-gb200.yaml b/tests/test_utils/recipes/gb200/gpt.yaml
similarity index 86%
rename from tests/test_utils/recipes/gpt-gb200.yaml
rename to tests/test_utils/recipes/gb200/gpt.yaml
index 9c3786332c9..e09ab673615 100644
--- a/tests/test_utils/recipes/gpt-gb200.yaml
+++ b/tests/test_utils/recipes/gb200/gpt.yaml
@@ -37,7 +37,7 @@ spec:
     ls
     cd /opt/megatron-lm
 
-    NAME=$(echo {test_case}_{environment} | sed 's/dgx_h100/dgx_a100/g')
+    NAME=$(echo {test_case}_{environment} | sed 's/dgx_gb200/dgx_a100/g')
     export GPUS_PER_NODE={gpus}
 
     ARGUMENTS=(
@@ -64,6 +64,9 @@ spec:
     exit $exit_code
 
 products:
+  #######################################################################
+  # Nightly tests: Run DEV unless something is flaky       #
+  #######################################################################
   - test_case: [gpt3_mcore_tp1_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather]
     products:
       - environment: [dev]
@@ -78,7 +81,6 @@ products:
     products:
       - environment: [dev]
         scope: [nightly]
-        platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_tp1_pp4]
     products:
       - environment: [dev]
@@ -88,7 +90,6 @@ products:
     products:
       - environment: [dev]
         scope: [nightly]
-        platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_tp4_pp1_resume_torch]
     products:
       - environment: [dev]
@@ -99,6 +100,10 @@ products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+  #######################################################################
+  # mr, mr-github tests: Mostly DEV on mr, mr-github, and LTS on nightly cadence, except for  #
+  #             some very important tests.                              #
+  #######################################################################
   - test_case: [gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files]
     products:
       - environment: [dev]
@@ -112,13 +117,17 @@ products:
   - test_case: [gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer]
     products:
       - environment: [dev]
-        scope: [nightly]
+        scope: [mr, mr-github-broken]
         platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute]
     products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+  # - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_cp4_a2a_p2p_nondeterministic]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
   - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings]
     products:
       - environment: [dev]
@@ -164,26 +173,16 @@ products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+      # - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist]
+      #   products:
+      #     - environment: [dev]
+      #       scope: [nightly]
+      #       platforms: [dgx_gb200] # Hangs: #513
   - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce]
     products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
-  - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather]
-    products:
-      - environment: [dev]
-        scope: [nightly]
-        platforms: [dgx_gb200]
-  - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_untied]
-    products:
-      - environment: [dev]
-        scope: [nightly]
-        platforms: [dgx_gb200]
-  - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_tunable_overlap]
-    products:
-      - environment: [dev]
-        scope: [nightly]
-        platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap]
     products:
       - environment: [dev]
@@ -264,6 +263,11 @@ products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp1_gdn]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github, mr-github-slim]
+        platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp2_pp2_mla]
     products:
       - environment: [dev]
@@ -284,11 +288,6 @@ products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
-  - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_no_create_attention_mask_in_dataloader]
-    products:
-      - environment: [dev]
-        scope: [nightly]
-        platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone]
     products:
       - environment: [dev]
@@ -329,15 +328,18 @@ products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+  #######################################################################
+  # Super important mr, mr-github tests that run for DEV per mr, mr-github       #
+  #######################################################################
   - test_case: [gpt3_mcore_reruns_persistent_1]
     products:
       - environment: [dev]
-        scope: [nightly]
+        scope: [mr, mr-github-broken]
         platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer]
     products:
       - environment: [dev]
-        scope: [nightly]
+        scope: [mr, mr-github, mr-github-slim]
         platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather]
     products:
@@ -347,10 +349,20 @@ products:
   - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather]
     products:
       - environment: [dev]
-        scope: [nightly]
+        scope: [mr, mr-github, mr-github-slim]
         platforms: [dgx_gb200]
   - test_case: [gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone]
     products:
       - environment: [dev]
         scope: [nightly]
         platforms: [dgx_gb200]
+  - test_case: [gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap]
+    products:
+      - environment: [dev]
+        scope: [weekly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap]
+    products:
+      - environment: [dev]
+        scope: [weekly]
+        platforms: [dgx_gb200]
diff --git a/tests/test_utils/recipes/gb200/moe.yaml b/tests/test_utils/recipes/gb200/moe.yaml
new file mode 100644
index 00000000000..bfa760caa29
--- /dev/null
+++ b/tests/test_utils/recipes/gb200/moe.yaml
@@ -0,0 +1,220 @@
+type: basic
+format_version: 1
+maintainers: [mcore]
+loggers: [stdout]
+spec:
+  name: "{test_case}_{environment}_{platforms}"
+  model: moe
+  build: mcore-pyt-{environment}
+  nodes: 2
+  gpus: 4
+  n_repeat: 5
+  platforms: dgx_gb200
+  script_setup: |
+    unset https_proxy
+    echo "machine gitlab-master.nvidia.com login okoenig password $RO_API_TOKEN" | tee -a /root/.netrc
+
+    # Checkout latest
+    cd /opt
+    rm -rf /opt/megatron-lm; mkdir megatron-lm; cd megatron-lm
+    git init
+    git remote add origin $MCORE_REPO
+    git fetch origin '+refs/merge-requests/*:refs/remotes/merge-requests/*'
+    git fetch origin $MCORE_MR_COMMIT
+    git checkout $MCORE_MR_COMMIT
+    git rev-parse HEAD
+
+    # Checkout backwards-ref
+    cd /opt
+    rm -rf /opt/megatron-lm-legacy; mkdir megatron-lm-legacy; cd megatron-lm-legacy
+    git init
+    git remote add origin $MCORE_REPO
+    git fetch origin $MCORE_BACKWARDS_COMMIT
+    git checkout $MCORE_BACKWARDS_COMMIT
+    git rev-parse HEAD
+    rm -rf megatron; cp -a /opt/megatron-lm/megatron ./
+  script: |-
+    ls
+    cd /opt/megatron-lm
+
+    NAME=$(echo {test_case}_{environment} | sed 's/dgx_gb200/dgx_a100/g')
+    export GPUS_PER_NODE={gpus}
+
+    ARGUMENTS=(
+        "DATA_PATH=/mnt/artifacts"
+        "DATA_CACHE_PATH=/lustre/fsw/coreai_dlalgo_mcore/mcore_ci/data/$RUN_ID/cache/"
+        "OUTPUT_PATH={assets_dir}"
+        "TENSORBOARD_PATH={assets_dir}/tensorboard"
+        "CHECKPOINT_SAVE_PATH={artifacts_dir}/checkpoints"
+        "CHECKPOINT_LOAD_PATH=/mnt/artifacts"
+        "TRAINING_SCRIPT_PATH=pretrain_gpt.py"
+        "TRAINING_PARAMS_PATH=./tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml"
+        "GOLDEN_VALUES_PATH=./tests/functional_tests/test_cases/{model}/{test_case}/golden_values_{environment}_{platforms}.json"
+        "N_REPEAT={n_repeat}"
+        "ENABLE_LIGHTWEIGHT_MODE=${{ENABLE_LIGHTWEIGHT_MODE}}"
+        "RECORD_CHECKPOINTS=${{RECORD_CHECKPOINTS}}"
+    )
+
+    bash ./tests/functional_tests/shell_test_utils/run_ci_test.sh ${{ARGUMENTS[@]}}
+
+products:
+  #######################################################################
+  # Nightly tests: Run both DEV and LTS unless something is flaky       #
+  #######################################################################
+  - test_case: [gpt3_mcore_tp2_pp2_ep2_te_4experts2parallel]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_cp2_pp2_ep2_te_4experts2parallel_nondeterministic_dp_last]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  # - test_case: [gpt3_mcore_tp2_pp2_resume_torch_dist_te_2experts]
+  #   products: # non-determinism: #478
+  #     - environment: [dev, lts]
+  #       scope: [nightly]
+  #######################################################################
+  # Weekly tests: Run both DEV and LTS unless something is flaky        #
+  #######################################################################
+  #######################################################################
+  # mr, mr-github tests: Mostly DEV on mr, mr-github, and LTS on nightly cadence, except for  #
+  #             some very important tests.                              #
+  #######################################################################
+  - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  # - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_mtp_resume_torch_dist_fp8]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200] # hang: #513
+  - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_resume_torch_dist_attn_cudagraph]
+    products:
+      - environment: [dev]
+        scope: [mr-broken]
+        platforms: [dgx_gb200] # hang: #513
+  # - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_selective_recompute_experimental]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200] # hang: #513
+  - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_zp_z3_resume_torch_dist_te_8experts2parallel_top2router]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  # - test_case: [gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200]
+  # - test_case: [gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph]
+    products:
+      - environment: [dev]
+        scope: [nightly]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github, mr-slim]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_muon]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github, mr-slim]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading]
+    products:
+      - environment: [dev]
+        scope: [mr-broken]
+        platforms: [dgx_gb200]
+  #######################################################################
+  # Super important mr, mr-github tests that run for both DEV and LTS per mr, mr-github       #
+  #######################################################################
+  # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_te_8experts2parallel_dist_optimizer]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200]
+  # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_te_8experts2parallel_groupedGEMM]
+  #   products:
+  #     - environment: [dev]
+  #       scope: [nightly]
+  #       platforms: [dgx_gb200]
+  ###########################
+  # Merge train tests       #
+  ###########################
+  - test_case: [gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github, mr-github-slim]
+        platforms: [dgx_gb200]
+  - test_case: [gpt3_mcore_te_tp2_pp2_ep4_etp1_memory_speed]
+    products:
+      - environment: [dev]
+        scope: [mr-broken]
+        platforms: [dgx_gb200]
diff --git a/tests/test_utils/recipes/gb200/unit-tests.yaml b/tests/test_utils/recipes/gb200/unit-tests.yaml
new file mode 100644
index 00000000000..0e8cb72916b
--- /dev/null
+++ b/tests/test_utils/recipes/gb200/unit-tests.yaml
@@ -0,0 +1,153 @@
+type: basic
+format_version: 1
+maintainers: [mcore]
+loggers: [stdout]
+spec:
+  name: "{test_case}_{environment}_{platforms}_{tag}"
+  model: unit-tests
+  nodes: 2
+  build: mcore-pyt-{environment}
+  gpus: 4
+  platforms: dgx_gb200
+  script_setup: |
+    unset https_proxy
+    echo "machine gitlab-master.nvidia.com login okoenig password $RO_API_TOKEN" | tee -a /root/.netrc
+
+    # Checkout latest
+    cd /opt
+    rm -rf /opt/megatron-lm; mkdir megatron-lm; cd megatron-lm
+    git init
+    git remote add origin $MCORE_REPO
+    git fetch origin '+refs/merge-requests/*:refs/remotes/merge-requests/*'
+    git fetch origin $MCORE_MR_COMMIT
+    git checkout $MCORE_MR_COMMIT
+    git rev-parse HEAD
+
+    # Checkout backwards-ref
+    cd /opt
+    bash /opt/megatron-lm/.gitlab/scripts/fetch-legacy-suite.sh \
+      --backwards-commit $MCORE_BACKWARDS_COMMIT \
+      --repo $MCORE_REPO
+
+  script: |-
+    ls
+
+    TAG={tag}
+    ENVIRONMENT={environment}
+    BUCKET="{test_case}"
+    UNIT_TEST_REPEAT={n_repeat}
+    export GPUS_PER_NODE={gpus}
+
+    if [[ "$TAG" == "latest" ]]; then
+        TEST_PATH="/opt/megatron-lm"
+    else
+        TEST_PATH="/opt/megatron-lm-legacy/"
+    fi
+
+
+    bash $TEST_PATH/tests/unit_tests/run_ci_test.sh \
+      --tag $TAG \
+      --environment $ENVIRONMENT \
+      --bucket $BUCKET \
+      --unit-test-repeat $UNIT_TEST_REPEAT \
+      --log-dir {assets_dir}/logs/1/
+
+    ls -al 
+
+    cd $TEST_PATH
+    /opt/venv/bin/coverage xml 
+    cp .coverage {assets_dir}/coverage_report
+    cp coverage.xml {assets_dir}
+
+products:
+  - test_case: [tests/unit_tests/test_model_configs.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/test_fp8_param.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/pipeline_parallel/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/models/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/data/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/dist_checkpointing/test_optimizer.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/dist_checkpointing/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/dist_checkpointing/models/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/dist_checkpointing/models/test_moe_experts.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/transformer/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/transformer/moe/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/distributed/megatron_fsdp/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
+  - test_case: [tests/unit_tests/**/*.py]
+    products:
+      - environment: [dev]
+        tag: [latest, legacy]
+        scope: [unit-tests]
+        n_repeat: [1]
+        time_limit: [1800]
diff --git a/tests/test_utils/recipes/ckpt_converter.yaml b/tests/test_utils/recipes/h100/ckpt_converter.yaml
similarity index 100%
rename from tests/test_utils/recipes/ckpt_converter.yaml
rename to tests/test_utils/recipes/h100/ckpt_converter.yaml
diff --git a/tests/test_utils/recipes/gpt-dynamic-inference-cuda-graphs.yaml b/tests/test_utils/recipes/h100/gpt-dynamic-inference-cuda-graphs.yaml
similarity index 100%
rename from tests/test_utils/recipes/gpt-dynamic-inference-cuda-graphs.yaml
rename to tests/test_utils/recipes/h100/gpt-dynamic-inference-cuda-graphs.yaml
diff --git a/tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml
similarity index 100%
rename from tests/test_utils/recipes/gpt-dynamic-inference-with-coordinator.yaml
rename to tests/test_utils/recipes/h100/gpt-dynamic-inference-with-coordinator.yaml
diff --git a/tests/test_utils/recipes/gpt-dynamic-inference.yaml b/tests/test_utils/recipes/h100/gpt-dynamic-inference.yaml
similarity index 100%
rename from tests/test_utils/recipes/gpt-dynamic-inference.yaml
rename to tests/test_utils/recipes/h100/gpt-dynamic-inference.yaml
diff --git a/tests/test_utils/recipes/gpt-grads.yaml b/tests/test_utils/recipes/h100/gpt-grads.yaml
similarity index 100%
rename from tests/test_utils/recipes/gpt-grads.yaml
rename to tests/test_utils/recipes/h100/gpt-grads.yaml
diff --git a/tests/test_utils/recipes/gpt-grpo.yaml b/tests/test_utils/recipes/h100/gpt-grpo.yaml
similarity index 90%
rename from tests/test_utils/recipes/gpt-grpo.yaml
rename to tests/test_utils/recipes/h100/gpt-grpo.yaml
index e707c1c2431..6df72dc3235 100644
--- a/tests/test_utils/recipes/gpt-grpo.yaml
+++ b/tests/test_utils/recipes/h100/gpt-grpo.yaml
@@ -54,15 +54,25 @@ spec:
     bash ./tests/functional_tests/shell_test_utils/run_ci_test.sh ${{ARGUMENTS[@]}}
 
 products:
+  - test_case: [gpt_grpo_basic_function]
+    products:
+      - environment: [dev]
+        scope: [mr, mr-github]
+        platforms: [dgx_h100]
   - test_case: [gpt_grpo_tp4_pp1_dp2_8b_throughput]
     products:
       - environment: [dev]
         scope: [mr]
         platforms: [dgx_h100]
+  - test_case: [gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput]
+    products:
+      - environment: [dev]
+        scope: [mr]
+        platforms: [dgx_h100]
   - test_case: [gpt_grpo_tp4_pp1_dp2_8b_throughput_github]
     products:
       - environment: [dev]
-        scope: [mr-github]
+        scope: [mr-github-broken]
         platforms: [dgx_h100]
   - test_case: [gpt_grpo_tp1tp2_pp1_dp8_583m_throughputtest]
     products:
@@ -74,8 +84,3 @@ products:
       - environment: [dev]
         scope: [mr-github-broken]
         platforms: [dgx_h100]
-  - test_case: [gpt_grpo_tp2tp1_pp4pp2_dp8_583m_throughputtest]
-    products:
-      - environment: [dev]
-        scope: [mr-broken]
-        platforms: [dgx_h100]
diff --git a/tests/test_utils/recipes/gpt-nemo.yaml b/tests/test_utils/recipes/h100/gpt-nemo.yaml
similarity index 100%
rename from tests/test_utils/recipes/gpt-nemo.yaml
rename to tests/test_utils/recipes/h100/gpt-nemo.yaml
diff --git a/tests/test_utils/recipes/gpt-static-inference.yaml b/tests/test_utils/recipes/h100/gpt-static-inference.yaml
similarity index 96%
rename from tests/test_utils/recipes/gpt-static-inference.yaml
rename to tests/test_utils/recipes/h100/gpt-static-inference.yaml
index 806762531fd..87046588b2b 100644
--- a/tests/test_utils/recipes/gpt-static-inference.yaml
+++ b/tests/test_utils/recipes/h100/gpt-static-inference.yaml
@@ -63,15 +63,15 @@ products:
   - test_case: [gpt_static_inference_tp1_pp1_583m_cudagraphs]
     products:
       - environment: [dev]
-        scope: [mr]
+        scope: [mr, mr-github]
         platforms: [dgx_h100]
   - test_case: [gpt_static_inference_tp1_pp1_583m_fp8_cudagraphs]
     products:
       - environment: [dev]
-        scope: [mr]
+        scope: [mr, mr-github]
         platforms: [dgx_h100]
   - test_case: [gpt_static_inference_tp1_pp1_16b_multiprompt_tokensmatch]
     products:
       - environment: [dev]
-        scope: [mr]
+        scope: [mr, mr-github]
         platforms: [dgx_h100]
diff --git a/tests/test_utils/recipes/gpt.yaml b/tests/test_utils/recipes/h100/gpt.yaml
similarity index 88%
rename from tests/test_utils/recipes/gpt.yaml
rename to tests/test_utils/recipes/h100/gpt.yaml
index a97a4d7bb38..52e38760f84 100644
--- a/tests/test_utils/recipes/gpt.yaml
+++ b/tests/test_utils/recipes/h100/gpt.yaml
@@ -487,67 +487,12 @@ products:
         platforms: [dgx_h100]
       - environment: [lts]
         scope: [nightly]
-  # - test_case: [gpt3_mcore_te_tp4_pp2_frozen_resume_torch_dist_reshard_8x1xNone]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [mr]
-  #       platforms: [dgx_h100]
-  # - test_case: [gpt3_mcore_te_tp1_pp1_frozen_resume_torch_dist_dist_optimizer]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [mr]
-  #       platforms: [dgx_h100]
-  # - test_case: [gpt3_mcore_te_tp1_pp4_frozen_resume_torch_dist_swiglu]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [mr]
-  #       platforms: [dgx_h100]
-  # - test_case: [gpt3_mcore_te_tp2_pp1_frozen_resume_torch_dist_cp2_nondeterministic]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [mr]
-  #       platforms: [dgx_a100, dgx_h100]
-  # - test_case: [gpt3_weekly_dgx_b200_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_b200]
-  # - test_case: [gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_b200]
-  # - test_case: [gpt3_weekly_dgx_b200_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_b200]
-  # - test_case: [gpt3_weekly_dgx_b200_mcore_tp4_cp2_mxfp8_tp_sp_cp]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_b200]
-  # - test_case: [gpt3_weekly_dgx_b200_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_b200]
-  - test_case: [gpt3_weekly_dgx_h100_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap]
+  - test_case: [gpt3_weekly_mcore_tp2_pp2_current_scaling_native_fp8_tp_pp_sp_tp_overlap]
     products:
       - environment: [dev]
         scope: [weekly]
         platforms: [dgx_h100]
-  # - test_case: [gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_fsdp]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_h100]
-  # - test_case: [gpt3_weekly_dgx_h100_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap]
-  #   products:
-  #     - environment: [dev]
-  #       scope: [weekly]
-  #       platforms: [dgx_h100]
-  - test_case: [gpt3_weekly_dgx_h100_mcore_tp4_cp2_native_fp8_tp_sp_cp_tp_overlap]
+  - test_case: [gpt3_weekly_mcore_tp4_cp2_current_scaling_native_fp8_tp_sp_cp_tp_overlap]
     products:
       - environment: [dev]
         scope: [weekly]
diff --git a/tests/test_utils/recipes/mamba-dynamic-inference.yaml b/tests/test_utils/recipes/h100/mamba-dynamic-inference.yaml
similarity index 100%
rename from tests/test_utils/recipes/mamba-dynamic-inference.yaml
rename to tests/test_utils/recipes/h100/mamba-dynamic-inference.yaml
diff --git a/tests/test_utils/recipes/mamba-static-inference.yaml b/tests/test_utils/recipes/h100/mamba-static-inference.yaml
similarity index 100%
rename from tests/test_utils/recipes/mamba-static-inference.yaml
rename to tests/test_utils/recipes/h100/mamba-static-inference.yaml
diff --git a/tests/test_utils/recipes/mamba.yaml b/tests/test_utils/recipes/h100/mamba.yaml
similarity index 100%
rename from tests/test_utils/recipes/mamba.yaml
rename to tests/test_utils/recipes/h100/mamba.yaml
diff --git a/tests/test_utils/recipes/mimo.yaml b/tests/test_utils/recipes/h100/mimo.yaml
similarity index 100%
rename from tests/test_utils/recipes/mimo.yaml
rename to tests/test_utils/recipes/h100/mimo.yaml
diff --git a/tests/test_utils/recipes/module_performance.yaml b/tests/test_utils/recipes/h100/module_performance.yaml
similarity index 100%
rename from tests/test_utils/recipes/module_performance.yaml
rename to tests/test_utils/recipes/h100/module_performance.yaml
diff --git a/tests/test_utils/recipes/moe-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
similarity index 97%
rename from tests/test_utils/recipes/moe-dynamic-inference-with-coordinator.yaml
rename to tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
index 513aa92834b..4ce11808bdc 100644
--- a/tests/test_utils/recipes/moe-dynamic-inference-with-coordinator.yaml
+++ b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml
@@ -39,7 +39,7 @@ spec:
     ARGUMENTS=(
         "CHECKPOINT_LOAD_PATH=/mnt/artifacts"
         "CHECKPOINT_SAVE_PATH=/tmp/checkpoints"
-        "DATA_PATH=null"
+        "DATA_PATH=/mnt/artifacts/"
         "DATA_CACHE_PATH=/workspace/data/cache"
         "TRAINING_SCRIPT_PATH=examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py"
         "TRAINING_PARAMS_PATH=./tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml"
@@ -63,6 +63,6 @@ products:
   - test_case: [gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_cudagraph_zmq]
     products:
       - environment: [dev]
-        scope: [flaky]
+        scope: [mr-github]
         platforms: [dgx_h100]
   
diff --git a/tests/test_utils/recipes/moe-dynamic-inference.yaml b/tests/test_utils/recipes/h100/moe-dynamic-inference.yaml
similarity index 100%
rename from tests/test_utils/recipes/moe-dynamic-inference.yaml
rename to tests/test_utils/recipes/h100/moe-dynamic-inference.yaml
diff --git a/tests/test_utils/recipes/moe-grpo.yaml b/tests/test_utils/recipes/h100/moe-grpo.yaml
similarity index 100%
rename from tests/test_utils/recipes/moe-grpo.yaml
rename to tests/test_utils/recipes/h100/moe-grpo.yaml
diff --git a/tests/test_utils/recipes/moe-static-inference.yaml b/tests/test_utils/recipes/h100/moe-static-inference.yaml
similarity index 95%
rename from tests/test_utils/recipes/moe-static-inference.yaml
rename to tests/test_utils/recipes/h100/moe-static-inference.yaml
index f10d293e953..fdab3ff430b 100644
--- a/tests/test_utils/recipes/moe-static-inference.yaml
+++ b/tests/test_utils/recipes/h100/moe-static-inference.yaml
@@ -58,15 +58,15 @@ products:
   - test_case: [gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch]
     products:
       - environment: [dev]
-        scope: [mr]
+        scope: [mr, mr-github-broken]
         platforms: [dgx_h100]
   - test_case: [gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch]
     products:
       - environment: [dev]
-        scope: [mr]
+        scope: [mr, mr-github-broken]
         platforms: [dgx_h100]
   - test_case: [gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch]
     products:
       - environment: [dev]
-        scope: [mr-broken]
+        scope: [mr, mr-github-broken]
         platforms: [dgx_h100]
diff --git a/tests/test_utils/recipes/moe.yaml b/tests/test_utils/recipes/h100/moe.yaml
similarity index 100%
rename from tests/test_utils/recipes/moe.yaml
rename to tests/test_utils/recipes/h100/moe.yaml
diff --git a/tests/test_utils/recipes/multimodal-llava.yaml b/tests/test_utils/recipes/h100/multimodal-llava.yaml
similarity index 100%
rename from tests/test_utils/recipes/multimodal-llava.yaml
rename to tests/test_utils/recipes/h100/multimodal-llava.yaml
diff --git a/tests/test_utils/recipes/unit-tests.yaml b/tests/test_utils/recipes/h100/unit-tests.yaml
similarity index 100%
rename from tests/test_utils/recipes/unit-tests.yaml
rename to tests/test_utils/recipes/h100/unit-tests.yaml
diff --git a/tests/unit_tests/data/test_builder.py b/tests/unit_tests/data/test_builder.py
index d0e86c87fb8..59f73911c82 100644
--- a/tests/unit_tests/data/test_builder.py
+++ b/tests/unit_tests/data/test_builder.py
@@ -22,7 +22,7 @@
 from megatron.core.datasets.indexed_dataset import DType, IndexedDatasetBuilder
 from megatron.core.datasets.megatron_dataset import LowLevelDataset, MegatronDataset
 from megatron.core.datasets.utils import Split, compile_helpers, get_blend_from_list
-from megatron.training.tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.training.utils import get_blend_and_blend_per_split
 from tests.unit_tests.dist_checkpointing import TempNamedDir
 from tests.unit_tests.test_utilities import Utils
diff --git a/tests/unit_tests/data/test_fim_dataset.py b/tests/unit_tests/data/test_fim_dataset.py
index 7022a4b5fa9..c5fb7329405 100644
--- a/tests/unit_tests/data/test_fim_dataset.py
+++ b/tests/unit_tests/data/test_fim_dataset.py
@@ -12,6 +12,7 @@
 
 @pytest.mark.parametrize("spm_rate", [0.0, 1.0])
 @pytest.mark.parametrize("split_sample", [None, "python"])
+@pytest.mark.usefixtures("ensure_test_data")
 def test_fim_gpt_dataset(spm_rate, split_sample):
     if torch.distributed.is_available():
         Utils.initialize_distributed()
diff --git a/tests/unit_tests/data/test_gpt_dataset.py b/tests/unit_tests/data/test_gpt_dataset.py
index fdfa8645792..a2d25090fb8 100644
--- a/tests/unit_tests/data/test_gpt_dataset.py
+++ b/tests/unit_tests/data/test_gpt_dataset.py
@@ -13,7 +13,7 @@
 from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
 from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset
 from megatron.core.datasets.utils import compile_helpers
-from megatron.training.tokenizer.tokenizer import _NullTokenizer
+from megatron.core.tokenizers import MegatronTokenizer
 from tests.unit_tests.test_utilities import Utils
 
 _MOCK_VOCAB_SIZE = 8192
@@ -37,7 +37,9 @@ def test_mock_gpt_dataset():
     else:
         compile_helpers()
 
-    tokenizer = _NullTokenizer(vocab_size=_MOCK_VOCAB_SIZE)
+    tokenizer = MegatronTokenizer.from_pretrained(
+        metadata_path={"library": "null-text"}, vocab_size=_MOCK_VOCAB_SIZE
+    )
 
     config = GPTDatasetConfig(
         random_seed=1234,
diff --git a/tests/unit_tests/data/test_multimodal_dataset.py b/tests/unit_tests/data/test_multimodal_dataset.py
index f6ff575684c..2f9f76b7218 100644
--- a/tests/unit_tests/data/test_multimodal_dataset.py
+++ b/tests/unit_tests/data/test_multimodal_dataset.py
@@ -11,7 +11,7 @@
 from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
 from megatron.core.datasets.multimodal_dataset import MockMultimodalDataset, MultimodalDatasetConfig
 from megatron.core.datasets.utils import compile_helpers
-from megatron.training.tokenizer.tokenizer import _NullTokenizer
+from megatron.core.tokenizers import MegatronTokenizer
 from tests.unit_tests.test_utilities import Utils
 
 _MOCK_VOCAB_SIZE = 8192
@@ -26,6 +26,9 @@ def test_mock_multimodal_dataset():
     else:
         compile_helpers()
 
+    tokenizer = MegatronTokenizer.from_pretrained(
+        metadata_path={"library": "null-text"}, vocab_size=_MOCK_VOCAB_SIZE
+    )
     config = MultimodalDatasetConfig(
         random_seed=1234,
         sequence_length=1024,
@@ -35,7 +38,7 @@ def test_mock_multimodal_dataset():
         image_h=336,
         image_w=336,
         split="990,9,1",
-        tokenizer=_NullTokenizer(vocab_size=_MOCK_VOCAB_SIZE),
+        tokenizer=tokenizer,
         mid_level_dataset_surplus=0.005,
     )
 
diff --git a/tests/unit_tests/data/test_preprocess_data.py b/tests/unit_tests/data/test_preprocess_data.py
index 48f3a2e7bb9..e6922ec3748 100644
--- a/tests/unit_tests/data/test_preprocess_data.py
+++ b/tests/unit_tests/data/test_preprocess_data.py
@@ -10,10 +10,7 @@
 import requests
 
 from megatron.core.datasets.indexed_dataset import IndexedDataset
-from megatron.training.tokenizer.gpt2_tokenization import (
-    PRETRAINED_MERGES_ARCHIVE_MAP,
-    PRETRAINED_VOCAB_ARCHIVE_MAP,
-)
+from megatron.core.tokenizers.text.libraries.megatron_hf_tokenizer import MEGATRON_CONFIG_MAP
 from tools.merge_datasets import main as merge_main
 from tools.preprocess_data import Encoder
 from tools.preprocess_data import get_args as build_args
@@ -170,7 +167,7 @@ def gpt2_vocab(odir):
         return __LOCAL_GPT2_VOCAB
     path = os.path.join(odir, "vocab.json")
     with open(path, "wb") as writer:
-        writer.write(requests.get(PRETRAINED_VOCAB_ARCHIVE_MAP['gpt2']).content)
+        writer.write(requests.get(MEGATRON_CONFIG_MAP['GPT2BPETokenizer']['vocab']).content)
     return path
 
 
@@ -179,7 +176,7 @@ def gpt2_merge(odir):
         return __LOCAL_GPT2_MERGE
     path = os.path.join(odir, "merge.txt")
     with open(path, "wb") as writer:
-        writer.write(requests.get(PRETRAINED_MERGES_ARCHIVE_MAP['gpt2']).content)
+        writer.write(requests.get(MEGATRON_CONFIG_MAP['GPT2BPETokenizer']['merges_file']).content)
     return path
 
 
diff --git a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py
index 0970e2adc8a..037e368ea2f 100644
--- a/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py
+++ b/tests/unit_tests/dist_checkpointing/models/test_mlp_glu.py
@@ -13,7 +13,9 @@
     get_param_id_to_sharded_param_map,
     optim_state_to_sharding_state,
 )
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.mlp import MLP, apply_swiglu_sharded_factory
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -32,7 +34,7 @@ def initialize_mlp(glu=True):
         gated_linear_unit=glu,
     )
     return MLP(
-        transformer_config, get_gpt_layer_with_transformer_engine_spec().submodules.mlp.submodules
+        transformer_config, get_gpt_layer_with_transformer_engine_submodules().mlp.submodules
     )
 
 
diff --git a/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py b/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
index ca546d746af..5d8153f49d7 100644
--- a/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
+++ b/tests/unit_tests/dist_checkpointing/models/test_moe_experts.py
@@ -1,6 +1,4 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
-import os
-
 import pytest
 import torch
 from transformer_engine.pytorch.fp8 import check_fp8_support, fp8_autocast
@@ -17,11 +15,18 @@
     FullyParallelSaveStrategyWrapper,
 )
 from megatron.core.models.gpt.gpt_layer_specs import (
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_local_submodules,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
-from megatron.core.transformer.moe.experts import GroupedMLP, SequentialMLP, TEGroupedMLP
+from megatron.core.transformer.mlp import MLPSubmodules
+from megatron.core.transformer.moe.experts import (
+    GroupedMLP,
+    SequentialMLP,
+    TEGroupedMLP,
+    TEGroupedMLPSubmodules,
+)
+from megatron.core.transformer.moe.moe_layer import MoESubmodules
 from megatron.core.transformer.moe.moe_utils import get_default_pg_collection
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.utils import is_te_min_version
@@ -54,33 +59,41 @@ def initialize_expert_layer(seed, glu=True, expert_type='sequential', fp8=False,
     if expert_type == 'grouped':
         model = GroupedMLP(num_local_experts, transformer_config, pg_collection)
     elif expert_type == 'te_grouped':
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        layer_submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=True
         )
+        assert isinstance(layer_submodules.mlp.submodules, MoESubmodules)
+        assert isinstance(
+            layer_submodules.mlp.submodules.experts.submodules, TEGroupedMLPSubmodules
+        )
         model = TEGroupedMLP(
             num_local_experts,
             transformer_config,
-            transformer_layer_spec.submodules.mlp.submodules.experts.submodules,
+            layer_submodules.mlp.submodules.experts.submodules,
             pg_collection,
         )
     elif expert_type == 'sequential':
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        layer_submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
+        assert isinstance(layer_submodules.mlp.submodules, MoESubmodules)
+        assert isinstance(layer_submodules.mlp.submodules.experts.submodules, MLPSubmodules)
         model = SequentialMLP(
             num_local_experts,
             transformer_config,
-            transformer_layer_spec.submodules.mlp.submodules.experts.submodules,
+            layer_submodules.mlp.submodules.experts.submodules,
             pg_collection,
         )
     elif expert_type == 'te_sequential':
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        layer_submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
+        assert isinstance(layer_submodules.mlp.submodules, MoESubmodules)
+        assert isinstance(layer_submodules.mlp.submodules.experts.submodules, MLPSubmodules)
         model = SequentialMLP(
             num_local_experts,
             transformer_config,
-            transformer_layer_spec.submodules.mlp.submodules.experts.submodules,
+            layer_submodules.mlp.submodules.experts.submodules,
             pg_collection,
         )
     else:
diff --git a/tests/unit_tests/dist_checkpointing/test_torch_dist.py b/tests/unit_tests/dist_checkpointing/test_torch_dist.py
index 4f4df058977..64a47b8cbb3 100644
--- a/tests/unit_tests/dist_checkpointing/test_torch_dist.py
+++ b/tests/unit_tests/dist_checkpointing/test_torch_dist.py
@@ -11,7 +11,6 @@
 from megatron.core.dist_checkpointing import ShardedTensor, load, save
 from megatron.core.dist_checkpointing.dict_utils import diff
 from megatron.core.dist_checkpointing.serialization import get_default_save_sharded_strategy
-from megatron.core.dist_checkpointing.strategies.async_utils import AsyncCallsQueue
 from tests.unit_tests.dist_checkpointing import TempNamedDir
 from tests.unit_tests.test_utilities import Utils
 
@@ -117,7 +116,7 @@ def test_cpu_tensors_dont_take_too_much_space(self, tmp_path_dist_ckpt):
         ) as ckpt_dir:
             save(sharded_state_dict, ckpt_dir)
 
-            distcp_files = [(ckpt_dir / '__0_0.distcp'), (ckpt_dir / '__0_1.distcp')]
+            distcp_files = [(ckpt_dir / '__0_0.distcp')]
             for file in distcp_files:
                 assert file.exists()
                 file_size = file.stat().st_size
diff --git a/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py b/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
index cbca505b405..d3ec9d3342a 100644
--- a/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
+++ b/tests/unit_tests/distributed/megatron_fsdp/test_mfsdp_fully_shard.py
@@ -494,11 +494,18 @@ def test_dcp_checkpoint_save_and_load(
             pre_output = model(toy_input, toy_input)
         pre_save_loss = mse_loss(pre_output, toy_target).item()
 
-        # Save deep copy of the model state dictionary before checkpointing.
-        s1 = deepcopy(model.state_dict())
-
-        # Save deep copy of the optimizer state dictionary before checkpointing.
-        o1 = deepcopy(optimizer.state_dict())
+        # Save deep copy of the model and optimizer state before checkpointing.
+        # NOTE(@cspades): deepcopy has issues with DTensors. Just clone().
+        s1 = {}
+        for key, val in model.state_dict().items():
+            s1[key] = val.clone()
+        optim_state_dict = optimizer.state_dict()
+        o1 = {"state": {}}
+        for idx, state in optim_state_dict["state"].items():
+            o1_state = o1["state"].setdefault(idx, {})
+            for key, val in state.items():
+                o1_state[key] = val.clone()
+        o1["param_groups"] = deepcopy(optim_state_dict["param_groups"])
 
         """
         MODEL CHECKPOINT SAVE
@@ -547,13 +554,13 @@ def test_dcp_checkpoint_save_and_load(
         torch.distributed.checkpoint.load(state_dict=ckpt_state_dict, checkpoint_id=str(CKPT_DIR))
         model.load_state_dict(ckpt_state_dict["model"], strict=False)
         optimizer.load_state_dict(ckpt_state_dict["optimizer"])
-        s2 = deepcopy(model.state_dict())
-        o2 = deepcopy(optimizer.state_dict())
 
         """
         MODEL CHECKPOINT STATE DICT VALIDATION
         """
         # Compare pre-save and post-load model state dictionaries.
+        s2 = model.state_dict()
+        nonempty_model_state = False
         for key in s1.keys() | s2.keys():
             v1 = s1.get(key, None)
             if isinstance(v1, DTensor):
@@ -568,14 +575,17 @@ def test_dcp_checkpoint_save_and_load(
                 v1.shape == v2.shape
             ), f"[Checkpoint Param {key} Shape Mismatch] {v1.shape} != {v2.shape}"
             assert torch.allclose(v1, v2), f"[Checkpoint Param {key} Value Mismatch] {v1} != {v2}"
+            nonempty_model_state = True
 
         # Compare pre-save and post-load optimizer state dictionaries.
+        o2 = optimizer.state_dict()
+        nonempty_optim_state = False
         for param_id in o1["state"].keys() | o2["state"].keys():
             param_state_1 = o1["state"].get(param_id, None)
             param_state_2 = o2["state"].get(param_id, None)
             assert (
                 param_state_1 is not None and param_state_2 is not None
-            ), f"[{param_id} Not Found] Original Param State: {param_state_1} | Checkpoint Param State: {param_state_2}"
+            ), f"[{param_id} Not Found] Original Optim State: {param_state_1} | Checkpoint Optim State: {param_state_2}"
             for key in param_state_1.keys() | param_state_2.keys():
                 v1 = param_state_1.get(key, None)
                 if isinstance(v1, DTensor):
@@ -585,23 +595,31 @@ def test_dcp_checkpoint_save_and_load(
                     v2 = v2.to_local()
                 assert (
                     v1 is not None and v2 is not None
-                ), f"[{param_id} {key} Not Found] Original Optimizer State: {v1} | Checkpoint Optimizer State: {v2}"
+                ), f"[{param_id} {key} Not Found] Original Optim State: {v1} | Checkpoint Optim State: {v2}"
                 assert (
                     v1.shape == v2.shape
-                ), f"[Optimizer State {param_id} {key} Shape Mismatch] {v1.shape} != {v2.shape}"
+                ), f"[Optim State {param_id} {key} Shape Mismatch] {v1.shape} != {v2.shape}"
                 assert torch.allclose(
                     v1, v2
-                ), f"[Optimizer State {param_id} {key} Value Mismatch] {v1} != {v2}"
+                ), f"[Optim State {param_id} {key} Value Mismatch] {v1} != {v2}"
+                nonempty_optim_state = True  # Optimizer state depends on wgrad, verify this!
         assert len(o1["param_groups"]) == len(
             o2["param_groups"]
-        ), f"[Optimizer State Param Groups Length Mismatch] {o1['param_groups']} != {o2['param_groups']}"
+        ), f"[Optim State Param Groups Length Mismatch] {o1['param_groups']} != {o2['param_groups']}"
         for i in range(len(o2["param_groups"])):
             for key in o1["param_groups"][i].keys():
                 v1 = o1["param_groups"][i][key]
                 v2 = o2["param_groups"][i][key]
-                assert (
-                    v1 == v2
-                ), f"[Optimizer State Param Group {i} {key} Value Mismatch] {v1} != {v2}"
+                assert v1 == v2, f"[Optim State Param Group {i} {key} Value Mismatch] {v1} != {v2}"
+
+        # Validate that at least 1 rank has a non-empty model and optimizer state.
+        # It is very possible that some ranks have completely empty state!
+        global_nonempty_model_state = [False] * torch.distributed.get_world_size()
+        torch.distributed.all_gather_object(global_nonempty_model_state, nonempty_model_state)
+        assert any(global_nonempty_model_state), "All ranks had an empty model state!"
+        global_nonempty_optim_state = [False] * torch.distributed.get_world_size()
+        torch.distributed.all_gather_object(global_nonempty_optim_state, nonempty_optim_state)
+        assert any(global_nonempty_optim_state), "All ranks had an empty optimizer state!"
 
         """
         MODEL CHECKPOINT FORWARD PASS VALIDATION
diff --git a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py
index e83f7142284..15c479774c6 100644
--- a/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py
+++ b/tests/unit_tests/distributed/test_grad_sync_with_expert_parallel.py
@@ -8,11 +8,12 @@
 
 from megatron.core import parallel_state
 from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig
-from megatron.core.distributed.param_and_grad_buffer import partition_buckets
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.moe.moe_layer import MoELayer
-from tests.unit_tests.test_utilities import TestModel, Utils
+from tests.unit_tests.test_utilities import Utils
 
 
 class TestMoEModel(torch.nn.Module):
@@ -41,15 +42,13 @@ def __init__(
             params_dtype=torch.bfloat16,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=moe_grouped_gemm
         )
         super().__init__()
         self.layers = torch.nn.ModuleList(
             [
-                MoELayer(
-                    transformer_config, transformer_layer_spec.submodules.mlp.submodules
-                ).cuda()
+                MoELayer(transformer_config, submodules.mlp.submodules).cuda()
                 for _ in range(num_layers)
             ]
         )
diff --git a/tests/unit_tests/export/trtllm/test_distributed_fp8.py b/tests/unit_tests/export/trtllm/test_distributed_fp8.py
index cf47a864108..09732e98263 100644
--- a/tests/unit_tests/export/trtllm/test_distributed_fp8.py
+++ b/tests/unit_tests/export/trtllm/test_distributed_fp8.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
 from functools import partial
 
 import pytest
@@ -15,8 +17,8 @@
 from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
+from megatron.core.tokenizers import MegatronTokenizer
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.training.tokenizer.tokenizer import _NullTokenizer
 from tests.unit_tests.test_utilities import Utils
 
 VOCAB_SIZE = 256
@@ -61,13 +63,16 @@ def _get_train_data_iterator():
     else:
         compile_helpers()
 
+    tokenizer = MegatronTokenizer.from_pretrained(
+        metadata_path={"library": "null-text"}, vocab_size=50
+    )
     config = GPTDatasetConfig(
         random_seed=0,
         sequence_length=SEQUENCE_LENGTH,
         reset_position_ids=False,
         reset_attention_mask=False,
         eod_mask_loss=False,
-        tokenizer=_NullTokenizer(vocab_size=50),
+        tokenizer=tokenizer,
         mid_level_dataset_surplus=0.005,
     )
 
diff --git a/tests/unit_tests/export/trtllm/test_single_device_fp8.py b/tests/unit_tests/export/trtllm/test_single_device_fp8.py
index 04bbfdb127d..2e0537894de 100644
--- a/tests/unit_tests/export/trtllm/test_single_device_fp8.py
+++ b/tests/unit_tests/export/trtllm/test_single_device_fp8.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
+
 from functools import partial
 
 import pytest
@@ -15,8 +17,8 @@
 from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
+from megatron.core.tokenizers import MegatronTokenizer
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.training.tokenizer.tokenizer import _NullTokenizer
 from tests.unit_tests.test_utilities import Utils
 
 SEQUENCE_LENGTH = 64
@@ -58,13 +60,16 @@ def _get_train_data_iterator():
     else:
         compile_helpers()
 
+    tokenizer = MegatronTokenizer.from_pretrained(
+        metadata_path={"library": "null-text"}, vocab_size=50
+    )
     config = GPTDatasetConfig(
         random_seed=0,
         sequence_length=SEQUENCE_LENGTH,
         reset_position_ids=False,
         reset_attention_mask=False,
         eod_mask_loss=False,
-        tokenizer=_NullTokenizer(vocab_size=50),
+        tokenizer=tokenizer,
         mid_level_dataset_surplus=0.005,
     )
 
diff --git a/tests/unit_tests/find_test_cases.py b/tests/unit_tests/find_test_cases.py
index 2e9f5515b7d..1445206cab5 100644
--- a/tests/unit_tests/find_test_cases.py
+++ b/tests/unit_tests/find_test_cases.py
@@ -50,7 +50,8 @@ def expand_pattern(pattern):
 
 def main():
     BUCKET = sys.argv[1]
-    YAML_FILE = 'tests/test_utils/recipes/unit-tests.yaml'
+    GPU_TYPE = sys.argv[2]
+    YAML_FILE = f'tests/test_utils/recipes/{GPU_TYPE}/unit-tests.yaml'
 
     all_test_cases = get_test_cases(YAML_FILE)
     bucket_files = set(expand_pattern(BUCKET))
diff --git a/tests/unit_tests/fusions/test_bias_dropout_fusion.py b/tests/unit_tests/fusions/test_bias_dropout_fusion.py
index 6303a87ba26..f8b23900543 100644
--- a/tests/unit_tests/fusions/test_bias_dropout_fusion.py
+++ b/tests/unit_tests/fusions/test_bias_dropout_fusion.py
@@ -1,7 +1,13 @@
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
 import pytest
 import torch
 
-from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
+from megatron.core.fusions.fused_bias_dropout import _bias_dropout_add_func, get_bias_dropout_add
+
+# ---------------------------------------------------------------------------
+# Existing test: fused vs. unfused parity (same dtype)
+# ---------------------------------------------------------------------------
 
 
 @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
@@ -46,3 +52,270 @@ def test_bias_dropout_add(dtype, training):
         # In‑place check for inference
         assert out_fused.data_ptr() == x_fused.data_ptr()
         assert torch.allclose(out_fused, x_fused, **tols)
+
+
+# ============================================================================
+# Tests for fp32 residual connection fix
+# ============================================================================
+#
+# The fix reverses the casting direction in _bias_dropout_add_func: when
+# x is bf16/fp16 and residual is fp32, x (and bias) are upcast to fp32
+# so that the residual stream stays in fp32.  The OLD (broken) code did the
+# opposite: it downcast the fp32 residual to match x's dtype, destroying
+# the fp32 residual stream from the very first layer.
+# ============================================================================
+
+
+class TestFp32ResidualPreservation:
+    """Tests that _bias_dropout_add_func preserves fp32 residual dtype."""
+
+    device = "cuda"
+    B, H = 16, 64
+
+    # -- helpers --------------------------------------------------------
+
+    @staticmethod
+    def _reference_bias_dropout_add(x, bias, residual, prob, training):
+        """Manual reference computation in fp32 (no dropout for determinism)."""
+        x_fp32 = x.float()
+        r_fp32 = residual.float()
+        if bias is not None:
+            b_fp32 = bias.float()
+            return r_fp32 + torch.nn.functional.dropout(x_fp32 + b_fp32, p=prob, training=training)
+        return r_fp32 + torch.nn.functional.dropout(x_fp32, p=prob, training=training)
+
+    # -- core: output dtype must follow residual, not x -----------------
+
+    @pytest.mark.parametrize("x_dtype", [torch.bfloat16, torch.float16])
+    @pytest.mark.parametrize("training", [True, False])
+    @pytest.mark.parametrize("has_bias", [True, False])
+    def test_output_dtype_is_residual_dtype(self, x_dtype, training, has_bias):
+        """The output tensor must have the same dtype as the residual (fp32),
+        NOT x's dtype.  This is the central invariant of the fix."""
+        x = torch.randn(self.B, self.H, dtype=x_dtype, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.float32, device=self.device)
+        bias = torch.randn(self.H, dtype=x_dtype, device=self.device) if has_bias else None
+
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=training)
+
+        assert (
+            out.dtype == torch.float32
+        ), f"Output dtype {out.dtype} must be fp32 (residual dtype), not {x_dtype}"
+
+    # -- numerical correctness of the upcast path ----------------------
+
+    @pytest.mark.parametrize("x_dtype", [torch.bfloat16, torch.float16])
+    @pytest.mark.parametrize("has_bias", [True, False])
+    def test_numerical_correctness_with_fp32_residual(self, x_dtype, has_bias):
+        """Forward result should match a reference computed entirely in fp32."""
+        torch.manual_seed(7)
+        x = torch.randn(self.B, self.H, dtype=x_dtype, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.float32, device=self.device)
+        bias = torch.randn(self.H, dtype=x_dtype, device=self.device) if has_bias else None
+
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=True)
+
+        ref = self._reference_bias_dropout_add(x, bias, residual, prob=0.0, training=True)
+        # fp32 tolerance – the only imprecision is the upcast from bf16/fp16
+        assert torch.allclose(
+            out, ref, rtol=1e-5, atol=1e-5
+        ), f"Max diff = {(out - ref).abs().max().item()}"
+
+    # -- backward: gradients flow correctly through the upcast ---------
+
+    @pytest.mark.parametrize("x_dtype", [torch.bfloat16, torch.float16])
+    @pytest.mark.parametrize("has_bias", [True, False])
+    def test_backward_with_fp32_residual(self, x_dtype, has_bias):
+        """Gradients should be computed for x and residual when dtypes differ."""
+        x = torch.randn(self.B, self.H, dtype=x_dtype, device=self.device, requires_grad=True)
+        residual = torch.randn(
+            self.B, self.H, dtype=torch.float32, device=self.device, requires_grad=True
+        )
+        bias = (
+            torch.randn(self.H, dtype=x_dtype, device=self.device, requires_grad=True)
+            if has_bias
+            else None
+        )
+
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=True)
+        grad_out = torch.randn_like(out)
+        out.backward(grad_out)
+
+        assert x.grad is not None, "x.grad must not be None"
+        assert residual.grad is not None, "residual.grad must not be None"
+        assert x.grad.dtype == x_dtype, f"x.grad dtype should be {x_dtype}"
+        assert residual.grad.dtype == torch.float32, "residual.grad must stay fp32"
+        if has_bias:
+            assert bias.grad is not None, "bias.grad must not be None"
+
+    # -- same dtype: no regression (both fp32 or both bf16) ------------
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+    @pytest.mark.parametrize("has_bias", [True, False])
+    def test_same_dtype_no_regression(self, dtype, has_bias):
+        """When x and residual share the same dtype, behaviour is unchanged."""
+        torch.manual_seed(99)
+        x = torch.randn(self.B, self.H, dtype=dtype, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=dtype, device=self.device)
+        bias = torch.randn(self.H, dtype=dtype, device=self.device) if has_bias else None
+
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=True)
+
+        assert out.dtype == dtype
+        ref = self._reference_bias_dropout_add(x, bias, residual, prob=0.0, training=True)
+        ref = ref.to(dtype)
+        tols = dict(rtol=1e-5, atol=1e-5) if dtype == torch.float32 else dict(rtol=2e-2, atol=1e-2)
+        assert torch.allclose(out, ref, **tols)
+
+    # -- inference in-place optimisation still fires for same dtype ----
+
+    def test_inplace_inference_same_dtype(self):
+        """In eval mode with no grad, the in-place path should still work."""
+        x = torch.randn(self.B, self.H, dtype=torch.bfloat16, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.bfloat16, device=self.device)
+        bias = torch.randn(self.H, dtype=torch.bfloat16, device=self.device)
+
+        x_ptr = x.data_ptr()
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=False)
+        # In-place: output should reuse x's storage
+        assert out.data_ptr() == x_ptr
+
+    # -- inference with mixed dtypes should NOT be in-place ------------
+
+    @pytest.mark.parametrize("x_dtype", [torch.bfloat16, torch.float16])
+    def test_no_inplace_when_dtypes_differ(self, x_dtype):
+        """When x is low-precision but residual is fp32, the upcast creates
+        a new tensor so the in-place optimisation must NOT fire."""
+        x = torch.randn(self.B, self.H, dtype=x_dtype, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.float32, device=self.device)
+
+        x_ptr = x.data_ptr()
+        out = _bias_dropout_add_func((x, None), residual, prob=0.0, training=False)
+        # The upcast `x = x.to(residual.dtype)` creates a new tensor,
+        # so the output must NOT alias the original x buffer.
+        assert out.dtype == torch.float32
+        assert out.data_ptr() != x_ptr
+
+    # -- dropout prob > 0 still works with mixed dtypes ----------------
+
+    @pytest.mark.parametrize("has_bias", [True, False])
+    def test_dropout_with_fp32_residual(self, has_bias):
+        """Smoke test: non-zero dropout with mixed dtypes doesn't crash."""
+        x = torch.randn(self.B, self.H, dtype=torch.bfloat16, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.float32, device=self.device)
+        bias = torch.randn(self.H, dtype=torch.bfloat16, device=self.device) if has_bias else None
+
+        out = _bias_dropout_add_func((x, bias), residual, prob=0.5, training=True)
+        assert out.dtype == torch.float32
+        # With dropout, some elements should be zeroed (before residual add)
+        # so the output shouldn't be identical to the no-dropout case.
+        out_no_drop = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=True)
+        # They *can* be equal with very low probability; just check dtypes
+        assert out_no_drop.dtype == torch.float32
+
+    # -- get_bias_dropout_add wrappers preserve fp32 residual ----------
+
+    @pytest.mark.parametrize("training", [True, False])
+    @pytest.mark.parametrize("fused", [True, False])
+    def test_get_bias_dropout_add_fp32_residual(self, training, fused):
+        """All four (training × fused) wrappers returned by get_bias_dropout_add
+        should preserve fp32 residual dtype."""
+        fn = get_bias_dropout_add(training=training, fused=fused)
+        x = torch.randn(self.B, self.H, dtype=torch.bfloat16, device=self.device)
+        residual = torch.randn(self.B, self.H, dtype=torch.float32, device=self.device)
+        bias = torch.randn(self.H, dtype=torch.bfloat16, device=self.device)
+
+        out = fn((x, bias), residual, prob=0.0)
+        assert out.dtype == torch.float32, (
+            f"get_bias_dropout_add(training={training}, fused={fused}) "
+            f"returned {out.dtype}, expected fp32"
+        )
+
+
+# ============================================================================
+# Tests simulating the multi-layer residual stream scenario
+# ============================================================================
+
+
+class TestFp32ResidualStreamAcrossLayers:
+    """Simulates what happens across multiple transformer layers to ensure
+    the fp32 residual stream is not degraded."""
+
+    device = "cuda"
+
+    def test_residual_stays_fp32_across_simulated_layers(self):
+        """Simulate N layers of bias-dropout-add with bf16 x and fp32 residual.
+        The residual should remain fp32 throughout — this is the scenario that
+        was broken before the fix."""
+        B, H = 4, 32
+        num_layers = 8
+
+        # Start with an fp32 residual (as the embedding layer would emit)
+        residual = torch.randn(B, H, dtype=torch.float32, device=self.device)
+
+        for layer_idx in range(num_layers):
+            # Each layer produces bf16 output (as the attention/MLP would)
+            x = torch.randn(B, H, dtype=torch.bfloat16, device=self.device)
+            bias = torch.randn(H, dtype=torch.bfloat16, device=self.device)
+
+            residual = _bias_dropout_add_func((x, bias), residual, prob=0.0, training=True)
+
+            assert (
+                residual.dtype == torch.float32
+            ), f"Layer {layer_idx}: residual dtype degraded to {residual.dtype}"
+
+    def test_residual_stays_fp32_no_bias(self):
+        """Same multi-layer simulation but without bias tensors."""
+        B, H = 4, 32
+        num_layers = 8
+
+        residual = torch.randn(B, H, dtype=torch.float32, device=self.device)
+
+        for layer_idx in range(num_layers):
+            x = torch.randn(B, H, dtype=torch.bfloat16, device=self.device)
+
+            residual = _bias_dropout_add_func((x, None), residual, prob=0.0, training=True)
+
+            assert (
+                residual.dtype == torch.float32
+            ), f"Layer {layer_idx}: residual dtype degraded to {residual.dtype}"
+
+    def test_fp32_residual_precision_advantage(self):
+        """Demonstrate that fp32 residuals accumulate more accurately than
+        bf16 residuals over many additions — the whole point of the feature."""
+        B, H = 2, 16
+        num_layers = 50
+        torch.manual_seed(42)
+
+        # Ground truth: everything in fp64
+        residual_fp64 = torch.randn(B, H, dtype=torch.float64, device=self.device)
+
+        # Track fp32 and bf16 residual streams
+        residual_fp32 = residual_fp64.float()
+        residual_bf16 = residual_fp64.to(torch.bfloat16)
+
+        for _ in range(num_layers):
+            x_fp64 = torch.randn(B, H, dtype=torch.float64, device=self.device)
+            x_bf16 = x_fp64.to(torch.bfloat16)
+
+            # fp64 reference
+            residual_fp64 = residual_fp64 + x_fp64
+
+            # fp32 residual path (the fix)
+            residual_fp32 = _bias_dropout_add_func(
+                (x_bf16, None), residual_fp32, prob=0.0, training=True
+            )
+
+            # bf16 residual path (the old broken behaviour)
+            residual_bf16 = _bias_dropout_add_func(
+                (x_bf16, None), residual_bf16.to(torch.bfloat16), prob=0.0, training=True
+            )
+
+        err_fp32 = (residual_fp32.double() - residual_fp64).abs().mean().item()
+        err_bf16 = (residual_bf16.double() - residual_fp64).abs().mean().item()
+
+        # fp32 residual should be meaningfully more precise
+        assert err_fp32 < err_bf16, (
+            f"fp32 residual error ({err_fp32:.6e}) should be less than "
+            f"bf16 residual error ({err_bf16:.6e})"
+        )
diff --git a/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py b/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py
new file mode 100644
index 00000000000..dd34061888e
--- /dev/null
+++ b/tests/unit_tests/inference/contexts/attention_metadata/test_mamba_metadata.py
@@ -0,0 +1,428 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import pytest
+import torch
+
+from megatron.core.inference.batch_dimensions_utils import InferenceBatchDimensions
+from megatron.core.inference.contexts.attention_context.mamba_metadata import MambaMetadata
+
+
+class TestMambaMetadata:
+
+    @pytest.fixture
+    def metadata_context(self):
+        """Fixture to initialize MambaMetadata with standard constraints."""
+        max_requests = 16
+        max_tokens = 2048
+        metadata = MambaMetadata(max_requests=max_requests, max_tokens=max_tokens)
+
+        # Manually allocate some slots to simulate a running state.
+        # We assume request_id i maps to mamba_slot i for simplicity in assertions.
+        for i in range(max_requests):
+            metadata.request_to_mamba_state_idx[i] = i
+
+        yield metadata
+        metadata.reset()
+
+    def _run_update_test(
+        self,
+        metadata: MambaMetadata,
+        req_seq_lengths: list[int],
+        num_decode_requests: int,
+        padded_dims: InferenceBatchDimensions,
+        enable_chunked_prefill: bool,
+    ):
+        """
+        Helper to construct inputs and run update().
+
+        Args:
+            metadata: The MambaMetadata instance.
+            req_seq_lengths: List of sequence lengths for all active requests.
+                             Order must be [decode_requests..., prefill_requests...].
+            num_decode_requests: Number of requests in req_seq_lengths that are in the decode phase.
+            padded_dims: The padded batch dimensions to test against.
+            enable_chunked_prefill: Whether chunked prefill is enabled.
+        """
+        num_active_requests = len(req_seq_lengths)
+        total_tokens = sum(req_seq_lengths)
+        num_prefill_requests = num_active_requests - num_decode_requests
+
+        real_dims = InferenceBatchDimensions(
+            token_count=total_tokens,
+            prefill_req_count=num_prefill_requests,
+            decode_req_count=num_decode_requests,
+        )
+
+        # Assuming 1:1 mapping (req_id i -> slot i)
+        active_mamba_indices = torch.arange(
+            num_active_requests, dtype=torch.int32, device=metadata.device
+        )
+
+        cu_seqlens = [0]
+        current_len = 0
+        for l in req_seq_lengths:
+            current_len += l
+            cu_seqlens.append(current_len)
+        cu_seqlens_tensor = torch.tensor(cu_seqlens, dtype=torch.int32, device=metadata.device)
+
+        token_to_req = []
+        for req_idx, length in enumerate(req_seq_lengths):
+            token_to_req.extend([req_idx] * length)
+        token_to_req_tensor = torch.tensor(token_to_req, dtype=torch.int32, device=metadata.device)
+
+        metadata.update(
+            active_mamba_indices=active_mamba_indices,
+            token_to_request_idx=token_to_req_tensor,
+            cu_seqlens=cu_seqlens_tensor,
+            batch_dimensions=real_dims,
+            padded_batch_dimensions=padded_dims,
+            enable_chunked_prefill=enable_chunked_prefill,
+        )
+
+        return real_dims, active_mamba_indices
+
+    # -------------------------------------------------------------------------
+    # Scenario 1: Decode Only
+    # -------------------------------------------------------------------------
+
+    @pytest.mark.internal
+    def test_update_decode_only_exact_match(self, metadata_context):
+        """Test simple decode only case where real dims match padded dims."""
+        seq_lengths = [1, 1, 1, 1]  # 4 requests
+        num_decode = 4
+        padded_dims = InferenceBatchDimensions(
+            token_count=4, prefill_req_count=0, decode_req_count=4
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_decode = torch.arange(4, dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+        assert metadata_context.batch_indices_prefill is None
+        assert metadata_context.batch_indices_chunked_prefill is None
+        assert metadata_context.device_decode_prefill is None
+        assert metadata_context.cu_seqlens is None
+        assert metadata_context.seq_idx is None
+
+    @pytest.mark.internal
+    def test_update_decode_only_padded(self, metadata_context):
+        """Test decode only with padding (e.g. using CUDA graphs bucket)."""
+        seq_lengths = [1, 1]  # 2 requests
+        num_decode = 2
+        # Padding to 4 requests
+        padded_dims = InferenceBatchDimensions(
+            token_count=4, prefill_req_count=0, decode_req_count=4
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_decode = torch.tensor(
+            [0, 1, -1, -1], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+        assert metadata_context.batch_indices_prefill is None
+        assert metadata_context.batch_indices_chunked_prefill is None
+        assert metadata_context.device_decode_prefill is None
+
+    @pytest.mark.internal
+    def test_update_chunked_enabled_no_prefill_reqs(self, metadata_context):
+        """Test edge case: Chunked prefill enabled, but only decode requests exist."""
+        seq_lengths = [1, 1]
+        num_decode = 2
+        padded_dims = InferenceBatchDimensions(
+            token_count=2, prefill_req_count=0, decode_req_count=2
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=True
+        )
+
+        # Should behave exactly like decode-only (chunked logic skipped if real_prefill == 0)
+        expected_decode = torch.tensor([0, 1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+        assert metadata_context.batch_indices_chunked_prefill is None
+        assert metadata_context.batch_indices_prefill is None
+        assert metadata_context.cu_seqlens is None
+        assert metadata_context.seq_idx is None
+
+    # -------------------------------------------------------------------------
+    # Scenario 2: Prefill Only
+    # -------------------------------------------------------------------------
+
+    @pytest.mark.internal
+    def test_update_prefill_only_exact(self, metadata_context):
+        """Test prefill only scenario (exact match)."""
+        seq_lengths = [10, 20]  # 2 requests
+        num_decode = 0
+        padded_dims = InferenceBatchDimensions(
+            token_count=30, prefill_req_count=2, decode_req_count=0
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_prefill = torch.tensor([0, 1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_cu_seqlens = torch.tensor(
+            [0, 10, 30], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu_seqlens)
+
+        expected_seq_idx_0 = torch.zeros((1, 10), dtype=torch.int32, device=metadata_context.device)
+        expected_seq_idx_1 = torch.ones((1, 20), dtype=torch.int32, device=metadata_context.device)
+        expected_seq_idx = torch.cat([expected_seq_idx_0, expected_seq_idx_1], dim=1)
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+        assert metadata_context.batch_indices_decode is None
+        assert metadata_context.batch_indices_chunked_prefill is None
+        assert metadata_context.device_decode_prefill is None
+
+    @pytest.mark.internal
+    def test_update_prefill_only_padded(self, metadata_context):
+        """Test prefill only with padding."""
+        seq_lengths = [10]  # 1 request
+        num_decode = 0
+        # Pad to 3 prefill requests
+        padded_dims = InferenceBatchDimensions(
+            token_count=30, prefill_req_count=3, decode_req_count=0
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_prefill = torch.tensor(
+            [0, -1, -1], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_cu_seqlens = torch.tensor(
+            [0, 10, 10, 10], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu_seqlens)
+
+        expected_seq_idx = torch.full(
+            (1, 30), -1, dtype=torch.int32, device=metadata_context.device
+        )
+        expected_seq_idx[:, :10] = 0
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+        assert metadata_context.batch_indices_decode is None
+        assert metadata_context.batch_indices_chunked_prefill is None
+        assert metadata_context.device_decode_prefill is None
+
+    # -------------------------------------------------------------------------
+    # Scenario 3: Mixed Batch (Decode + Prefill)
+    # -------------------------------------------------------------------------
+
+    @pytest.mark.internal
+    def test_update_mixed_batch_exact(self, metadata_context):
+        """Test mix of decode and prefill requests (exact match)."""
+        # 2 decode (len 1), 2 prefill (len 10, 20)
+        seq_lengths = [1, 1, 10, 20]
+        num_decode = 2
+        padded_dims = InferenceBatchDimensions(
+            token_count=32, prefill_req_count=2, decode_req_count=2
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_decode = torch.tensor([0, 1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
+        expected_prefill = torch.tensor([2, 3], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_device_counts = torch.tensor(
+            [2, 2], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
+
+        expected_cu_seqlens = torch.tensor(
+            [0, 10, 30], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu_seqlens)
+
+        expected_seq_idx_0 = torch.zeros((1, 10), dtype=torch.int32, device=metadata_context.device)
+        expected_seq_idx_1 = torch.ones((1, 20), dtype=torch.int32, device=metadata_context.device)
+        expected_seq_idx_padding = torch.full(
+            (1, 2), -1, dtype=torch.int32, device=metadata_context.device
+        )
+        expected_seq_idx = torch.cat(
+            [expected_seq_idx_0, expected_seq_idx_1, expected_seq_idx_padding], dim=1
+        )
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+    @pytest.mark.internal
+    def test_update_padded_prefill_and_decode(self, metadata_context):
+        """Test scenario where padded dimensions differ from real dimensions (Mixed)."""
+        # Real: 1 decode, 1 prefill.
+        seq_lengths = [1, 10]
+        num_decode = 1
+
+        # Padded: 4 decode, 4 prefill. Total tokens 32.
+        padded_dims = InferenceBatchDimensions(
+            token_count=32, prefill_req_count=4, decode_req_count=4
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=False
+        )
+
+        expected_decode = torch.tensor(
+            [0, -1, -1, -1], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
+        expected_prefill = torch.tensor(
+            [1, -1, -1, -1], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_device_counts = torch.tensor(
+            [1, 1], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
+
+        expected_cu = torch.tensor(
+            [0, 10, 10, 10, 10], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu)
+
+        expected_seq_idx = torch.full(
+            (1, 32), -1, dtype=torch.int32, device=metadata_context.device
+        )
+        expected_seq_idx[:, :10] = 0
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+    # -------------------------------------------------------------------------
+    # Scenario 4: Chunked Prefill
+    # -------------------------------------------------------------------------
+
+    @pytest.mark.internal
+    def test_update_chunked_prefill_mixed_exact(self, metadata_context):
+        """Test chunked prefill mixed with decode (Exact match)."""
+        # 1 decode, 1 chunked prefill (len 50), 1 regular prefill (len 10)
+        seq_lengths = [1, 50, 10]
+        num_decode = 1
+
+        # Exact dimensions
+        padded_dims = InferenceBatchDimensions(
+            token_count=61, prefill_req_count=2, decode_req_count=1
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=True
+        )
+
+        expected_device_chunked_prefill = torch.tensor(
+            [50, 10], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_chunked_prefill, expected_device_chunked_prefill)
+
+        assert metadata_context.batch_indices_chunked_prefill[0] == 1
+
+        expected_prefill = torch.tensor([2, -1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_device_counts = torch.tensor(
+            [1, 2], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
+
+        expected_cu_seqlens = torch.tensor(
+            [0, 10, 10], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu_seqlens)
+
+        expected_seq_idx = torch.zeros((1, 61), dtype=torch.int32, device=metadata_context.device)
+        expected_seq_idx[:, 10:] = -1
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+    @pytest.mark.internal
+    def test_update_chunked_prefill_mixed_padded(self, metadata_context):
+        """Test chunked prefill mixed with decode (Padded)."""
+        # 2 decode, 1 chunked prefill (len 50), 1 regular prefill (len 10)
+        seq_lengths = [1, 1, 50, 10]
+        num_decode = 2
+        padded_dims = InferenceBatchDimensions(
+            token_count=62, prefill_req_count=2, decode_req_count=2
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=True
+        )
+
+        expected_decode = torch.tensor([0, 1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_decode, expected_decode)
+
+        expected_device_chunked_prefill = torch.tensor(
+            [50, 10], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_chunked_prefill, expected_device_chunked_prefill)
+
+        assert metadata_context.batch_indices_chunked_prefill[0] == 2
+
+        expected_prefill = torch.tensor([3, -1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_device_counts = torch.tensor(
+            [2, 2], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_decode_prefill, expected_device_counts)
+
+        expected_cu = torch.tensor([0, 10, 10], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu)
+
+        expected_seq_idx = torch.full(
+            (1, 62), -1, dtype=torch.int32, device=metadata_context.device
+        )
+        expected_seq_idx[:, :10] = 0
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+    @pytest.mark.internal
+    def test_update_chunked_only_padded(self, metadata_context):
+        """Test a case with only chunked prefill (no decode, no regular prefill) but with padding."""
+        # 1 chunked prefill request.
+        seq_lengths = [100]
+        num_decode = 0
+
+        padded_dims = InferenceBatchDimensions(
+            token_count=128, prefill_req_count=2, decode_req_count=0
+        )
+
+        self._run_update_test(
+            metadata_context, seq_lengths, num_decode, padded_dims, enable_chunked_prefill=True
+        )
+
+        assert metadata_context.batch_indices_decode is None
+
+        assert metadata_context.batch_indices_chunked_prefill[0] == 0
+
+        expected_prefill = torch.tensor([-1, -1], dtype=torch.int32, device=metadata_context.device)
+        assert torch.equal(metadata_context.batch_indices_prefill, expected_prefill)
+
+        expected_cu_seqlens = torch.tensor(
+            [0, 0, 0], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.cu_seqlens, expected_cu_seqlens)
+
+        expected_seq_idx = torch.full(
+            (1, 128), -1, dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.seq_idx, expected_seq_idx)
+
+        expected_device_chunked_prefill = torch.tensor(
+            [100, 0], dtype=torch.int32, device=metadata_context.device
+        )
+        assert torch.equal(metadata_context.device_chunked_prefill, expected_device_chunked_prefill)
+
+        assert metadata_context.device_decode_prefill is None
diff --git a/tests/unit_tests/inference/contexts/test_dynamic_context.py b/tests/unit_tests/inference/contexts/test_dynamic_context.py
index 05e0306bfd8..67d62c7e4fa 100644
--- a/tests/unit_tests/inference/contexts/test_dynamic_context.py
+++ b/tests/unit_tests/inference/contexts/test_dynamic_context.py
@@ -1,14 +1,13 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
+import contextlib
 import math
 
 import pytest
 import torch
 
 from megatron.core import parallel_state
-from megatron.core.inference.contexts.attention_context.mamba_metadata import (
-    MambaInferenceStateConfig,
-)
+from megatron.core.inference.config import InferenceConfig, MambaInferenceStateConfig
 from megatron.core.inference.contexts.dynamic_context import (
     DynamicInferenceContext,
     RequestOverflowError,
@@ -18,14 +17,21 @@
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.transformer_config import TransformerConfig
 from tests.unit_tests.test_utilities import Utils
 
 
-def set_rounder(value):
-    """Utility function to set the DynamicInferenceContext rounder."""
-    DynamicInferenceContext.ROUNDER = value  # For backwards compatibility
-    DynamicInferenceContext.TOKEN_ROUNDER = value
-    DynamicInferenceContext.REQUEST_ROUNDER = value
+@contextlib.contextmanager
+def rounder_override(n):
+    original_token_rounder = DynamicInferenceContext.TOKEN_ROUNDER
+    original_request_rounder = DynamicInferenceContext.REQUEST_ROUNDER
+    try:
+        DynamicInferenceContext.TOKEN_ROUNDER = n
+        DynamicInferenceContext.REQUEST_ROUNDER = n
+        yield
+    finally:
+        DynamicInferenceContext.TOKEN_ROUNDER = original_token_rounder
+        DynamicInferenceContext.REQUEST_ROUNDER = original_request_rounder
 
 
 class TestDynamicContext:
@@ -52,11 +58,8 @@ def _get_dynamic_context(
         max_tokens,
         is_hybrid_model=False,
         layer_type_list=None,
-        rounder=64,
         paused_buffer_size_gb=None,
     ):
-        set_rounder(rounder)
-
         if is_hybrid_model:
             if layer_type_list is None:
                 layer_type_list = [Symbols.MAMBA, Symbols.MLP, Symbols.ATTENTION, Symbols.MLP]
@@ -69,23 +72,27 @@ def _get_dynamic_context(
             mamba_inference_state_config = None
 
         dynamic_context = DynamicInferenceContext(
-            params_dtype=params_dtype,
-            num_layers=num_layers // self.pp_size,
-            kv_channels=kv_channels,
-            num_attention_heads=num_attention_heads,
-            max_sequence_length=max_sequence_length,
-            num_cuda_graphs=None,
-            use_cuda_graphs_for_non_decode_steps=True,
-            buffer_size_gb=buffer_size_gb,
-            paused_buffer_size_gb=(
-                0.2 * buffer_size_gb if paused_buffer_size_gb is None else paused_buffer_size_gb
+            model_config=TransformerConfig(
+                params_dtype=params_dtype,
+                num_layers=num_layers,
+                kv_channels=kv_channels,
+                num_attention_heads=num_attention_heads,
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=max_sequence_length,
+                num_cuda_graphs=None,
+                use_cuda_graphs_for_non_decode_steps=True,
+                buffer_size_gb=buffer_size_gb,
+                paused_buffer_size_gb=(
+                    0.2 * buffer_size_gb if paused_buffer_size_gb is None else paused_buffer_size_gb
+                ),
+                block_size_tokens=block_size_tokens,
+                max_tokens=max_tokens,
+                mamba_inference_state_config=mamba_inference_state_config,
+                use_flashinfer_fused_rope=None,  # default to using flash-infer if available
+                # this is for compatibility with the LTS environment
+                unified_memory_level=0,  # unit tests currently broken with UVM
             ),
-            block_size_tokens=block_size_tokens,
-            max_tokens=max_tokens,
-            mamba_inference_state_config=mamba_inference_state_config,
-            use_flashinfer_fused_rope=None,  # default to using flash-infer if available
-            # this is for compatibility with the LTS environment
-            unified_memory_level=0,  # unit tests currently broken with UVM
         )
         return dynamic_context
 
@@ -93,6 +100,7 @@ def teardown_method(self, method):
         Utils.destroy_model_parallel()
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_initialize_dynamic_context(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -107,7 +115,6 @@ def test_initialize_dynamic_context(self, is_hybrid_model: bool):
             block_size_tokens=128,
             max_tokens=None,
             is_hybrid_model=is_hybrid_model,
-            rounder=64,
         )
 
         if not is_hybrid_model:
@@ -145,6 +152,7 @@ def test_is_static_batching(self):
         assert not dynamic_context.is_static_batching()
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_is_memory_available(self, is_hybrid_model):
         self._setup_model_parallel_group(1, 1)
@@ -168,6 +176,7 @@ def test_is_memory_available(self, is_hybrid_model):
         assert not dynamic_context.block_allocator.is_memory_available(1)
 
     @pytest.mark.internal
+    @rounder_override(1)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_request_overflow(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -181,7 +190,6 @@ def test_request_overflow(self, is_hybrid_model: bool):
             buffer_size_gb=0.01,
             block_size_tokens=32,
             max_tokens=None,
-            rounder=1,
             is_hybrid_model=is_hybrid_model,
         )
         dynamic_context.max_requests //= 2
@@ -198,6 +206,7 @@ def test_request_overflow(self, is_hybrid_model: bool):
                 )  # Adding more than allowed requests
 
     @pytest.mark.internal
+    @rounder_override(1)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_token_overflow_error(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -211,7 +220,6 @@ def test_token_overflow_error(self, is_hybrid_model: bool):
             buffer_size_gb=0.1,
             block_size_tokens=128,
             max_tokens=200,  # setting low, but >= context.max_requests.
-            rounder=1,
             is_hybrid_model=is_hybrid_model,
         )
 
@@ -227,6 +235,7 @@ def test_token_overflow_error(self, is_hybrid_model: bool):
             )  # Exceeding max token count
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_reset(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -301,6 +310,7 @@ def test_reset(self, is_hybrid_model: bool):
             assert torch.all(dynamic_context.mamba_metadata.request_to_mamba_state_idx == -1)
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_allocate_and_release_memory_blocks(self, is_hybrid_model):
         self._setup_model_parallel_group(1, 1)
@@ -349,6 +359,7 @@ def test_allocate_and_release_memory_blocks(self, is_hybrid_model):
         )
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_add_request(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -422,6 +433,7 @@ def test_add_request(self, is_hybrid_model: bool):
         )
 
     @pytest.mark.internal
+    @rounder_override(64)
     def test_add_dummy_requests_parallel_populates_state(self):
         self._setup_model_parallel_group(1, 1)
 
@@ -520,6 +532,7 @@ def test_add_dummy_requests_parallel_populates_state(self):
         )
 
     @pytest.mark.internal
+    @rounder_override(64)
     def test_add_dummy_requests_parallel_hybrid_allocates_mamba(self):
         self._setup_model_parallel_group(1, 1)
 
@@ -550,6 +563,7 @@ def test_add_dummy_requests_parallel_hybrid_allocates_mamba(self):
         assert torch.all(dynamic_context.mamba_ssm_states[:, mamba_idx] == 0)
 
     @pytest.mark.internal
+    @rounder_override(64)
     def test_add_dummy_requests_parallel_decode_does_not_count_as_prefill(self):
         self._setup_model_parallel_group(1, 1)
 
@@ -575,6 +589,7 @@ def test_add_dummy_requests_parallel_decode_does_not_count_as_prefill(self):
         assert dynamic_context.num_prefill_requests == 0
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_update_request(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -774,6 +789,7 @@ def test_update_request(self, is_hybrid_model: bool):
             )
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_release_memory_blocks_for_finished_requests(self, is_hybrid_model):
         """Test that memory blocks are correctly released for finished requests."""
@@ -846,6 +862,7 @@ def test_release_memory_blocks_for_finished_requests(self, is_hybrid_model):
             assert mamba_idx[4] == -1
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_finished_requests_with_multiple_blocks(self, is_hybrid_model):
         """Test that all memory blocks are correctly released for finished requests that use multiple blocks."""
@@ -913,6 +930,7 @@ def test_finished_requests_with_multiple_blocks(self, is_hybrid_model):
         assert dynamic_context.block_allocator.total_avail == initial_available_blocks + 6
 
     @pytest.mark.internal
+    @rounder_override(64)
     @pytest.mark.parametrize("is_hybrid_model", [False, True])
     def test_mamba_states_cache(self, is_hybrid_model: bool):
         self._setup_model_parallel_group(1, 1)
@@ -988,6 +1006,7 @@ def test_mamba_states_cache(self, is_hybrid_model: bool):
         assert torch.all(ssm_state_layer3 == 40.0)
 
     @pytest.mark.internal
+    @rounder_override(64)
     def test_calculate_and_store_log_probs(self):
         self._setup_model_parallel_group(1, 1)
         dynamic_context = self._get_dynamic_context(
@@ -1205,6 +1224,7 @@ def test_calculate_and_store_log_probs(self):
                 current_global_token_offset += expected_len
 
     @pytest.mark.internal
+    @rounder_override(64)
     def test_pipeline_parallel_uneven_layers(self):
         """
         Test that DynamicInferenceContext synchronizes the total block count across
@@ -1215,23 +1235,39 @@ def test_pipeline_parallel_uneven_layers(self):
 
         rank = parallel_state.get_pipeline_model_parallel_rank()
 
+        mamba_conv_states_shape = (544, 4)
+        mamba_ssm_states_shape = (8, 64, 16)
+
         if rank == 0:
-            local_num_layers = 12
+            mamba_inference_state_config = MambaInferenceStateConfig(
+                [Symbols.MAMBA] + [Symbols.ATTENTION] * 4,
+                mamba_conv_states_shape,
+                mamba_ssm_states_shape,
+            )
         else:
-            local_num_layers = 4
+            mamba_inference_state_config = MambaInferenceStateConfig(
+                [Symbols.MAMBA] * 4 + [Symbols.ATTENTION],
+                mamba_conv_states_shape,
+                mamba_ssm_states_shape,
+            )
 
         context = DynamicInferenceContext(
-            params_dtype=torch.float32,
-            num_layers=local_num_layers,
-            kv_channels=64,
-            num_attention_heads=8,
-            max_sequence_length=128,
-            buffer_size_gb=0.1,
-            block_size_tokens=16,
-            max_tokens=1024,
-            pipeline_model_parallel_size=pp_size,
-            tensor_model_parallel_size=1,
-            unified_memory_level=0,
+            model_config=TransformerConfig(
+                params_dtype=torch.float32,
+                num_layers=10,
+                kv_channels=64,
+                num_attention_heads=8,
+                pipeline_model_parallel_size=pp_size,
+                tensor_model_parallel_size=1,
+                pipeline_dtype=torch.float32,
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=128,
+                buffer_size_gb=0.1,
+                block_size_tokens=16,
+                max_tokens=1024,
+                unified_memory_level=0,
+            ),
         )
 
         # Collect the total block counts on each rank
@@ -1251,3 +1287,101 @@ def test_pipeline_parallel_uneven_layers(self):
         assert (
             len(unique_counts) == 1
         ), f"Block counts were not synchronized across ranks. Gathered: {all_counts}"
+
+    @pytest.mark.internal
+    @pytest.mark.parametrize("ratio", [0.2, 0.4, 0.6, 0.8])
+    @rounder_override(64)
+    def test_mamba_memory_ratio_allocation(self, ratio):
+        """
+        Test that max_requests and block counts are partitioned correctly by mamba_memory_ratio.
+        """
+        self._setup_model_parallel_group(1, 1)
+
+        buffer_gb = 0.05
+        paused_gb = 0.01
+        block_size = 256
+        num_attention_heads = 8
+        kv_channels = 64
+        params_dtype = torch.float32
+
+        layer_type_list = [Symbols.MAMBA, Symbols.ATTENTION]
+        mamba_conv_states_shape = (544, 4)
+        mamba_ssm_states_shape = (8, 64, 16)
+        mamba_config = MambaInferenceStateConfig(
+            layer_type_list, mamba_conv_states_shape, mamba_ssm_states_shape
+        )
+
+        context = DynamicInferenceContext(
+            model_config=TransformerConfig(
+                params_dtype=params_dtype,
+                num_layers=2,  # 1 Attn, 1 Mamba
+                kv_channels=kv_channels,
+                num_attention_heads=num_attention_heads,
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=512,
+                buffer_size_gb=buffer_gb,
+                paused_buffer_size_gb=paused_gb,
+                block_size_tokens=block_size,
+                max_tokens=2048,
+                mamba_inference_state_config=mamba_config,
+                mamba_memory_ratio=ratio,
+                unified_memory_level=0,
+            ),
+        )
+
+        dtype_size = torch.tensor([], dtype=params_dtype).element_size()
+
+        mamba_mem_per_req = math.prod(mamba_conv_states_shape) + math.prod(mamba_ssm_states_shape)
+        mamba_mem_per_req *= dtype_size
+
+        kv_buffer_bytes = int(buffer_gb * 1024**3)
+        kv_paused_bytes = int(paused_gb * 1024**3)
+        total_mem_bytes = kv_buffer_bytes + kv_paused_bytes
+        expected_mamba_mem = total_mem_bytes * ratio
+        expected_mamba_max_reqs = int(expected_mamba_mem // mamba_mem_per_req)
+
+        # KV block calculation with buffer size reduced by Mamba memory ratio
+        kv_buffer_bytes = int(kv_buffer_bytes * (1.0 - ratio))
+        kv_paused_bytes = int(kv_paused_bytes * (1.0 - ratio))
+
+        kv_block_size_bytes = dtype_size * 2 * 1 * block_size * num_attention_heads * kv_channels
+
+        expected_active_blocks = kv_buffer_bytes // kv_block_size_bytes
+        expected_paused_blocks = kv_paused_bytes // kv_block_size_bytes
+        expected_total_blocks = expected_active_blocks + expected_paused_blocks
+
+        # Check that block allocator received the reduced block counts
+        assert context.block_allocator.total_count == expected_active_blocks
+        assert context.block_allocator.paused_count == expected_paused_blocks
+
+        # max_requests should be limited by the Mamba calculation if mamba_max_requests is smaller
+        # or the block count - 1 if that is smaller
+        expected_limit = min(expected_total_blocks - 1, expected_mamba_max_reqs)
+
+        # Apply rounding (REQUEST_ROUNDER = 64 in this test)
+        expected_max_requests = (expected_limit // 64) * 64
+
+        assert context.max_requests == expected_max_requests
+        assert context.is_hybrid_model is True
+
+    @pytest.mark.internal
+    @rounder_override(64)
+    def test_max_requests_less_than_tp_size(self):
+        tp_size = 2
+        self._setup_model_parallel_group(tensor_parallel_size=tp_size, pipeline_parallel_size=1)
+
+        model_config = TransformerConfig(
+            params_dtype=torch.float32,
+            num_layers=2,
+            kv_channels=64,
+            num_attention_heads=8,
+            tensor_model_parallel_size=tp_size,
+        )
+
+        inference_config = InferenceConfig(
+            max_sequence_length=512, buffer_size_gb=0.1, block_size_tokens=128, max_requests=1
+        )
+
+        with pytest.raises(AssertionError):
+            DynamicInferenceContext(model_config=model_config, inference_config=inference_config)
diff --git a/tests/unit_tests/inference/engines/test_dynamic_engine.py b/tests/unit_tests/inference/engines/test_dynamic_engine.py
index d5803b3638e..e679b5d7c64 100644
--- a/tests/unit_tests/inference/engines/test_dynamic_engine.py
+++ b/tests/unit_tests/inference/engines/test_dynamic_engine.py
@@ -1,10 +1,12 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import asyncio
+import gc
 import math
 import random
 import types
 from dataclasses import dataclass, field
+from functools import partial
 from typing import Dict, List, Optional, Tuple
 
 import pytest
@@ -13,7 +15,9 @@
 from transformer_engine.pytorch.fp8 import check_fp8_support
 
 from megatron.core import parallel_state
-from megatron.core.inference.contexts.attention_context.mamba_metadata import (
+from megatron.core.inference.config import (
+    InferenceConfig,
+    KVCacheManagementMode,
     MambaInferenceStateConfig,
 )
 from megatron.core.inference.contexts.dynamic_context import (
@@ -28,9 +32,6 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
@@ -48,13 +49,16 @@
 from megatron.core.transformer.cuda_graphs import CudaGraphManager, _CudagraphGlobalRecord
 from megatron.core.transformer.enums import CudaGraphScope
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.utils import (
-    get_mamba_inference_state_config_from_model,
-    is_fa_min_version,
-    is_te_min_version,
-)
+from megatron.core.utils import is_fa_min_version, is_te_min_version
 from tests.unit_tests.test_utilities import Utils
 
+try:
+    from torch_memory_saver import torch_memory_saver  # noqa: F401
+
+    HAVE_TORCH_MEMORY_SAVER = True
+except ImportError:
+    HAVE_TORCH_MEMORY_SAVER = False
+
 
 def skip_if_mamba_sequence_packing_not_available(model_provider: str):
     if model_provider == "mamba":
@@ -72,6 +76,17 @@ def set_rounder(value):
     DynamicInferenceContext.REQUEST_ROUNDER = value
 
 
+def mock_forward(input_ids, position_ids, attention_mask, *args, **kwargs):
+    """Mock forward function to avoid numerics issues with random inputs."""
+    return torch.randn(
+        input_ids.size(0),
+        input_ids.size(1),
+        kwargs["vocab_size"],
+        device=input_ids.device,
+        dtype=torch.bfloat16,
+    )
+
+
 @dataclass
 class DynamicEngineTestConfig:
     """Test configuration args."""
@@ -109,7 +124,7 @@ class DynamicEngineTestConfig:
     skip_prompt_log_probs: bool = False
     enable_chunked_prefill: bool = False
     cuda_graph_scope: List[CudaGraphScope] = field(
-        default_factory=lambda: [CudaGraphScope.full_iteration]
+        default_factory=lambda: [CudaGraphScope.full_iteration_inference]
     )
     force_build_cuda_graphs: bool = False
     transformer_impl: str = "local"
@@ -120,6 +135,9 @@ class DynamicEngineTestConfig:
     # relevant to the test. The tests only check if the required
     # context attributes are set correctly.
     suspend_resume_interval: Optional[int] = None
+    kv_cache_management_mode: str = "persist"
+    static_kv_memory_pointers: bool = True
+    track_generated_token_events: bool = False
 
     fp8: bool = False
 
@@ -223,26 +241,28 @@ def _build_inference_context(
 
         # Inference context.
         context = DynamicInferenceContext(
-            params_dtype=transformer_config.params_dtype,
-            num_layers=transformer_config.num_layers
-            // transformer_config.pipeline_model_parallel_size,
-            kv_channels=transformer_config.kv_channels,
-            num_attention_heads=transformer_config.num_query_groups,
-            max_sequence_length=test_config.max_sequence_length,
-            num_cuda_graphs=test_config.num_cuda_graphs,
-            use_cuda_graphs_for_non_decode_steps=True,
-            buffer_size_gb=test_config.context_buffer_size_gb,
-            paused_buffer_size_gb=test_config.context_paused_buffer_size_gb,
-            block_size_tokens=test_config.context_block_size_tokens,
-            max_requests=test_config.context_max_requests,
-            max_tokens=test_config.context_max_tokens,
-            tensor_model_parallel_size=transformer_config.tensor_model_parallel_size,
-            pipeline_model_parallel_size=transformer_config.pipeline_model_parallel_size,
-            mamba_inference_state_config=mamba_inference_state_config,
-            materialize_only_last_token_logits=test_config.materialize_only_last_token_logits,
-            use_flashinfer_fused_rope=None,  # default to using flash-infer if available
-            # this is for compatibility with the LTS environment
-            unified_memory_level=0,  # unit tests currently broken with UVM
+            model_config=transformer_config,
+            inference_config=InferenceConfig(
+                max_sequence_length=test_config.max_sequence_length,
+                num_cuda_graphs=test_config.num_cuda_graphs,
+                use_cuda_graphs_for_non_decode_steps=True,
+                buffer_size_gb=test_config.context_buffer_size_gb,
+                paused_buffer_size_gb=test_config.context_paused_buffer_size_gb,
+                block_size_tokens=test_config.context_block_size_tokens,
+                max_requests=test_config.context_max_requests,
+                max_tokens=test_config.context_max_tokens,
+                mamba_inference_state_config=mamba_inference_state_config,
+                materialize_only_last_token_logits=test_config.materialize_only_last_token_logits,
+                kv_cache_management_mode=KVCacheManagementMode(
+                    test_config.kv_cache_management_mode
+                ),
+                static_kv_memory_pointers=test_config.static_kv_memory_pointers,
+                enable_chunked_prefill=test_config.enable_chunked_prefill,
+                use_flashinfer_fused_rope=None,  # default to using flash-infer if available
+                # this is for compatibility with the LTS environment
+                unified_memory_level=0,  # unit tests currently broken with UVM
+                track_generated_token_events=test_config.track_generated_token_events,
+            ),
         )
 
         return context
@@ -382,17 +402,7 @@ def _build_test_env(cls, test_config):
 
         model.eval()
 
-        mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
-
-        # Inference config.
-        inference_config = InferenceWrapperConfig(
-            hidden_size=transformer_config.hidden_size,
-            inference_batch_times_seqlen_threshold=400,
-            fp32_residual_connection=False,
-            params_dtype=transformer_config.params_dtype,
-            fp8=transformer_config.fp8,
-            padded_vocab_size=test_config.vocab_size,
-        )
+        mamba_inference_state_config = MambaInferenceStateConfig.from_model(model)
 
         # Inference context.
         inference_context = cls._build_inference_context(
@@ -403,7 +413,7 @@ def _build_test_env(cls, test_config):
         )
 
         # Inference model wrapper.
-        inference_wrapped_model = GPTInferenceWrapper(model, inference_config, inference_context)
+        inference_wrapped_model = GPTInferenceWrapper(model, inference_context)
 
         # Note: the following is taken from AbstractModelInferenceWrapper.prep_model_for_inference().
         inference_wrapped_model.model_is_pipeline_parallel = not (
@@ -424,13 +434,7 @@ def _build_test_env(cls, test_config):
         CudaGraphManager.global_mempool = None
 
         # Inference engine.
-        engine = DynamicInferenceEngine(
-            text_generation_controller,
-            inference_context,
-            random_seed=test_config.random_seed,
-            enable_cuda_graph=transformer_config.cuda_graph_impl == "local",
-            enable_chunked_prefill=test_config.enable_chunked_prefill,
-        )
+        engine = DynamicInferenceEngine(text_generation_controller, inference_context)
 
         # Test env.
         env = DynamicEngineTestEnv(config=test_config, requests=requests, engine=engine)
@@ -539,8 +543,8 @@ def teardown_method(self, method):
         not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
     )
     @pytest.mark.parametrize("model_provider", ["gpt", "mamba"])
-    @pytest.mark.parametrize("num_cuda_graphs", [None, 1, 4])
-    @pytest.mark.parametrize("cuda_graph_scope", [[], [CudaGraphScope.full_iteration]])
+    @pytest.mark.parametrize("num_cuda_graphs", [None, 1, 4, -1])
+    @pytest.mark.parametrize("cuda_graph_scope", [[], [CudaGraphScope.full_iteration_inference]])
     def test_simple(self, model_provider, num_cuda_graphs, cuda_graph_scope) -> None:
         """Simple test that runs without errors, and validates output."""
         skip_if_mamba_sequence_packing_not_available(model_provider)
@@ -553,11 +557,24 @@ def test_simple(self, model_provider, num_cuda_graphs, cuda_graph_scope) -> None
             num_cuda_graphs=num_cuda_graphs,
             cuda_graph_scope=cuda_graph_scope,
             force_build_cuda_graphs=True,
+            context_max_requests=128,
         )
 
         # Validate max_requests, max_tokens.
         assert env.engine.context.max_tokens == DynamicInferenceContext.DEFAULT_MAX_TOKENS
 
+        if num_cuda_graphs is not None:
+            assert env.engine.context.cuda_graph_token_counts is not None
+            assert env.engine.context.cuda_graph_batch_dimensions_list
+            model = env.engine.controller.inference_wrapped_model.model
+            if cuda_graph_scope == [CudaGraphScope.full_iteration_inference]:
+                # check if cudagraph runners are created at the decoder level
+                assert model.decoder.cudagraph_manager.cudagraph_runners
+            else:
+                # check if cudagraph runners are created at the layer level
+                for layer in model.decoder.layers:
+                    assert layer.cudagraph_manager.cudagraph_runners
+
         # Validate generated tokens.
         gpt_expected_generated_tokens = [
             [69, 85, 55, 74, 56, 89, 64, 59, 55, 67, 15, 58, 6, 37, 54, 47],
@@ -1121,54 +1138,396 @@ def test_events(self):
         )
 
         expected_event_types = [
-            ['ADD', 'FINISH'],
-            ['ADD', 'FINISH'],
-            ['ADD', 'FINISH'],
-            ['ADD', 'FINISH'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ADD', 'FINISH'],
-            ['ERROR_NONTRANSIENT', 'FAIL'],
-            ['ERROR_NONTRANSIENT', 'FAIL'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ERROR_NONTRANSIENT', 'FAIL'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ADD', 'FINISH'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
-            ['ERROR_TRANSIENT', 'ADD', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ERROR_NONTRANSIENT', 'FAIL'],
+            ['ADD_ENGINE', 'ADD_CONTEXT', 'FINISH'],
+        ]
+        result_event_types = [
+            [e.type.name for e in r.events if e.type.name != 'GENERATED_TOKEN']
+            for r in env.requests
         ]
-        result_event_types = [[e.type.name for e in r.events] for r in env.requests]
-
         assert result_event_types == expected_event_types
 
     @pytest.mark.internal
     @pytest.mark.skipif(
         not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
     )
-    @pytest.mark.parametrize("model_provider", ["gpt", "mamba"])
     @torch.inference_mode()
-    def test_chunked_prefill(self, model_provider: str):
-        """Verify that chunked prefill output is equivalent to regular prefill."""
-        skip_if_mamba_sequence_packing_not_available(model_provider)
-
-        prompt_length = 1200
-        num_tokens_to_generate = 16
-        max_sequence_length = prompt_length + num_tokens_to_generate
+    def test_event_timestamps(self):
+        """Test that events are recorded with sensical timestamps.
 
-        # Configure context to force chunking
+        Verifies:
+        1. Completed requests have ADD_ENGINE, ADD_CONTEXT, GENERATED_TOKEN(s), FINISH events
+        2. Event timestamps are monotonically increasing
+        3. TTFT (time-to-first-token) can be computed as first GENERATED_TOKEN - ADD_ENGINE
+        """
+        num_tokens_to_generate = 8
         env = self._run_test(
-            num_requests=1,
-            min_prompt_length=prompt_length,
-            max_prompt_length=prompt_length,
+            num_requests=4,
+            max_prompt_length=16,
             num_tokens_to_generate=num_tokens_to_generate,
-            materialize_only_last_token_logits=False,
-            model_provider=model_provider,
+            context_buffer_size_gb=0.1,
+            num_gap_steps=0,
+            track_generated_token_events=True,
+        )
+
+        # All requests should complete with this generous config (large buffer, no gap steps).
+        assert all(r.status == Status.COMPLETED for r in env.requests)
+        for request in env.requests:
+
+            # Verify event types for completed requests
+            event_types = [e.type.name for e in request.events]
+            # Should be: ADD_ENGINE, ADD_CONTEXT, GENERATED_TOKEN (repeated), FINISH
+            assert (
+                event_types[0] == 'ADD_ENGINE'
+            ), f"Request {request.request_id}: first event should be ADD_ENGINE, got {event_types[0]}"
+            assert (
+                event_types[1] == 'ADD_CONTEXT'
+            ), f"Request {request.request_id}: second event should be ADD_CONTEXT, got {event_types[1]}"
+            assert (
+                event_types[-1] == 'FINISH'
+            ), f"Request {request.request_id}: last event should be FINISH, got {event_types[-1]}"
+            # Check that GENERATED_TOKEN events are in the middle
+            gen_token_count = event_types.count('GENERATED_TOKEN')
+            assert gen_token_count == len(request.generated_tokens), (
+                f"Request {request.request_id}: GENERATED_TOKEN count ({gen_token_count}) != "
+                f"generated_tokens length ({len(request.generated_tokens)})"
+            )
+
+            # Verify timestamps are monotonically increasing
+            timestamps = [e.timestamp for e in request.events]
+            for i in range(1, len(timestamps)):
+                assert timestamps[i] >= timestamps[i - 1], (
+                    f"Request {request.request_id}: timestamp[{i}] ({timestamps[i]}) < "
+                    f"timestamp[{i-1}] ({timestamps[i-1]})"
+                )
+
+            # Verify TTFT is positive and sensical (first GENERATED_TOKEN - ADD_ENGINE)
+            add_engine_ts = request.events[0].timestamp
+            first_token_ts = request.events[2].timestamp  # First GENERATED_TOKEN event
+            assert (
+                request.events[2].type.name == 'GENERATED_TOKEN'
+            ), f"Request {request.request_id}: event[2] should be GENERATED_TOKEN"
+            ttft = first_token_ts - add_engine_ts
+            assert ttft >= 0, f"Request {request.request_id}: TTFT is negative ({ttft})"
+
+            # Verify total request time is positive
+            finish_ts = request.events[-1].timestamp
+            total_time = finish_ts - add_engine_ts
+            assert (
+                total_time >= ttft
+            ), f"Request {request.request_id}: total_time ({total_time}) < TTFT ({ttft})"
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @torch.inference_mode()
+    def test_mamba_chunked_prefill(self):
+        """
+        Test chunked prefill with a Mamba model.
+        """
+        skip_if_mamba_sequence_packing_not_available("mamba")
+
+        # Context max tokens = 50.
+        test_config = DynamicEngineTestConfig(
+            model_provider="mamba",
+            num_requests=0,
+            num_tokens_to_generate=None,
+            num_tokens_total=200,
+            context_max_tokens=52,
+            context_max_requests=5,
             context_block_size_tokens=256,
-            context_max_tokens=1000,
             enable_chunked_prefill=True,
+            use_cuda_graphs_for_non_decode_steps=False,
         )
 
+        env = self._build_test_env(test_config)
+        ctx = env.engine.context
+
+        # Mock the model forward function to avoid possible numerics issues
+        # caused by random inputs
+        model_instance = env.engine.controller.inference_wrapped_model.model
+        model_instance.forward = partial(mock_forward, vocab_size=test_config.vocab_size)
+
+        # Request 1: 150 tokens
+        req1_tokens = torch.randint(0, test_config.vocab_size, (130,), device='cuda')
+        req1 = DynamicInferenceRequest(
+            request_id=1,
+            prompt_tokens=req1_tokens,
+            sampling_params=SamplingParams(num_tokens_to_generate=3),
+        )
+
+        # Request 2: 160 tokens
+        req2_tokens = torch.randint(0, test_config.vocab_size, (160,), device='cuda')
+        req2 = DynamicInferenceRequest(
+            request_id=2,
+            prompt_tokens=req2_tokens,
+            sampling_params=SamplingParams(num_tokens_to_generate=1),
+        )
+
+        # Request 3: 24 tokens
+        req3_tokens = torch.randint(0, test_config.vocab_size, (24,), device='cuda')
+        req3 = DynamicInferenceRequest(
+            request_id=3,
+            prompt_tokens=req3_tokens,
+            sampling_params=SamplingParams(num_tokens_to_generate=1),
+        )
+
+        # Add requests 1-3
+        env.engine._add_request(req1)
+        env.engine._add_request(req2)
+        env.engine._add_request(req3)
+
+        # Run step 1
+        env.engine.schedule_waiting_requests()
+
+        env.engine.step_modern()
+        assert req1.finished_chunk_token_count == 52
+
+        # Prepare for step 2
+        env.engine.schedule_waiting_requests()
+
+        # Verify that requests 2 and 3 are queued because request 1 is still running
+        assert ctx.num_prefill_requests == 1
+        active_ids = ctx.request_ids[: ctx.total_request_count].tolist()
+        assert 1 in active_ids
+        assert 2 not in active_ids
+        assert 3 not in active_ids
+        assert 2 in env.engine.waiting_request_ids
+        assert 3 in env.engine.waiting_request_ids
+
+        # Verify that active token count == max tokens - 1
+        # The -1 is for the useless chunked prefill token
+        assert ctx.active_token_count == 51
+
+        # Verify that request 1 is the designated chunked prefill request
+        assert ctx.chunked_prefill_request_id == 1
+
+        # Run step 2
+        env.engine.step_modern()
+        assert req1.finished_chunk_token_count == 104
+
+        # Prepare for step 3
+        env.engine.schedule_waiting_requests()
+
+        # Verify that request 2 got partially scheduled and is now
+        # the designated chunked prefill request
+        req2_idx = ctx.request_ids.tolist().index(2)
+        assert req2_idx == 1
+        assert ctx.num_prefill_requests == 2
+        assert ctx.chunked_prefill_request_id == 2
+        assert ctx.get_index_of_chunked_prefill_request() == req2_idx
+        active_ids = ctx.request_ids[: ctx.total_request_count].tolist()
+        assert 1 in active_ids
+        assert 2 in active_ids
+        assert 3 not in active_ids
+
+        # Store the Mamba state tensor idx for request 2
+        req2_mamba_idx = ctx.mamba_metadata.request_to_mamba_state_idx[req2_idx].item()
+
+        # Verify that the active token count is the maximum token count
+        assert ctx.active_token_count == 52
+
+        # Run step 3
+        env.engine.step_modern()
+        assert req1.finished_chunk_token_count == 104
+
+        # Prepare for step 4
+        env.engine.schedule_waiting_requests()
+
+        # Verify that request 2 is still the first prefill request
+        assert ctx.request_ids.tolist().index(2) == 1
+        assert ctx.mamba_metadata.request_to_mamba_state_idx[1] == req2_mamba_idx
+
+        # Verify that request 1 is running decode
+        active_ids = ctx.request_ids[: ctx.total_request_count].tolist()
+        assert ctx.num_decode_requests == 1
+        assert 1 in active_ids
+
+        # Verify that request 2 is still running prefill as the designated chunked prefill request
+        assert ctx.num_prefill_requests == 1
+        assert ctx.chunked_prefill_request_id == 2
+        assert ctx.get_index_of_chunked_prefill_request() == 1
+
+        # Verify that request 3 is still waiting
+        assert 3 not in active_ids
+        assert 3 in env.engine.waiting_request_ids
+
+        # Verify that active token count == max tokens - 1
+        # The -1 is for the useless chunked prefill token
+        assert ctx.active_token_count == 51
+
+        # Run step 4
+        env.engine.step_modern()
+
+        assert req2.finished_chunk_token_count == 77
+
+        # Prepare for step 5
+        env.engine.schedule_waiting_requests()
+
+        # Verify that request 2 is still the first prefill request
+        assert ctx.request_ids.tolist().index(2) == 1
+        assert ctx.mamba_metadata.request_to_mamba_state_idx[1] == req2_mamba_idx
+
+        # Run step 5
+        env.engine.step_modern()
+        assert req2.finished_chunk_token_count == 128
+
+        # Prepare for step 6
+        env.engine.schedule_waiting_requests()
+
+        # Verify that request 1 has completed
+        assert req1.status == Status.COMPLETED
+
+        # Verify that request 2 is still the first prefill request
+        assert ctx.request_ids.tolist().index(2) == 0
+        assert ctx.mamba_metadata.request_to_mamba_state_idx[0] == req2_mamba_idx
+
+        # Verify that request 3 is now scheduled as the chunked prefill request
+        active_ids = ctx.request_ids[: ctx.total_request_count].tolist()
+        assert 2 in active_ids
+        assert 3 in active_ids
+        assert ctx.chunked_prefill_request_id == 3
+        req3_idx = active_ids.index(3)
+        assert req3_idx == 1
+
+        # Store the Mamba state tensor idx for request 3
+        req3_mamba_idx = ctx.mamba_metadata.request_to_mamba_state_idx[req3_idx].item()
+
+        # Run step 6
+        env.engine.step_modern()
+
+        # Verify that request 2 has finished
+        assert req2.status == Status.COMPLETED
+        assert req3.finished_chunk_token_count == 20
+
+        # Prepare for step 7
+        env.engine.schedule_waiting_requests()
+
+        # Verify that request 3 is now the first prefill request
+        req3_idx = ctx.request_ids.tolist().index(3)
+        assert req3_idx == 0
+        assert ctx.mamba_metadata.request_to_mamba_state_idx[0] == req3_mamba_idx
+
+        # Run step 7
+        env.engine.step_modern()
+
+        # Verify that request 3 has finished
+        assert req3.status == Status.COMPLETED
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @torch.inference_mode()
+    def test_chunked_prefill_avoid_single_token_chunk(self):
+        """
+        Test that chunked prefill scheduling avoids leaving exactly 1 token for the final chunk.
+        This leads to a known bug in the Flash Attention kernel:
+        https://github.com/Dao-AILab/flash-attention/issues/1537
+
+        Scenario:
+            - Max tokens per step (Chunk Size): 256
+            - Request prompt length: 512
+
+        Note that for all chunks after the first chunk we subtract 1 from the active token
+        count, so the chunk size will be 1 less.
+
+        Default scheduling would do:
+            1. Chunk 256 (Remaining 256)
+            2. Chunk 255 (Remaining 1) -> max_seqlen_q=1 triggers decode path in kernel
+            3. Chunk 1
+
+        Fixed scheduling should do:
+            1. Chunk 256 (Remaining 256) -> 256 - 256 != 1. Schedule full 256.
+            2. Chunk 254 (Remaining 2)   -> 256 tokens left. If we take 255, 1 remains.
+                                            So we reduce chunk to 254.
+            3. Chunk 2   (Remaining 0)
+        """
+        chunk_size = 256
+        # Prompt length designed to trigger the edge case: Chunk + (Chunk + 1)
+        # 256 + 256 = 512
+        prompt_len = 512
+
+        test_config = DynamicEngineTestConfig(
+            model_provider="gpt",
+            num_requests=0,
+            num_tokens_to_generate=None,
+            num_tokens_total=513,
+            context_max_tokens=chunk_size,
+            context_max_requests=1,
+            context_block_size_tokens=256,
+            enable_chunked_prefill=True,
+            use_cuda_graphs_for_non_decode_steps=False,
+        )
+
+        env = self._build_test_env(test_config)
+        ctx = env.engine.context
+
+        # Mock the model forward function to avoid possible numerics issues
+        # caused by random inputs
+        model_instance = env.engine.controller.inference_wrapped_model.model
+        model_instance.forward = partial(mock_forward, vocab_size=test_config.vocab_size)
+
+        # Create a request with length 513
+        req_tokens = torch.randint(0, test_config.vocab_size, (prompt_len,), device='cuda')
+        req = DynamicInferenceRequest(
+            request_id=1,
+            prompt_tokens=req_tokens,
+            sampling_params=SamplingParams(num_tokens_to_generate=1),
+        )
+
+        env.engine._add_request(req)
+
+        assert req.status == Status.ACTIVE_AND_GENERATING_TOKENS
+
+        # --- Step 1 ---
+        # Available: 256. Remaining: 512.
+        # Logic: 512 - 256 = 256. Not 1. Schedule full 256.
+        env.engine.schedule_waiting_requests()
+        env.engine.step_modern()
+
+        assert (
+            req.finished_chunk_token_count == 256
+        ), f"Step 1: Expected 256 tokens processed, got {req.finished_chunk_token_count}"
+
+        # --- Step 2 ---
+        # Available: 256. Remaining un-prefilled: 256.
+        # Logic: 256 - (256 - 1) = 1. This is the edge case!
+        # Fix should reduce chunk size by 1 (to 254).
+        env.engine.schedule_waiting_requests()
+        env.engine.step_modern()
+
+        # 256 (previous) + 254 (this step) = 510
+        assert req.finished_chunk_token_count == 510, (
+            "Step 2: Expected 510 tokens processed (256+254), "
+            f"got {req.finished_chunk_token_count}. "
+        )
+
+        # --- Step 3 ---
+        # Remaining un-prefilled: 2. Available: 255.
+        # Logic: 2 <= 255. Schedule 2.
+        env.engine.schedule_waiting_requests()
+        env.engine.step_modern()
+
+        # Verify request finishes prefill and completes
+        assert ctx.num_prefill_requests == 0
+        assert req.status == Status.COMPLETED
+
     @pytest.mark.internal
     @pytest.mark.skipif(
         not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
@@ -1387,3 +1746,254 @@ def test_max_requests(self, max_requests: int | None):
             assert context.max_requests == 4
             assert step_count == 34
         assert context.block_allocator.active_count == 655
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @pytest.mark.parametrize("static_kv_memory_pointers", [True, False])
+    @pytest.mark.parametrize("kv_cache_management_mode", ["persist", "offload", "recompute"])
+    @torch.inference_mode()
+    def test_suspend_resume_cycle(self, kv_cache_management_mode, static_kv_memory_pointers):
+        """Full suspend -> resume cycle with memory, data, and address checks."""
+        needs_tms = static_kv_memory_pointers and kv_cache_management_mode != "persist"
+
+        test_config = DynamicEngineTestConfig(
+            kv_cache_management_mode=kv_cache_management_mode,
+            static_kv_memory_pointers=static_kv_memory_pointers,
+        )
+
+        # Without TMS, these combos must assert on construction.
+        if needs_tms and not HAVE_TORCH_MEMORY_SAVER:
+            with pytest.raises(AssertionError, match="Static KV memory pointers"):
+                self._build_test_env(test_config)
+            return
+
+        env = self._build_test_env(test_config)
+        engine = env.engine
+        context = engine.context
+
+        assert not engine.is_suspended
+        assert context.is_tensor_state_allocated
+
+        deallocates = kv_cache_management_mode != "persist"
+        uses_tms = context._uses_torch_memory_saver
+        preserves_data = kv_cache_management_mode != "recompute"
+
+        # Write a deterministic pattern for data integrity check.
+        if preserves_data:
+            context.memory_buffer.copy_(torch.randn_like(context.memory_buffer))
+            expected = context.memory_buffer.clone()
+
+        addr_before = context.memory_buffer.data_ptr()
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+        mem_before = torch.cuda.memory_allocated()
+        if uses_tms:
+            phys_mem_before = torch.cuda.mem_get_info()[0]
+
+        # Suspend.
+        engine.suspend()
+        assert engine.is_suspended
+        assert not context.is_tensor_state_allocated
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+        mem_suspended = torch.cuda.memory_allocated()
+        if uses_tms:
+            phys_mem_suspended = torch.cuda.mem_get_info()[0]
+
+        if deallocates and not uses_tms:
+            assert mem_suspended < mem_before, (
+                f"GPU memory should decrease after suspend "
+                f"(mode={kv_cache_management_mode}). "
+                f"Before: {mem_before}, After: {mem_suspended}"
+            )
+        else:
+            assert mem_suspended == mem_before, (
+                f"Memory should not change on suspend. "
+                f"Before: {mem_before}, Suspended: {mem_suspended}"
+            )
+
+        if uses_tms:
+            assert phys_mem_suspended > phys_mem_before, (
+                f"torch_memory_saver should free physical GPU memory after suspend. "
+                f"Before: {phys_mem_before}, After: {phys_mem_suspended}"
+            )
+
+        # Resume.
+        engine.resume()
+        assert not engine.is_suspended
+        assert context.is_tensor_state_allocated
+
+        if deallocates and not uses_tms:
+            torch.cuda.synchronize()
+            mem_resumed = torch.cuda.memory_allocated()
+            assert mem_resumed > mem_suspended, (
+                f"GPU memory should increase after resume. "
+                f"Suspended: {mem_suspended}, Resumed: {mem_resumed}"
+            )
+
+        if uses_tms:
+            torch.cuda.synchronize()
+            phys_mem_resumed = torch.cuda.mem_get_info()[0]
+            assert phys_mem_resumed < phys_mem_suspended, (
+                f"torch_memory_saver should re-allocate physical GPU memory after resume. "
+                f"Suspended: {phys_mem_suspended}, Resumed: {phys_mem_resumed}"
+            )
+
+        # Data integrity.
+        if preserves_data:
+            torch.testing.assert_close(
+                context.memory_buffer,
+                expected,
+                msg="memory_buffer data must be identical after suspend/resume",
+            )
+
+        # Address stability when CUDA graphs persist.
+        if static_kv_memory_pointers:
+            addr_after = context.memory_buffer.data_ptr()
+            assert addr_before == addr_after, (
+                f"Tensor address must be stable when static_kv_memory_pointers is set. "
+                f"Before: {addr_before:#x}, After: {addr_after:#x}"
+            )
+
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @pytest.mark.parametrize("use_checkpoint", [False, True], ids=["persist", "recompute"])
+    @torch.inference_mode()
+    def test_staleness_tracking(self, use_checkpoint):
+        """Test that staleness is correct tracked.
+        The use_checkpoint parameter simulates the behavior of different kv_cache_management_mode.
+        """
+        PROMPT_LEN = 8
+        NUM_TOKENS = 8
+
+        test_config = DynamicEngineTestConfig(
+            num_requests=0,
+            min_prompt_length=PROMPT_LEN,
+            max_prompt_length=PROMPT_LEN,
+            num_tokens_to_generate=NUM_TOKENS,
+        )
+        env = self._build_test_env(test_config)
+        engine = env.engine
+
+        for i in range(2):
+            prompt_tokens = torch.randint(
+                0,
+                test_config.vocab_size - 1,
+                (PROMPT_LEN,),
+                dtype=torch.int64,
+                device=torch.cuda.current_device(),
+            )
+            engine._add_request(
+                DynamicInferenceRequest(
+                    request_id=i,
+                    prompt_tokens=prompt_tokens,
+                    sampling_params=SamplingParams(
+                        num_tokens_to_generate=NUM_TOKENS, termination_id=-1
+                    ),
+                )
+            )
+
+        for _ in range(3):
+            engine.step_modern()
+
+        for entry in engine.requests.values():
+            assert len(entry.record[-1].generated_tokens) == 3
+            assert entry.record[-1].policy_staleness is None
+            assert entry.record[-1].kv_cache_staleness is None
+
+        # Increment staleness.
+        for entry in engine.requests.values():
+            entry.record.increment_staleness()
+
+        for entry in engine.requests.values():
+            ps = entry.record[-1].policy_staleness
+            ks = entry.record[-1].kv_cache_staleness
+            assert ps.shape == ks.shape == (PROMPT_LEN + 3,)
+            assert (ps == 1).all()
+            assert (ks == 1).all()
+
+        # Simulate RECOMPUTE
+        if use_checkpoint:
+            for entry in engine.requests.values():
+                old_req = entry.record[-1]
+                event_add_engine = old_req.event_add_engine
+                entry.record.checkpoint()
+                # Prevent TTFT crash due to missing _add_request in test.
+                entry.record[-1].event_add_engine = event_add_engine
+
+            for entry in engine.requests.values():
+                ps = entry.record[-1].policy_staleness
+                ks = entry.record[-1].kv_cache_staleness
+                assert ps.shape == ks.shape == (PROMPT_LEN + 3,)
+                assert (ps == 1).all()
+                assert (ks == 0).all()
+
+        for _ in range(3):
+            engine.step_modern()
+
+        # Increment staleness.
+        for entry in engine.requests.values():
+            entry.record.increment_staleness()
+
+        for entry in engine.requests.values():
+            ps = entry.record[-1].policy_staleness
+            ks = entry.record[-1].kv_cache_staleness
+            assert ps.shape == ks.shape == (PROMPT_LEN + 6,)
+            assert (ps[: PROMPT_LEN + 3] == 2).all()
+            assert (ps[PROMPT_LEN + 3 :] == 1).all()
+            if use_checkpoint:
+                assert (ks == 1).all()
+            else:
+                assert (ks[: PROMPT_LEN + 3] == 2).all()
+                assert (ks[PROMPT_LEN + 3 :] == 1).all()
+
+        if use_checkpoint:
+            for entry in engine.requests.values():
+                old_req = entry.record[-1]
+                event_add_engine = old_req.event_add_engine
+                entry.record.checkpoint()
+                entry.record[-1].event_add_engine = event_add_engine
+
+            for entry in engine.requests.values():
+                ks = entry.record[-1].kv_cache_staleness
+                assert (ks == 0).all()
+
+        finished_records = []
+        while engine.has_unfinished_requests():
+            result = engine.step_modern()
+            finished_records.extend(result["finished_request_records"])
+
+        for record in finished_records:
+            merged = record.merge()
+
+            assert merged.policy_staleness is not None
+            assert merged.policy_staleness.shape == (PROMPT_LEN + NUM_TOKENS,)
+            assert (merged.policy_staleness[: PROMPT_LEN + 3] == 2).all()
+            assert (merged.policy_staleness[PROMPT_LEN + 3 : PROMPT_LEN + 6] == 1).all()
+            assert (merged.policy_staleness[PROMPT_LEN + 6 :] == 0).all()
+
+            assert merged.kv_cache_staleness is not None
+            assert merged.kv_cache_staleness.shape == (PROMPT_LEN + NUM_TOKENS,)
+            if use_checkpoint:
+                assert (merged.kv_cache_staleness == 0).all()
+            else:
+                assert (merged.kv_cache_staleness[: PROMPT_LEN + 3] == 2).all()
+                assert (merged.kv_cache_staleness[PROMPT_LEN + 3 : PROMPT_LEN + 6] == 1).all()
+                assert (merged.kv_cache_staleness[PROMPT_LEN + 6 :] == 0).all()
+
+        # Verify evicted requests don't have their policy staleness incremented.
+        record = finished_records[0]
+        record.checkpoint()
+        pre_ps = record[-1].policy_staleness.clone()
+
+        record.increment_staleness(policy_only=True)  # This mimics the coordinator's action.
+
+        assert (record[-1].policy_staleness == pre_ps + 1).all()
+        assert (record[-1].kv_cache_staleness == 0).all()
diff --git a/tests/unit_tests/inference/engines/test_dynamic_events.py b/tests/unit_tests/inference/engines/test_dynamic_events.py
new file mode 100644
index 00000000000..35e0933db18
--- /dev/null
+++ b/tests/unit_tests/inference/engines/test_dynamic_events.py
@@ -0,0 +1,670 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+"""Comprehensive tests for DynamicInferenceEvent and request event lifecycle.
+
+This test suite covers all 9 event types, payload validation, serialization,
+request methods, and lifecycle sequences with exactly 9 substantial tests.
+"""
+
+import time
+
+import pytest
+import torch
+
+from megatron.core.inference.contexts.dynamic_context import (
+    BlockOverflowError,
+    MaxSequenceLengthOverflowError,
+    RequestOverflowError,
+    TokenOverflowError,
+)
+from megatron.core.inference.inference_request import (
+    DynamicInferenceEvent,
+    DynamicInferenceEventType,
+    DynamicInferenceRequest,
+    DynamicInferenceRequestRecord,
+)
+from megatron.core.inference.sampling_params import SamplingParams
+
+# ============================================================================
+# Fixtures
+# ============================================================================
+
+
+@pytest.fixture
+def basic_request():
+    """Create a basic request for testing."""
+    return DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3, 4], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=10),
+    )
+
+
+# ============================================================================
+# Test 1: All Event Types Creation and Payload Validation
+# ============================================================================
+
+
+def test_all_event_types_creation_and_payload_validation():
+    """Test all 9 event types exist, are unique, and enforce payload validation rules.
+
+    Coverage:
+    - All 9 event types (ADD_ENGINE, ADD_CONTEXT, GENERATED_TOKEN, PAUSE, EVICT,
+      FINISH, FAIL, ERROR_TRANSIENT, ERROR_NONTRANSIENT)
+    - Payload requirements (int for GENERATED_TOKEN, exception for errors, None for others)
+    - Auto-timestamp generation
+    - Invalid payload rejection via AssertionError
+    """
+    # Verify exactly 9 event types exist
+    expected_types = [
+        'ADD_ENGINE',
+        'ADD_CONTEXT',
+        'GENERATED_TOKEN',
+        'PAUSE',
+        'EVICT',
+        'FINISH',
+        'FAIL',
+        'ERROR_TRANSIENT',
+        'ERROR_NONTRANSIENT',
+    ]
+    for event_type in expected_types:
+        assert hasattr(DynamicInferenceEventType, event_type), f"Missing: {event_type}"
+    assert len(DynamicInferenceEventType) == 9
+
+    # Verify all values are unique
+    values = [e.value for e in DynamicInferenceEventType]
+    assert len(values) == len(set(values)), "Duplicate event type values"
+
+    # Create events that require no payload
+    no_payload_types = [
+        DynamicInferenceEventType.ADD_ENGINE,
+        DynamicInferenceEventType.ADD_CONTEXT,
+        DynamicInferenceEventType.PAUSE,
+        DynamicInferenceEventType.EVICT,
+        DynamicInferenceEventType.FINISH,
+        DynamicInferenceEventType.FAIL,
+    ]
+    for event_type in no_payload_types:
+        event = DynamicInferenceEvent(type=event_type)
+        assert event.type == event_type
+        assert event.payload is None
+        assert event.timestamp is not None
+        # Verify these types reject non-None payloads
+        with pytest.raises(AssertionError):
+            DynamicInferenceEvent(type=event_type, payload="not allowed")
+
+    # Create GENERATED_TOKEN event with dict payload
+    token_event = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.GENERATED_TOKEN, payload={"token_id": 42}
+    )
+    assert token_event.type == DynamicInferenceEventType.GENERATED_TOKEN
+    assert token_event.payload == {"token_id": 42}
+    assert token_event.timestamp is not None
+
+    # GENERATED_TOKEN rejects None, string, float, and int payloads (must be dict with "token_id")
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.GENERATED_TOKEN)
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.GENERATED_TOKEN, payload="bad")
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.GENERATED_TOKEN, payload=3.14)
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.GENERATED_TOKEN, payload=42)
+
+    # Create error events with exception payloads
+    transient_error = RequestOverflowError(request_id=1, message="Transient")
+    event_transient = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.ERROR_TRANSIENT, payload=transient_error
+    )
+    assert event_transient.type == DynamicInferenceEventType.ERROR_TRANSIENT
+    assert event_transient.payload is transient_error
+
+    nontransient_error = MaxSequenceLengthOverflowError(request_id=2, message="Fatal")
+    event_nontransient = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.ERROR_NONTRANSIENT, payload=nontransient_error
+    )
+    assert event_nontransient.type == DynamicInferenceEventType.ERROR_NONTRANSIENT
+    assert event_nontransient.payload is nontransient_error
+
+    # Error events reject None payloads
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.ERROR_TRANSIENT)
+    with pytest.raises(AssertionError):
+        DynamicInferenceEvent(type=DynamicInferenceEventType.ERROR_NONTRANSIENT)
+
+    # Verify auto-timestamp is reasonable
+    before = time.time()
+    event = DynamicInferenceEvent(type=DynamicInferenceEventType.ADD_ENGINE)
+    after = time.time()
+    assert before <= event.timestamp <= after
+
+
+# ============================================================================
+# Test 2: Successful Request Lifecycle with Serialization Roundtrip
+# ============================================================================
+
+
+def test_successful_request_lifecycle_with_serialization_roundtrip(basic_request):
+    """Test full request lifecycle: ADD_ENGINE -> ADD_CONTEXT -> GENERATED_TOKEN(s) -> FINISH.
+
+    Coverage:
+    - Full successful lifecycle event sequence
+    - Event order preservation
+    - Request serialization/deserialization roundtrip
+    - generated_tokens as direct List[int] field
+    """
+    # Build complete successful lifecycle
+    basic_request.add_event_add_engine()
+    basic_request.add_event_add_context()
+    basic_request.generated_tokens.extend([100, 200, 300])
+    basic_request.add_event_generated_token(100)
+    basic_request.add_event_generated_token(200)
+    basic_request.add_event_generated_token(300)
+    basic_request.add_event_finish()
+
+    # Verify event sequence
+    event_types = [e.type for e in basic_request.events]
+    assert event_types == [
+        DynamicInferenceEventType.ADD_ENGINE,
+        DynamicInferenceEventType.ADD_CONTEXT,
+        DynamicInferenceEventType.GENERATED_TOKEN,
+        DynamicInferenceEventType.GENERATED_TOKEN,
+        DynamicInferenceEventType.GENERATED_TOKEN,
+        DynamicInferenceEventType.FINISH,
+    ]
+
+    # Verify generated_tokens is a direct list field
+    assert basic_request.generated_tokens == [100, 200, 300]
+    assert isinstance(basic_request.generated_tokens, list)
+
+    # Serialize request
+    serialized = basic_request.serialize()
+    assert 'events' in serialized
+    assert len(serialized['events']) == 6
+    assert serialized['generated_tokens'] == [100, 200, 300]
+
+    # Verify GENERATED_TOKEN events serialize with {"token_id": ...} payload
+    token_events = [e for e in serialized['events'] if e['type'] == 'GENERATED_TOKEN']
+    assert len(token_events) == 3
+    assert token_events[0]['payload'] == {"token_id": 100}
+    assert token_events[1]['payload'] == {"token_id": 200}
+    assert token_events[2]['payload'] == {"token_id": 300}
+
+    # Deserialize and verify restoration
+    restored = DynamicInferenceRequest.deserialize(serialized)
+    assert len(restored.events) == 6
+    assert restored.generated_tokens == [100, 200, 300]
+
+    # Verify event order preserved after roundtrip
+    restored_types = [e.type for e in restored.events]
+    assert restored_types == event_types
+
+    # Verify timestamps preserved
+    for orig, rest in zip(basic_request.events, restored.events):
+        assert rest.timestamp == orig.timestamp
+
+
+# ============================================================================
+# Test 3: Error Event Serialization with ContextErrorFactory
+# ============================================================================
+
+
+def test_error_event_serialization_with_context_error_factory():
+    """Test error event serialization using ContextErrorFactory for all error types.
+
+    Coverage:
+    - ERROR_TRANSIENT and ERROR_NONTRANSIENT events
+    - All 4 error types: RequestOverflowError, TokenOverflowError,
+      MaxSequenceLengthOverflowError, BlockOverflowError
+    - Error attribute preservation (request_id, message, is_transient) through roundtrip
+    """
+    error_cases = [
+        # (event_type, error_class, request_id, message, is_transient)
+        (
+            DynamicInferenceEventType.ERROR_TRANSIENT,
+            RequestOverflowError,
+            1,
+            "Max requests exceeded",
+            True,
+        ),
+        (
+            DynamicInferenceEventType.ERROR_TRANSIENT,
+            TokenOverflowError,
+            2,
+            "Token limit exceeded",
+            True,
+        ),
+        (DynamicInferenceEventType.ERROR_TRANSIENT, BlockOverflowError, 3, "Block overflow", True),
+        (
+            DynamicInferenceEventType.ERROR_NONTRANSIENT,
+            MaxSequenceLengthOverflowError,
+            4,
+            "Sequence too long",
+            False,
+        ),
+    ]
+
+    for event_type, error_class, req_id, message, is_transient in error_cases:
+        # Create error and event
+        error = error_class(request_id=req_id, message=message)
+        event = DynamicInferenceEvent(type=event_type, payload=error)
+
+        # Verify error attributes before serialization
+        assert event.payload.request_id == req_id
+        assert event.payload.message == message
+        assert event.payload.is_transient == is_transient
+
+        # Serialize and deserialize
+        serialized = event.serialize()
+        assert serialized['type'] == event_type.name
+        assert serialized['payload'] is not None
+
+        deserialized = DynamicInferenceEvent.deserialize(serialized)
+
+        # Verify restoration
+        assert deserialized.type == event_type
+        assert deserialized.payload.request_id == req_id
+        assert deserialized.payload.message == message
+        assert deserialized.payload.is_transient == is_transient
+        assert deserialized.timestamp == event.timestamp
+
+
+# ============================================================================
+# Test 4: Generated Tokens Property Filters and Preserves Order
+# ============================================================================
+
+
+def test_generated_tokens_is_direct_list_field(basic_request):
+    """Test generated_tokens is a direct List[int] field, independent of events.
+
+    Coverage:
+    - generated_tokens starts as empty list
+    - Direct append works independently of events
+    - Token ID edge cases: 0, large values (2^62)
+    - Field is serialized/deserialized correctly
+    """
+    # Start with empty list
+    assert basic_request.generated_tokens == []
+    assert isinstance(basic_request.generated_tokens, list)
+
+    # Direct append works
+    basic_request.generated_tokens.append(0)
+    assert basic_request.generated_tokens == [0]
+
+    basic_request.generated_tokens.append(100)
+    assert basic_request.generated_tokens == [0, 100]
+
+    # Large token ID
+    large_id = 2**62
+    basic_request.generated_tokens.append(large_id)
+    assert basic_request.generated_tokens == [0, 100, large_id]
+
+    basic_request.generated_tokens.append(200)
+    assert basic_request.generated_tokens == [0, 100, large_id, 200]
+
+    # Events are independent - add some events
+    basic_request.add_event_add_engine()
+    basic_request.add_event_add_context()
+    basic_request.add_event_finish()
+    assert len(basic_request.events) == 3
+    assert len(basic_request.generated_tokens) == 4
+
+    # Serialization roundtrip preserves generated_tokens
+    serialized = basic_request.serialize()
+    assert serialized['generated_tokens'] == [0, 100, large_id, 200]
+    restored = DynamicInferenceRequest.deserialize(serialized)
+    assert restored.generated_tokens == [0, 100, large_id, 200]
+
+
+# ============================================================================
+# Test 5: Request Record Merge Across Eviction Recovery
+# ============================================================================
+
+
+def test_request_record_merge_across_eviction_recovery():
+    """Test RequestRecord.merge() combines events from multiple checkpoints across eviction/recovery.
+
+    Coverage:
+    - RequestRecord creation and merge()
+    - Eviction lifecycle simulation (first attempt evicted, second attempt succeeds)
+    - Multi-checkpoint event combination
+    - Token aggregation from all checkpoints
+    """
+    # First request: starts, generates some tokens, then gets evicted
+    req1 = DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=10),
+    )
+    req1.add_event_add_engine()
+    req1.add_event_add_context()
+    req1.generated_tokens.extend([100, 101])
+    req1.add_event_generated_token(100)
+    req1.add_event_generated_token(101)
+    req1.add_event_evict()
+
+    # Second request: recovered, generates more tokens, completes
+    req2 = DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3, 100, 101], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=8),
+    )
+    req2.add_event_add_engine()
+    req2.add_event_add_context()
+    req2.generated_tokens.extend([200, 201])
+    req2.add_event_generated_token(200)
+    req2.add_event_generated_token(201)
+    req2.add_event_finish()
+
+    # Create record with both checkpoints
+    record = DynamicInferenceRequestRecord()
+    record.requests.append(req1)
+    record.requests.append(req2)
+
+    # Merge
+    merged = record.merge()
+
+    # Verify all events present (5 from req1 + 5 from req2)
+    assert len(merged.events) == 10
+
+    # Verify event types include eviction
+    event_types = [e.type for e in merged.events]
+    assert DynamicInferenceEventType.EVICT in event_types
+    assert event_types.count(DynamicInferenceEventType.ADD_ENGINE) == 2
+    assert event_types.count(DynamicInferenceEventType.ADD_CONTEXT) == 2
+
+    # Verify all tokens combined correctly
+    assert merged.generated_tokens == [100, 101, 200, 201]
+
+    # Test from_request convenience method
+    single_record = DynamicInferenceRequestRecord.from_request(req1)
+    single_merged = single_record.merge()
+    assert len(single_merged.events) == len(req1.events)
+    assert single_merged.generated_tokens == req1.generated_tokens
+
+
+# ============================================================================
+# Test 6: TTFT Calculation from Event Timestamps
+# ============================================================================
+
+
+def test_ttft_calculation_from_event_timestamps(basic_request):
+    """Test TTFT (time-to-first-token) calculation using ADD_ENGINE and GENERATED_TOKEN timestamps.
+
+    Coverage:
+    - Timestamp accuracy
+    - ADD_ENGINE/ADD_CONTEXT split for TTFT calculation
+    - Timestamps are monotonically increasing
+    - Explicit timestamp preservation through serialization
+    """
+    # Add events with deliberate delays
+    basic_request.add_event_add_engine()
+    time.sleep(0.01)
+    basic_request.add_event_add_context()
+    time.sleep(0.01)
+    basic_request.add_event_generated_token(100)
+
+    # Find timestamps
+    add_engine_time = None
+    first_token_time = None
+    for event in basic_request.events:
+        if event.type == DynamicInferenceEventType.ADD_ENGINE:
+            add_engine_time = event.timestamp
+        elif event.type == DynamicInferenceEventType.GENERATED_TOKEN and first_token_time is None:
+            first_token_time = event.timestamp
+
+    assert add_engine_time is not None
+    assert first_token_time is not None
+
+    # Calculate TTFT
+    ttft = first_token_time - add_engine_time
+    assert ttft > 0, "TTFT should be positive"
+    assert ttft > 0.01, "TTFT should include deliberate delay"
+    assert ttft < 1.0, "TTFT should be reasonable in test"
+
+    # Verify timestamps are monotonically increasing
+    timestamps = [e.timestamp for e in basic_request.events]
+    for i in range(1, len(timestamps)):
+        assert timestamps[i] >= timestamps[i - 1], "Timestamps should not decrease"
+
+    # Test explicit timestamp preservation through serialization
+    explicit_time = 1700000000.123456
+    event_with_explicit = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.FINISH, timestamp=explicit_time
+    )
+    serialized = event_with_explicit.serialize()
+    deserialized = DynamicInferenceEvent.deserialize(serialized)
+    assert deserialized.timestamp == explicit_time
+
+
+# ============================================================================
+# Test 7: Failed Request Lifecycle with Error Propagation
+# ============================================================================
+
+
+def test_failed_request_lifecycle_with_error_propagation(basic_request):
+    """Test failed request lifecycle: transient errors followed by nontransient error and FAIL.
+
+    Coverage:
+    - FAIL event
+    - Error event -> failure path
+    - Multiple transient errors followed by nontransient
+    - Error serialization preserved in full request roundtrip
+    """
+    # Build failure lifecycle
+    basic_request.add_event_add_engine()
+
+    # Transient errors (retryable)
+    transient1 = TokenOverflowError(request_id=1, message="Token overflow 1")
+    basic_request.add_event_error_transient(transient1)
+
+    transient2 = BlockOverflowError(request_id=1, message="Block overflow")
+    basic_request.add_event_error_transient(transient2)
+
+    # Fatal error
+    fatal = MaxSequenceLengthOverflowError(request_id=1, message="Sequence too long")
+    basic_request.add_event_error_nontransient(fatal)
+
+    # Request fails
+    basic_request.add_event_fail()
+
+    # Verify event sequence
+    event_types = [e.type for e in basic_request.events]
+    assert event_types == [
+        DynamicInferenceEventType.ADD_ENGINE,
+        DynamicInferenceEventType.ERROR_TRANSIENT,
+        DynamicInferenceEventType.ERROR_TRANSIENT,
+        DynamicInferenceEventType.ERROR_NONTRANSIENT,
+        DynamicInferenceEventType.FAIL,
+    ]
+
+    # Serialize entire request
+    serialized = basic_request.serialize()
+    assert len(serialized['events']) == 5
+
+    # Deserialize
+    restored = DynamicInferenceRequest.deserialize(serialized)
+    assert len(restored.events) == 5
+
+    # Verify error payloads preserved
+    error_events = [e for e in restored.events if 'ERROR' in e.type.name]
+    assert len(error_events) == 3
+
+    # Check first transient error
+    assert error_events[0].payload.message == "Token overflow 1"
+    assert error_events[0].payload.is_transient is True
+
+    # Check second transient error
+    assert error_events[1].payload.message == "Block overflow"
+    assert error_events[1].payload.is_transient is True
+
+    # Check nontransient error
+    assert error_events[2].payload.message == "Sequence too long"
+    assert error_events[2].payload.is_transient is False
+
+
+# ============================================================================
+# Test 8: Event String Representation for All Types
+# ============================================================================
+
+
+def test_event_str_representation_for_all_types():
+    """Test __str__ method produces correct format for all event types.
+
+    Coverage:
+    - Simple events show type and timestamp
+    - GENERATED_TOKEN shows token=<id>
+    - Error events show error class name
+    - Format consistency across types
+    """
+    # Simple events (no payload)
+    simple_types = [
+        DynamicInferenceEventType.ADD_ENGINE,
+        DynamicInferenceEventType.ADD_CONTEXT,
+        DynamicInferenceEventType.PAUSE,
+        DynamicInferenceEventType.EVICT,
+        DynamicInferenceEventType.FINISH,
+        DynamicInferenceEventType.FAIL,
+    ]
+    for event_type in simple_types:
+        event = DynamicInferenceEvent(type=event_type)
+        str_repr = str(event)
+        assert event_type.name in str_repr, f"{event_type.name} not in {str_repr}"
+        # Timestamp should appear (at least first few digits)
+        assert str(event.timestamp)[:5] in str_repr
+
+    # GENERATED_TOKEN shows token=<id>
+    token_event = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.GENERATED_TOKEN, payload={"token_id": 42}
+    )
+    str_repr = str(token_event)
+    assert 'GENERATED_TOKEN' in str_repr
+    assert 'token=42' in str_repr
+
+    # Large token ID
+    large_token_event = DynamicInferenceEvent(
+        type=DynamicInferenceEventType.GENERATED_TOKEN, payload={"token_id": 9999999}
+    )
+    assert 'token=9999999' in str(large_token_event)
+
+    # Error events show error class name
+    error_cases = [
+        (DynamicInferenceEventType.ERROR_TRANSIENT, RequestOverflowError, "RequestOverflowError"),
+        (DynamicInferenceEventType.ERROR_TRANSIENT, TokenOverflowError, "TokenOverflowError"),
+        (DynamicInferenceEventType.ERROR_TRANSIENT, BlockOverflowError, "BlockOverflowError"),
+        (
+            DynamicInferenceEventType.ERROR_NONTRANSIENT,
+            MaxSequenceLengthOverflowError,
+            "MaxSequenceLengthOverflowError",
+        ),
+    ]
+    for event_type, error_class, expected_name in error_cases:
+        error = error_class(request_id=1, message="Test error")
+        event = DynamicInferenceEvent(type=event_type, payload=error)
+        str_repr = str(event)
+        assert event_type.name in str_repr
+        assert expected_name in str_repr
+
+
+# ============================================================================
+# Test 9: Complex Multi-Pause-Evict Lifecycle with Record
+# ============================================================================
+
+
+def test_complex_multi_pause_evict_lifecycle_with_record():
+    """Test complex real-world scenario with multiple pauses, evictions, and recoveries.
+
+    Coverage:
+    - Complex lifecycle with multiple PAUSE events
+    - Multiple evictions and recoveries
+    - Full roundtrip with RequestRecord
+    - Complete event history and all tokens present
+    """
+    # First attempt: starts, generates token, pauses, generates more, gets evicted
+    req1 = DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=20),
+    )
+    req1.add_event_add_engine()
+    req1.add_event_add_context()
+    req1.generated_tokens.append(10)
+    req1.add_event_generated_token(10)
+    req1.add_event_pause()  # Paused for higher priority request
+    req1.generated_tokens.append(11)
+    req1.add_event_generated_token(11)
+    req1.generated_tokens.append(12)
+    req1.add_event_generated_token(12)
+    req1.add_event_evict()  # Memory pressure
+
+    # Second attempt: recovered, generates tokens, evicted again
+    req2 = DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3, 10, 11, 12], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=17),
+    )
+    req2.add_event_add_engine()
+    req2.add_event_add_context()
+    req2.generated_tokens.append(20)
+    req2.add_event_generated_token(20)
+    req2.add_event_pause()
+    req2.add_event_pause()  # Paused twice
+    req2.generated_tokens.append(21)
+    req2.add_event_generated_token(21)
+    req2.add_event_evict()
+
+    # Third attempt: finally completes
+    req3 = DynamicInferenceRequest(
+        request_id=1,
+        prompt_tokens=torch.tensor([1, 2, 3, 10, 11, 12, 20, 21], dtype=torch.int64),
+        sampling_params=SamplingParams(num_tokens_to_generate=15),
+    )
+    req3.add_event_add_engine()
+    req3.add_event_add_context()
+    req3.generated_tokens.extend([30, 31, 32])
+    req3.add_event_generated_token(30)
+    req3.add_event_generated_token(31)
+    req3.add_event_generated_token(32)
+    req3.add_event_finish()
+
+    # Create record with all checkpoints
+    record = DynamicInferenceRequestRecord()
+    record.requests.append(req1)
+    record.requests.append(req2)
+    record.requests.append(req3)
+
+    # Serialize and deserialize record
+    serialized = record.serialize()
+    assert len(serialized['requests']) == 3
+
+    # Merge all checkpoints
+    merged = record.merge()
+
+    # Verify all tokens collected
+    assert merged.generated_tokens == [10, 11, 12, 20, 21, 30, 31, 32]
+
+    # Count event types in merged history
+    event_types = [e.type for e in merged.events]
+    assert event_types.count(DynamicInferenceEventType.ADD_ENGINE) == 3
+    assert event_types.count(DynamicInferenceEventType.ADD_CONTEXT) == 3
+    assert event_types.count(DynamicInferenceEventType.EVICT) == 2
+    assert event_types.count(DynamicInferenceEventType.PAUSE) == 3
+    assert event_types.count(DynamicInferenceEventType.FINISH) == 1
+    assert event_types.count(DynamicInferenceEventType.GENERATED_TOKEN) == 8
+
+    # Total events: 7 from req1 + 7 from req2 + 6 from req3 = 20
+    assert len(merged.events) == 20
+
+    # Test serialization roundtrip of entire record
+    serialized = record.serialize()
+    assert len(serialized['requests']) == 3
+
+    # Verify all events are dicts (no compact mode)
+    first_request_events = serialized['requests'][0]['events']
+    assert all(isinstance(e, dict) for e in first_request_events)
+
+    # Verify GENERATED_TOKEN events use {"token_id": ...} payload format
+    token_events = [e for e in first_request_events if e['type'] == 'GENERATED_TOKEN']
+    assert token_events[0]['payload'] == {"token_id": 10}
+    assert token_events[1]['payload'] == {"token_id": 11}
+    assert token_events[2]['payload'] == {"token_id": 12}
diff --git a/tests/unit_tests/inference/engines/test_static_engine.py b/tests/unit_tests/inference/engines/test_static_engine.py
index 03b3712e39a..483a21d13bd 100644
--- a/tests/unit_tests/inference/engines/test_static_engine.py
+++ b/tests/unit_tests/inference/engines/test_static_engine.py
@@ -20,9 +20,6 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
@@ -85,20 +82,11 @@ def setup_engine(
         ).cuda()
         gpt_model.to(inference_config_params_dtype)
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=self.hidden_size,
-            inference_batch_times_seqlen_threshold=400,
-            inference_max_requests=self.batch_size,
-            fp32_residual_connection=False,
-            params_dtype=inference_config_params_dtype,
-            padded_vocab_size=self.vocab_size,
+        inference_context = StaticInferenceContext(
+            max_batch_size=self.batch_size, max_sequence_length=self.sequence_length
         )
 
-        inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
-
-        inference_wrapped_model = GPTInferenceWrapper(
-            gpt_model, inference_wrapper_config, inference_context
-        )
+        inference_wrapped_model = GPTInferenceWrapper(gpt_model, inference_context)
         self.mock_tokenizer = mock.Mock()
         # Set required tokenizer attributes before engine creation
         self.mock_tokenizer.vocab_size = self.vocab_size
@@ -200,8 +188,6 @@ def test_generate_dynamic(self, batch_size: int, num_trials: int, empty_prompt:
 
             assert len(results) == batch_size
             for result in results:
-                if isinstance(result, DynamicInferenceRequestRecord):
-                    result = result.merge()
                 assert isinstance(result, InferenceRequest), (
                     "expected <InferenceRequest>; found <%s>." % type(result).__name__
                 )
diff --git a/tests/unit_tests/inference/model_inference_wrappers/gpt/test_gpt_inference_wrapper.py b/tests/unit_tests/inference/model_inference_wrappers/gpt/test_gpt_inference_wrapper.py
index 07afebe1067..f49a67790e7 100644
--- a/tests/unit_tests/inference/model_inference_wrappers/gpt/test_gpt_inference_wrapper.py
+++ b/tests/unit_tests/inference/model_inference_wrappers/gpt/test_gpt_inference_wrapper.py
@@ -1,7 +1,5 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
-from argparse import Namespace
-
 import pytest
 import torch
 
@@ -10,13 +8,7 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
-from megatron.core.models.gpt.gpt_layer_specs import (
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
-)
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -53,27 +45,15 @@ def setup_model(self, tensor_parallel_size, pipeline_parallel_size):
             post_process=parallel_state.is_pipeline_last_stage(),
         ).cuda()
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=hidden_size,
-            inference_batch_times_seqlen_threshold=20,
-            inference_max_requests=self.batch_size,
-            fp32_residual_connection=False,
-            params_dtype=torch.float,
-            padded_vocab_size=self.vocab_size,
-        )
-
-        inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+        inference_context = StaticInferenceContext(self.batch_size, self.sequence_length)
 
-        self.inference_wrapped_model = GPTInferenceWrapper(
-            gpt_model, inference_wrapper_config, inference_context
-        )
+        self.inference_wrapped_model = GPTInferenceWrapper(gpt_model, inference_context)
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
 
-    # This will call the inference_wrapped_model.forward_pass_with_pipeline_parallel_small_input_batch()
     @pytest.mark.parametrize("materialize_only_last_token_logits", [True, False])
-    def test_inference_pipeline_parallel_small_size(self, materialize_only_last_token_logits):
+    def test_inference_pipeline_parallel(self, materialize_only_last_token_logits):
         self.setup_model(tensor_parallel_size=2, pipeline_parallel_size=2)
 
         batch_prompt_tokens = (
@@ -82,7 +62,7 @@ def test_inference_pipeline_parallel_small_size(self, materialize_only_last_toke
             .cuda()
         )
         self.inference_wrapped_model.prep_model_for_inference()
-        self.inference_wrapped_model.inference_context.materialize_only_last_token_logits = (
+        self.inference_wrapped_model.inference_context.config.materialize_only_last_token_logits = (
             materialize_only_last_token_logits
         )
 
@@ -107,42 +87,6 @@ def test_inference_pipeline_parallel_small_size(self, materialize_only_last_toke
                 self.vocab_size,
             ), f"Shape mismatch . Expected {(self.batch_size, logits_seq_len, self.vocab_size)}, but got {logits.shape}"
 
-    # This will call the inference_wrapped_model.forward_pass_with_pipeline_parallel_large_input_batch()
-    @pytest.mark.parametrize("materialize_only_last_token_logits", [True, False])
-    def test_inference_pipeline_parallel_large_size(self, materialize_only_last_token_logits):
-        self.setup_model(tensor_parallel_size=2, pipeline_parallel_size=2)
-
-        batch_prompt_tokens = (
-            torch.randint(low=0, high=self.vocab_size, size=(self.batch_size, self.sequence_length))
-            .int()
-            .cuda()
-        )
-        self.inference_wrapped_model.prep_model_for_inference()
-        self.inference_wrapped_model.inference_context.materialize_only_last_token_logits = (
-            materialize_only_last_token_logits
-        )
-
-        inference_input = self.inference_wrapped_model.prep_inference_input(
-            prompts_tokens=batch_prompt_tokens
-        )
-
-        inference_input_for_context_window = (
-            self.inference_wrapped_model.get_batch_for_context_window(inference_input, 0, 10)
-        )
-
-        logits_seq_len = 1 if materialize_only_last_token_logits else 10
-
-        logits = self.inference_wrapped_model.run_one_forward_step(
-            inference_input_for_context_window
-        )
-
-        if parallel_state.is_pipeline_last_stage():
-            assert logits.shape == (
-                self.batch_size,
-                logits_seq_len,
-                self.vocab_size,
-            ), f"Shape mismatch . Expected {(self.batch_size, logits_seq_len, self.vocab_size)}, but got {logits.shape}"
-
     @pytest.mark.parametrize("materialize_only_last_token_logits", [True, False])
     def test_inference_only_tensor_parallel(self, materialize_only_last_token_logits):
         self.setup_model(tensor_parallel_size=4, pipeline_parallel_size=1)
@@ -153,7 +97,7 @@ def test_inference_only_tensor_parallel(self, materialize_only_last_token_logits
             .cuda()
         )
         self.inference_wrapped_model.prep_model_for_inference()
-        self.inference_wrapped_model.inference_context.materialize_only_last_token_logits = (
+        self.inference_wrapped_model.inference_context.config.materialize_only_last_token_logits = (
             materialize_only_last_token_logits
         )
 
diff --git a/tests/unit_tests/inference/model_inference_wrappers/t5/test_t5_inference_wrapper.py b/tests/unit_tests/inference/model_inference_wrappers/t5/test_t5_inference_wrapper.py
index 36d5187b5eb..eb06f6ed78b 100644
--- a/tests/unit_tests/inference/model_inference_wrappers/t5/test_t5_inference_wrapper.py
+++ b/tests/unit_tests/inference/model_inference_wrappers/t5/test_t5_inference_wrapper.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 from argparse import Namespace
 from copy import deepcopy
 from unittest import mock
@@ -7,9 +9,6 @@
 
 from megatron.core import parallel_state
 from megatron.core.inference.contexts import StaticInferenceContext
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.model_inference_wrappers.t5.t5_inference_wrapper import (
     T5InferenceWrapper,
 )
@@ -77,19 +76,9 @@ def setup_model(self, tensor_parallel_size, pipeline_parallel_size):
             add_decoder=True,
         ).cuda()
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=hidden_size,
-            inference_batch_times_seqlen_threshold=-1,
-            fp32_residual_connection=False,
-            params_dtype=torch.float,
-            padded_vocab_size=self.vocab_size,
-        )
-
-        inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+        inference_context = StaticInferenceContext(max_batch_size=8, max_sequence_length=2560)
 
-        self.inference_wrapped_model = T5InferenceWrapper(
-            t5_model, inference_wrapper_config, inference_context
-        )
+        self.inference_wrapped_model = T5InferenceWrapper(t5_model, inference_context)
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/inference/model_inference_wrappers/test_model_inference_wrapper_config.py b/tests/unit_tests/inference/model_inference_wrappers/test_model_inference_wrapper_config.py
deleted file mode 100644
index 794634760d0..00000000000
--- a/tests/unit_tests/inference/model_inference_wrappers/test_model_inference_wrapper_config.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import torch
-
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
-
-
-class TestModelInferenceWrapperConfig:
-
-    def test_inference_config(self):
-        inference_config = InferenceWrapperConfig(
-            hidden_size=10,
-            inference_batch_times_seqlen_threshold=10,
-            padded_vocab_size=10,
-            params_dtype=torch.float,
-            fp32_residual_connection=False,
-        )
-        inference_config.add_attributes({"abc": 45})
-        assert (
-            inference_config.abc == 45
-        ), f"min tokens not set correctly. it is {inference_config.min_tokens}"
diff --git a/tests/unit_tests/inference/test_inference_config.py b/tests/unit_tests/inference/test_inference_config.py
new file mode 100644
index 00000000000..6d58328dade
--- /dev/null
+++ b/tests/unit_tests/inference/test_inference_config.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+import dataclasses
+
+from megatron.core.inference.config import InferenceConfig
+from megatron.core.transformer.transformer_config import TransformerConfig
+
+
+class TestInferenceConfig:
+    def test_mutual_exclusivity_with_transformer_config(self):
+        """
+        Ensure mutual exclusivity between fields in `InferenceConfig` and
+        `TransformerConfig`.
+        """
+        dynamic_inference_config_fields = set(dataclasses.fields(InferenceConfig))
+        transformer_config_fields = set(dataclasses.fields(TransformerConfig))
+        assert len(dynamic_inference_config_fields.intersection(transformer_config_fields)) == 0
diff --git a/tests/unit_tests/inference/test_wandb_logging.py b/tests/unit_tests/inference/test_wandb_logging.py
index cab464af503..1417926f13b 100644
--- a/tests/unit_tests/inference/test_wandb_logging.py
+++ b/tests/unit_tests/inference/test_wandb_logging.py
@@ -7,6 +7,7 @@
 import pytest
 import torch
 
+from megatron.core.inference.config import InferenceConfig
 from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
 from megatron.core.inference.engines import DynamicInferenceEngine
 from megatron.core.inference.inference_request import DynamicInferenceRequest
@@ -15,6 +16,7 @@
     TextGenerationController,
 )
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.transformer_config import TransformerConfig
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -50,20 +52,26 @@ def _get_dynamic_context(
         max_sequence_length=512,
         buffer_size_gb=0.03,
         block_size_tokens=128,
+        logging_step_interval=0,
         metrics_writer=None,
     ):
         """Helper to create a DynamicInferenceContext."""
         return DynamicInferenceContext(
-            params_dtype=params_dtype,
-            num_layers=num_layers,
-            kv_channels=kv_channels,
-            num_attention_heads=num_attention_heads,
-            max_sequence_length=max_sequence_length,
-            num_cuda_graphs=None,
-            buffer_size_gb=buffer_size_gb,
-            block_size_tokens=block_size_tokens,
-            metrics_writer=metrics_writer,
-            unified_memory_level=0,  # unit tests currently broken with UVM
+            model_config=TransformerConfig(
+                params_dtype=params_dtype,
+                num_layers=num_layers,
+                kv_channels=kv_channels,
+                num_attention_heads=num_attention_heads,
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=max_sequence_length,
+                num_cuda_graphs=None,
+                buffer_size_gb=buffer_size_gb,
+                block_size_tokens=block_size_tokens,
+                unified_memory_level=0,  # unit tests currently broken with UVM
+                logging_step_interval=logging_step_interval,
+                metrics_writer=metrics_writer,
+            ),
         )
 
     @pytest.mark.internal
@@ -195,12 +203,14 @@ def test_kvcache_utilization_stats_types(self):
     @pytest.mark.internal
     @patch('megatron.core.inference.engines.dynamic_engine.HAVE_WANDB', True)
     def test_engine_logging_step_interval_zero(self):
-        """Test that no logging occurs when inference_logging_step_interval is 0."""
+        """Test that no logging occurs when logging_step_interval is 0."""
         mock_wandb = Mock()
         mock_wandb.__name__ = "wandb"
         mock_wandb.log = Mock()
 
-        dynamic_context = self._get_dynamic_context(metrics_writer=mock_wandb)
+        dynamic_context = self._get_dynamic_context(
+            logging_step_interval=0, metrics_writer=mock_wandb
+        )
 
         # Create mock controller with proper spec to pass isinstance checks
         mock_controller = create_autospec(TextGenerationController, instance=True)
@@ -210,12 +220,7 @@ def test_engine_logging_step_interval_zero(self):
         mock_controller.inference_wrapped_model.model.config = Mock()
         mock_controller.inference_wrapped_model.model.config.cuda_graph_impl = "none"
 
-        engine = DynamicInferenceEngine(
-            controller=mock_controller,
-            context=dynamic_context,
-            random_seed=123,
-            inference_logging_step_interval=0,  # Disabled
-        )
+        engine = DynamicInferenceEngine(controller=mock_controller, context=dynamic_context)
 
         # Verify log was never called
         mock_wandb.log.assert_not_called()
@@ -225,15 +230,16 @@ def test_paused_requests_in_stats(self):
         """Test that paused requests are correctly reflected in stats."""
         set_rounder(1)
         dynamic_context = DynamicInferenceContext(
-            params_dtype=torch.float32,
-            num_layers=2,
-            kv_channels=64,
-            num_attention_heads=8,
-            max_sequence_length=128,
-            num_cuda_graphs=None,
-            buffer_size_gb=0.01,  # Small buffer to force pausing
-            block_size_tokens=32,
-            unified_memory_level=0,  # unit tests currently broken with UVM
+            model_config=TransformerConfig(
+                params_dtype=torch.float32, num_layers=2, kv_channels=64, num_attention_heads=8
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=128,
+                num_cuda_graphs=None,
+                buffer_size_gb=0.01,  # Small buffer to force pausing
+                block_size_tokens=32,
+                unified_memory_level=0,  # unit tests currently broken with UVM
+            ),
         )
 
         # Add multiple requests to potentially trigger pausing
@@ -257,7 +263,7 @@ def test_paused_requests_in_stats(self):
     @pytest.mark.internal
     def test_metrics_writer_none_handling(self):
         """Test that engine handles None metrics_writer gracefully."""
-        dynamic_context = self._get_dynamic_context(metrics_writer=None)
+        dynamic_context = self._get_dynamic_context(logging_step_interval=10, metrics_writer=None)
 
         # Create mock controller with proper spec to pass isinstance checks
         mock_controller = create_autospec(TextGenerationController, instance=True)
@@ -268,13 +274,8 @@ def test_metrics_writer_none_handling(self):
         mock_controller.inference_wrapped_model.model.config.cuda_graph_impl = "none"
 
         # Should not raise error even with logging interval set
-        engine = DynamicInferenceEngine(
-            controller=mock_controller,
-            context=dynamic_context,
-            random_seed=123,
-            inference_logging_step_interval=10,
-        )
+        engine = DynamicInferenceEngine(controller=mock_controller, context=dynamic_context)
 
         # Verify engine was created successfully
-        assert engine.inference_logging_step_interval == 10
-        assert engine.context.metrics_writer is None
+        assert engine.logging_step_interval == 10
+        assert engine.metrics_writer is None
diff --git a/tests/unit_tests/inference/text_generation_controllers/test_encoder_decoder_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_encoder_decoder_text_generation_controller.py
index 93a208710fc..5bd39ec1324 100644
--- a/tests/unit_tests/inference/text_generation_controllers/test_encoder_decoder_text_generation_controller.py
+++ b/tests/unit_tests/inference/text_generation_controllers/test_encoder_decoder_text_generation_controller.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 import random
 import string
 import time
@@ -12,9 +14,6 @@
 
 from megatron.core.inference.contexts import StaticInferenceContext
 from megatron.core.inference.inference_request import InferenceRequest, Status
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.model_inference_wrappers.t5.t5_inference_wrapper import (
     T5InferenceWrapper,
 )
@@ -85,19 +84,9 @@ def setup_method(self, method):
             add_decoder=True,
         ).cuda()
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=hidden_size,
-            inference_batch_times_seqlen_threshold=-1,
-            fp32_residual_connection=False,
-            params_dtype=torch.float,
-            padded_vocab_size=self.vocab_size,
-        )
-
-        inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+        inference_context = StaticInferenceContext(max_batch_size=8, max_sequence_length=2560)
 
-        inference_wrapped_model = T5InferenceWrapper(
-            t5_model, inference_wrapper_config, inference_context
-        )
+        inference_wrapped_model = T5InferenceWrapper(t5_model, inference_context)
 
         self.mock_tokenizer = mock.Mock()
 
diff --git a/tests/unit_tests/inference/text_generation_controllers/test_simple_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
similarity index 96%
rename from tests/unit_tests/inference/text_generation_controllers/test_simple_text_generation_controller.py
rename to tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
index 0885401e7a0..bdf95c2d9bf 100644
--- a/tests/unit_tests/inference/text_generation_controllers/test_simple_text_generation_controller.py
+++ b/tests/unit_tests/inference/text_generation_controllers/test_text_generation_controller.py
@@ -14,6 +14,7 @@
 from transformer_engine.pytorch.fp8 import check_fp8_support
 
 from megatron.core import parallel_state
+from megatron.core.inference.config import InferenceConfig
 from megatron.core.inference.contexts import DynamicInferenceContext, StaticInferenceContext
 from megatron.core.inference.contexts.dynamic_context import MaxSequenceLengthOverflowError
 from megatron.core.inference.inference_request import (
@@ -24,9 +25,6 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
@@ -100,37 +98,24 @@ def setup_model(
         if dtype == torch.bfloat16:
             gpt_model = Float16Module(gpt_model.config, gpt_model)
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=self.hidden_size,
-            inference_batch_times_seqlen_threshold=-1,
-            inference_max_seq_length=2048,
-            inference_max_requests=16 if fp8 else self.batch_size,
-            fp32_residual_connection=False,
-            params_dtype=dtype,
-            padded_vocab_size=self.vocab_size,
-        )
-
         if static:
-            inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+            inference_context = StaticInferenceContext(
+                max_batch_size=16 if fp8 else self.batch_size, max_sequence_length=2048
+            )
         else:
             inference_context = DynamicInferenceContext(
-                params_dtype=dtype,
-                num_layers=transformer_config.num_layers // pipeline_model_parallel_size,
-                kv_channels=transformer_config.kv_channels,
-                num_attention_heads=transformer_config.num_attention_heads,
-                tensor_model_parallel_size=transformer_config.tensor_model_parallel_size,
-                pipeline_model_parallel_size=transformer_config.pipeline_model_parallel_size,
-                max_sequence_length=2048,
-                buffer_size_gb=0.2,
-                materialize_only_last_token_logits=False,
-                use_flashinfer_fused_rope=None,  # default to using flash-infer if available
-                # this is for compatibility with the LTS environment
-                unified_memory_level=0,  # unit tests currently broken with UVM
+                model_config=transformer_config,
+                inference_config=InferenceConfig(
+                    max_sequence_length=2048,
+                    buffer_size_gb=0.2,
+                    materialize_only_last_token_logits=False,
+                    use_flashinfer_fused_rope=None,  # default to using flash-infer if available
+                    # this is for compatibility with the LTS environment
+                    unified_memory_level=0,  # unit tests currently broken with UVM
+                ),
             )
 
-        inference_wrapped_model = GPTInferenceWrapper(
-            gpt_model, inference_wrapper_config, inference_context
-        )
+        inference_wrapped_model = GPTInferenceWrapper(gpt_model, inference_context)
 
         inference_wrapped_model.model_is_pipeline_parallel = not (
             parallel_state.is_pipeline_first_stage() and parallel_state.is_pipeline_last_stage()
diff --git a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py
index 31bf415ba56..5344ae9f8eb 100644
--- a/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py
+++ b/tests/unit_tests/inference/text_generation_controllers/test_vlm_text_generation_controller.py
@@ -1,11 +1,11 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 import copy
-import os
 import random
 import string
 import time
-from argparse import Namespace
 from collections import OrderedDict
-from typing import Dict
+from typing import Dict, List
 from unittest import mock
 
 import pytest
@@ -13,9 +13,6 @@
 
 from megatron.core.inference.contexts import StaticInferenceContext
 from megatron.core.inference.inference_request import InferenceRequest, Status, VLMInferenceRequest
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.model_inference_wrappers.multimodal.vlm_inference_wrapper import (
     VLMInferenceWrapper,
 )
@@ -23,12 +20,13 @@
 from megatron.core.inference.text_generation_controllers.vlm_text_generation_controller import (
     VLMTextGenerationController,
 )
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.models.multimodal.llava_model import LLaVAModel
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
-from megatron.core.transformer.enums import AttnBackend
 from megatron.core.transformer.module import Float16Module
+from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayer
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -69,15 +67,19 @@ def setup_method(self, method):
             bf16=True,
         )
 
-        language_layer_spec = get_gpt_layer_local_spec()
-        vision_layer_spec = copy.deepcopy(language_layer_spec)
-        vision_projection_spec = copy.deepcopy(language_layer_spec.submodules.mlp.submodules)
+        language_layer_submodules = get_gpt_layer_local_submodules()
+        vision_layer_spec = ModuleSpec(
+            module=TransformerLayer, submodules=copy.deepcopy(language_layer_submodules)
+        )
+        vision_projection_spec = copy.deepcopy(language_layer_submodules.mlp.submodules)
 
         language_config.language_model_type = "dummy"
         vision_config.vision_model_type = "clip"
         self.model = LLaVAModel(
             language_transformer_config=language_config,
-            language_transformer_layer_spec=language_layer_spec,
+            language_transformer_layer_spec=ModuleSpec(
+                module=TransformerLayer, submodules=language_layer_submodules
+            ),
             language_vocab_size=self.language_vocab_size,
             language_max_sequence_length=self.language_max_sequence_length,
             vision_transformer_config=vision_config,
@@ -92,19 +94,9 @@ def setup_method(self, method):
         self.image_token_index = self.model.image_token_index
         self.model = Float16Module(self.model.config, self.model)
 
-        inference_wrapper_config = InferenceWrapperConfig(
-            hidden_size=self.language_hidden_size,
-            inference_batch_times_seqlen_threshold=-1,
-            fp32_residual_connection=False,
-            params_dtype=torch.float,
-            padded_vocab_size=self.language_vocab_size,
-        )
-
-        inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+        inference_context = StaticInferenceContext(max_batch_size=8, max_sequence_length=2560)
 
-        inference_wrapped_model = VLMInferenceWrapper(
-            self.model, inference_wrapper_config, inference_context
-        )
+        inference_wrapped_model = VLMInferenceWrapper(self.model, inference_context)
 
         self.mock_tokenizer = mock.Mock()
 
diff --git a/tests/unit_tests/models/test_bert_model.py b/tests/unit_tests/models/test_bert_model.py
index c878fd4d3b5..db7b8255776 100644
--- a/tests/unit_tests/models/test_bert_model.py
+++ b/tests/unit_tests/models/test_bert_model.py
@@ -1,21 +1,22 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 import os
-from importlib.metadata import version
 
 import pytest
 import torch
 from packaging.version import Version as PkgVersion
-from pytest_mock import mocker
 
 from megatron.core.models.bert.bert_layer_specs import (
     bert_layer_local_spec,
-    bert_layer_with_transformer_engine_spec,
+    get_bert_layer_with_transformer_engine_spec,
+    get_bert_layer_with_transformer_engine_submodules,
 )
 from megatron.core.models.bert.bert_model import BertModel
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.enums import AttnBackend, AttnMaskType
+from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayer
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -40,7 +41,7 @@ def setup_method(self, method):
         self.bert_model = BertModel(
             config=transformer_config,
             num_tokentypes=0,
-            transformer_layer_spec=bert_layer_with_transformer_engine_spec,
+            transformer_layer_spec=get_bert_layer_with_transformer_engine_spec(),
             vocab_size=100,
             max_sequence_length=4,
         )
@@ -112,7 +113,7 @@ def setup_method(self, method):
         self.bert_model = BertModel(
             config=self.transformer_config,
             num_tokentypes=0,
-            transformer_layer_spec=bert_layer_with_transformer_engine_spec,
+            transformer_layer_spec=get_bert_layer_with_transformer_engine_spec(),
             vocab_size=100,
             max_sequence_length=4,
         )
@@ -139,16 +140,15 @@ def test_local_spec_exception(self, mocker):
 
     @pytest.mark.internal
     def test_transformer_engine_version_1_10(self, mocker):
-        bert_layer_with_transformer_engine_spec.submodules.self_attention.params[
-            'attn_mask_type'
-        ] == AttnMaskType.arbitrary
+        submodules = get_bert_layer_with_transformer_engine_submodules()
+        submodules.self_attention.params['attn_mask_type'] = AttnMaskType.arbitrary
 
         mocker.patch("megatron.core.utils.get_te_version", return_value=PkgVersion("1.10"))
-        self.bert_model.transformer_layer_spec = bert_layer_with_transformer_engine_spec
+        self.bert_model.transformer_layer_spec = ModuleSpec(
+            module=TransformerLayer, submodules=submodules
+        )
         attn_mask_dimensions = self.bert_model._sanity_check_attention_and_get_attn_mask_dimension()
-        attn_mask_type = self.bert_model.transformer_layer_spec.submodules.self_attention.params[
-            'attn_mask_type'
-        ]
+        attn_mask_type = submodules.self_attention.params['attn_mask_type']
         assert (
             attn_mask_type == AttnMaskType.padding
         ), f"Exepcted attn mask type to be padding, but got {attn_mask_type}"
@@ -160,7 +160,7 @@ def test_transformer_engine_version_1_10(self, mocker):
     def test_transformer_engine_version_1_7_to_1_10_flash_attn(self, mocker):
         self.bert_model.config.attention_backend = AttnBackend.flash
         mocker.patch("megatron.core.utils.get_te_version", return_value=PkgVersion("1.8"))
-        self.bert_model.transformer_layer_spec = bert_layer_with_transformer_engine_spec
+        self.bert_model.transformer_layer_spec = get_bert_layer_with_transformer_engine_spec()
         attn_mask_dimensions = self.bert_model._sanity_check_attention_and_get_attn_mask_dimension()
         assert (
             attn_mask_dimensions == "b11s"
@@ -170,15 +170,14 @@ def test_transformer_engine_version_1_7_to_1_10_flash_attn(self, mocker):
     @pytest.mark.flaky
     @pytest.mark.flaky_in_dev
     def test_transformer_engine_version_1_7_to_1_10_rng_error(self, mocker):
-        bert_layer_with_transformer_engine_spec.submodules.self_attention.params[
-            'attn_mask_type'
-        ] == AttnMaskType.padding
+        submodules = get_bert_layer_with_transformer_engine_submodules()
+        submodules.self_attention.params['attn_mask_type'] = AttnMaskType.padding
         mocker.patch("megatron.core.utils.get_te_version", return_value=PkgVersion("1.8"))
         with pytest.raises(Exception) as exc_info:
             self.bert_model = BertModel(
                 config=self.transformer_config,
                 num_tokentypes=0,
-                transformer_layer_spec=bert_layer_with_transformer_engine_spec,
+                transformer_layer_spec=ModuleSpec(module=TransformerLayer, submodules=submodules),
                 vocab_size=100,
                 max_sequence_length=4,
             )
@@ -191,15 +190,14 @@ def test_transformer_engine_version_1_7_to_1_10_rng_error(self, mocker):
     @pytest.mark.internal
     def test_transformer_engine_version_1_7_to_1_10_unfused_attention(self, mocker):
         self.bert_model.config.attention_backend = AttnBackend.unfused
-        bert_layer_with_transformer_engine_spec.submodules.self_attention.params[
-            'attn_mask_type'
-        ] == AttnMaskType.padding
+        submodules = get_bert_layer_with_transformer_engine_submodules()
+        submodules.self_attention.params['attn_mask_type'] = AttnMaskType.padding
         mocker.patch("megatron.core.utils.get_te_version", return_value=PkgVersion("1.8"))
-        self.bert_model.transformer_layer_spec = bert_layer_with_transformer_engine_spec
+        self.bert_model.transformer_layer_spec = ModuleSpec(
+            module=TransformerLayer, submodules=submodules
+        )
         attn_mask_dimensions = self.bert_model._sanity_check_attention_and_get_attn_mask_dimension()
-        attn_mask_type = self.bert_model.transformer_layer_spec.submodules.self_attention.params[
-            'attn_mask_type'
-        ]
+        attn_mask_type = submodules.self_attention.params['attn_mask_type']
         assert (
             attn_mask_type == AttnMaskType.arbitrary
         ), f"Exepcted attn mask type to be arbitrary, but got {attn_mask_type}"
@@ -218,7 +216,7 @@ def test_transformer_engine_version_less_than_1_7(self, mocker):
             self.bert_model = BertModel(
                 config=self.transformer_config,
                 num_tokentypes=0,
-                transformer_layer_spec=bert_layer_with_transformer_engine_spec,
+                transformer_layer_spec=get_bert_layer_with_transformer_engine_spec(),
                 vocab_size=100,
                 max_sequence_length=4,
             )
diff --git a/tests/unit_tests/models/test_gpt_model.py b/tests/unit_tests/models/test_gpt_model.py
index cf3bd40ee4b..87aba9c6ed9 100644
--- a/tests/unit_tests/models/test_gpt_model.py
+++ b/tests/unit_tests/models/test_gpt_model.py
@@ -12,6 +12,7 @@
 
 from megatron.core import parallel_state
 from megatron.core.hyper_comm_grid import HyperCommGrid
+from megatron.core.inference.config import InferenceConfig
 from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
 from megatron.core.inference.inference_request import DynamicInferenceRequest
 from megatron.core.inference.sampling_params import SamplingParams
@@ -392,14 +393,18 @@ def test_dynamic_inference_padding_with_fp8(self):
         config = self.gpt_model.config
 
         inference_context = DynamicInferenceContext(
-            params_dtype=config.params_dtype,
-            num_layers=config.num_layers,
-            kv_channels=config.hidden_size // config.num_attention_heads,
-            num_attention_heads=config.num_attention_heads,
-            max_sequence_length=self.gpt_model.module.max_sequence_length,
-            buffer_size_gb=1.0,
-            block_size_tokens=256,
-            materialize_only_last_token_logits=False,
+            model_config=TransformerConfig(
+                params_dtype=config.params_dtype,
+                num_layers=config.num_layers,
+                kv_channels=config.hidden_size // config.num_attention_heads,
+                num_attention_heads=config.num_attention_heads,
+            ),
+            inference_config=InferenceConfig(
+                max_sequence_length=self.gpt_model.module.max_sequence_length,
+                buffer_size_gb=1.0,
+                block_size_tokens=256,
+                materialize_only_last_token_logits=False,
+            ),
         )
 
         # Add a request with 10 tokens. Since 10 is not a multiple of 64,
diff --git a/tests/unit_tests/models/test_gpt_model_batch_invariant.py b/tests/unit_tests/models/test_gpt_model_batch_invariant.py
index ead9125e5ec..9ab7e445c0d 100644
--- a/tests/unit_tests/models/test_gpt_model_batch_invariant.py
+++ b/tests/unit_tests/models/test_gpt_model_batch_invariant.py
@@ -5,17 +5,15 @@
 import torch
 import torch.distributed as dist
 
+from megatron.core.inference.config import InferenceConfig
 from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
 from megatron.core.inference.engines.dynamic_engine import DynamicInferenceEngine
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
-from megatron.core.inference.text_generation_controllers.simple_text_generation_controller import (
-    SimpleTextGenerationController,
+from megatron.core.inference.text_generation_controllers.text_generation_controller import (
+    TextGenerationController,
 )
 from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
 from megatron.core.models.gpt.gpt_model import GPTModel
@@ -91,6 +89,8 @@ def _build_flash_attn_bik_model(seq_len: int, vocab_size: int, hidden_size: int
         normalization="RMSNorm",
         params_dtype=torch.bfloat16,
         attention_backend=AttnBackend.flash,
+        fp32_residual_connection=False,
+        nccl_all_reduce_for_prefill=False,
     )
     cfg.fp16 = False
     cfg.bf16 = True
@@ -184,32 +184,21 @@ def test_dynamic_engine_matches_batched_forward_rl(self):
         inference_model = Float16Module(base_model.config, base_model).cuda().eval()
 
         ctx = DynamicInferenceContext(
-            params_dtype=torch.bfloat16,
-            num_layers=base_model.config.num_layers,
-            kv_channels=base_model.config.kv_channels,
-            num_attention_heads=base_model.config.num_attention_heads,
-            max_sequence_length=seq_len,
-            buffer_size_gb=0.125,
-            block_size_tokens=16,
-            num_cuda_graphs=None,
-            materialize_only_last_token_logits=False,
-            use_cuda_graphs_for_non_decode_steps=False,
-            unified_memory_level=0,
+            model_config=base_model.config,
+            inference_config=InferenceConfig(
+                max_sequence_length=seq_len,
+                buffer_size_gb=0.125,
+                block_size_tokens=16,
+                num_cuda_graphs=None,
+                materialize_only_last_token_logits=False,
+                use_cuda_graphs_for_non_decode_steps=False,
+                unified_memory_level=0,
+            ),
         )
 
-        wrapper_cfg = InferenceWrapperConfig(
-            hidden_size=base_model.config.hidden_size,
-            inference_batch_times_seqlen_threshold=-1,
-            fp32_residual_connection=False,
-            params_dtype=torch.bfloat16,
-            padded_vocab_size=vocab_size,
-            inference_max_seq_length=seq_len,
-            inference_max_requests=8,
-            nccl_all_reduce_for_prefill=False,
-        )
-        wrapper = GPTInferenceWrapper(inference_model, wrapper_cfg, ctx)
+        wrapper = GPTInferenceWrapper(inference_model, ctx)
         tokenizer = DummyTokenizer(vocab_size=vocab_size, bos=None, eod=vocab_size - 1, pad=0)
-        controller = SimpleTextGenerationController(wrapper, tokenizer)
+        controller = TextGenerationController(wrapper, tokenizer)
         engine = DynamicInferenceEngine(
             controller=controller, context=ctx, enable_cuda_graph=False, random_seed=123
         )
@@ -273,32 +262,21 @@ def test_dynamic_engine_is_batch_invariant(self):
 
         def _run_engine_with_order(order):
             ctx = DynamicInferenceContext(
-                params_dtype=torch.bfloat16,
-                num_layers=base_model.config.num_layers,
-                kv_channels=base_model.config.kv_channels,
-                num_attention_heads=base_model.config.num_attention_heads,
-                max_sequence_length=seq_len,
-                buffer_size_gb=0.125,
-                block_size_tokens=16,
-                num_cuda_graphs=None,
-                materialize_only_last_token_logits=False,
-                use_cuda_graphs_for_non_decode_steps=False,
-                unified_memory_level=0,
+                model_config=based_model.config,
+                inference_config=InferenceConfig(
+                    max_sequence_length=seq_len,
+                    buffer_size_gb=0.125,
+                    block_size_tokens=16,
+                    num_cuda_graphs=None,
+                    materialize_only_last_token_logits=False,
+                    use_cuda_graphs_for_non_decode_steps=False,
+                    unified_memory_level=0,
+                ),
             )
 
-            wrapper_cfg = InferenceWrapperConfig(
-                hidden_size=base_model.config.hidden_size,
-                inference_batch_times_seqlen_threshold=-1,
-                fp32_residual_connection=False,
-                params_dtype=torch.bfloat16,
-                padded_vocab_size=vocab_size,
-                inference_max_seq_length=seq_len,
-                inference_max_requests=8,
-                nccl_all_reduce_for_prefill=False,
-            )
-            wrapper = GPTInferenceWrapper(inference_model, wrapper_cfg, ctx)
+            wrapper = GPTInferenceWrapper(inference_model, ctx)
             tokenizer = DummyTokenizer(vocab_size=vocab_size, bos=None, eod=vocab_size - 1, pad=0)
-            controller = SimpleTextGenerationController(wrapper, tokenizer)
+            controller = TextGenerationController(wrapper, tokenizer)
             engine = DynamicInferenceEngine(
                 controller=controller, context=ctx, enable_cuda_graph=False, random_seed=123
             )
diff --git a/tests/unit_tests/models/test_llava_model.py b/tests/unit_tests/models/test_llava_model.py
index cee6d2b0b27..970b0e54635 100644
--- a/tests/unit_tests/models/test_llava_model.py
+++ b/tests/unit_tests/models/test_llava_model.py
@@ -6,16 +6,18 @@
 import pytest
 import torch
 
-from megatron.core import parallel_state as ps
 from megatron.core.inference.contexts import StaticInferenceContext
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.models.multimodal import context_parallel
 from megatron.core.models.multimodal.llava_model import LLaVAModel
-from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.enums import AttnMaskType
+from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayer
 from megatron.core.utils import is_te_min_version
 from megatron.training.global_vars import set_args
 from tests.unit_tests.test_utilities import Utils
@@ -47,15 +49,19 @@ def setup_method(self, method):
             use_cpu_initialization=False,
         )
 
-        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
-        vision_layer_spec = deepcopy(language_layer_spec)
-        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
+        language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules()
+        vision_layer_spec = ModuleSpec(
+            module=TransformerLayer, submodules=deepcopy(language_layer_submodules)
+        )
+        vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules)
 
         language_config.language_model_type = "dummy"
         vision_config.vision_model_type = "clip"
         self.model = LLaVAModel(
             language_transformer_config=language_config,
-            language_transformer_layer_spec=language_layer_spec,
+            language_transformer_layer_spec=ModuleSpec(
+                module=TransformerLayer, submodules=language_layer_submodules
+            ),
             language_vocab_size=8192,
             language_max_sequence_length=4096,
             vision_transformer_config=vision_config,
@@ -481,16 +487,20 @@ def setup_and_teardown_llava_model(request):
         use_cpu_initialization=False,
     )
 
-    language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
-    vision_layer_spec = deepcopy(language_layer_spec)
-    vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
+    language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules()
+    vision_layer_spec = ModuleSpec(
+        module=TransformerLayer, submodules=deepcopy(language_layer_submodules)
+    )
+    vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules)
 
     language_config.language_model_type = "dummy"
     vision_model_type = request.param
     vision_config.vision_model_type = vision_model_type
     model = LLaVAModel(
         language_transformer_config=language_config,
-        language_transformer_layer_spec=language_layer_spec,
+        language_transformer_layer_spec=ModuleSpec(
+            module=TransformerLayer, submodules=language_layer_submodules
+        ),
         language_vocab_size=2048,
         language_max_sequence_length=4096,
         vision_transformer_config=vision_config,
@@ -573,31 +583,33 @@ def _init_llava_model(self, cp_size, tp_size, sequence_parallel):
             context_parallel_size=1,
         )
 
-        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
+        language_layer_submodules = get_gpt_layer_with_transformer_engine_submodules()
         # SP/CP either requires user to ensure token lengths do not require padding OR change mask type to padding
         if (
-            language_layer_spec.submodules.self_attention.params.get('attn_mask_type', '')
+            language_layer_submodules.self_attention.params.get('attn_mask_type', '')
             == AttnMaskType.causal
         ):
-            language_layer_spec.submodules.self_attention.params['attn_mask_type'] = (
+            language_layer_submodules.self_attention.params['attn_mask_type'] = (
                 AttnMaskType.padding_causal
             )
         elif (
-            language_layer_spec.submodules.self_attention.params.get('attn_mask_type', '')
+            language_layer_submodules.self_attention.params.get('attn_mask_type', '')
             == AttnMaskType.no_mask
         ):
-            language_layer_spec.submodules.self_attention.params['attn_mask_type'] = (
-                AttnMaskType.padding
-            )
+            language_layer_submodules.self_attention.params['attn_mask_type'] = AttnMaskType.padding
 
-        vision_layer_spec = deepcopy(language_layer_spec)
-        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
+        vision_layer_spec = ModuleSpec(
+            module=TransformerLayer, submodules=deepcopy(language_layer_submodules)
+        )
+        vision_projection_spec = deepcopy(language_layer_submodules.mlp.submodules)
 
         language_config.language_model_type = "dummy"
         vision_config.vision_model_type = "clip"
         model = LLaVAModel(
             language_transformer_config=language_config,
-            language_transformer_layer_spec=language_layer_spec,
+            language_transformer_layer_spec=ModuleSpec(
+                module=TransformerLayer, submodules=language_layer_submodules
+            ),
             language_vocab_size=8192,
             language_max_sequence_length=4096,
             vision_transformer_config=vision_config,
diff --git a/tests/unit_tests/models/test_mamba_model.py b/tests/unit_tests/models/test_mamba_model.py
index 9eb7b2dea9a..29e3630d7bb 100644
--- a/tests/unit_tests/models/test_mamba_model.py
+++ b/tests/unit_tests/models/test_mamba_model.py
@@ -10,6 +10,7 @@
 
 from megatron.core import parallel_state
 from megatron.core.hyper_comm_grid import HyperCommGrid
+from megatron.core.inference.config import InferenceConfig, MambaInferenceStateConfig
 from megatron.core.inference.contexts import BaseInferenceContext, StaticInferenceContext
 from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
 from megatron.core.inference.inference_request import DynamicInferenceRequest
@@ -21,12 +22,7 @@
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import AttnBackend
 from megatron.core.transformer.module import Float16Module
-from megatron.core.utils import (
-    divide,
-    get_mamba_inference_state_config_from_model,
-    is_fa_min_version,
-    is_torch_min_version,
-)
+from megatron.core.utils import divide, is_fa_min_version, is_torch_min_version
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -344,20 +340,17 @@ def test_dynamic_inference_padding_with_fp8(self):
         self.model.eval()
         config = self.model.config
 
-        mamba_inference_state_config = get_mamba_inference_state_config_from_model(
-            self.model.module
-        )
+        mamba_inference_state_config = MambaInferenceStateConfig.from_model(self.model.module)
 
         inference_context = DynamicInferenceContext(
-            params_dtype=config.params_dtype,
-            num_layers=config.num_layers,
-            kv_channels=config.hidden_size // config.num_attention_heads,
-            num_attention_heads=config.num_attention_heads,
-            max_sequence_length=self.model.module.max_sequence_length,
-            buffer_size_gb=1.0,
-            block_size_tokens=256,
-            materialize_only_last_token_logits=False,
-            mamba_inference_state_config=mamba_inference_state_config,
+            model_config=self.model.config,
+            inference_config=InferenceConfig(
+                max_sequence_length=self.model.module.max_sequence_length,
+                buffer_size_gb=1.0,
+                block_size_tokens=256,
+                materialize_only_last_token_logits=False,
+                mamba_inference_state_config=mamba_inference_state_config,
+            ),
         )
 
         # Add a request with 10 tokens. Since 10 is not a multiple of 64 (TOKEN_ROUNDER),
diff --git a/tests/unit_tests/models/test_mamba_moe_model.py b/tests/unit_tests/models/test_mamba_moe_model.py
index 2481649bc3f..39b4a18e243 100644
--- a/tests/unit_tests/models/test_mamba_moe_model.py
+++ b/tests/unit_tests/models/test_mamba_moe_model.py
@@ -37,7 +37,6 @@
     "apply_query_key_layer_scaling": False,
     "apply_residual_connection_post_layernorm": False,
     "apply_rope_fusion": False,
-    "async_tensor_model_parallel_allreduce": True,
     "attention_backend": {
         "__objclass__": "megatron.core.transformer.enums.AttnBackend",
         "_name_": "flash",
@@ -156,7 +155,6 @@
     "moe_deepep_num_sms": 20,
     "moe_enable_deepep": False,
     "moe_expert_capacity_factor": None,
-    "moe_extended_tp": False,
     "moe_ffn_hidden_size": 1856,
     "moe_flex_dispatcher_backend": "deepep",
     "moe_grouped_gemm": True,
@@ -166,6 +164,7 @@
     "moe_layer_freq": 1,
     "moe_layer_recompute": False,
     "moe_pad_expert_input_to_capacity": False,
+    "moe_pad_experts_for_cuda_graph_inference": False,
     "moe_per_layer_logging": False,
     "moe_permute_fusion": False,
     "moe_router_bias_update_rate": 0.001,
@@ -193,10 +192,13 @@
     "moe_z_loss_coeff": None,
     "moe_enable_routing_replay": False,
     "mrope_section": None,
+    "mtp_hybrid_override_pattern": None,
     "mtp_loss_scaling_factor": 0.1,
     "mtp_num_layers": None,
     "mtp_standalone": False,
+    "mtp_use_repeated_layer": False,
     "multi_latent_attention": False,
+    "nccl_all_reduce_for_prefill": False,
     "no_rope_freq": None,
     "no_sync_func": None,
     "normalization": "RMSNorm",
@@ -437,7 +439,6 @@ def create_test_args(self):
         args.padded_vocab_size = 131072
 
         # The following args would be set in the user's nano v3 config.
-        args.async_tensor_model_parallel_allreduce = True
         args.attention_backend = AttnBackend.flash
         args.bf16 = True
         args.ckpt_format = 'torch_dist'
diff --git a/tests/unit_tests/post_training/test_modelopt_module_spec.py b/tests/unit_tests/post_training/test_modelopt_module_spec.py
index dac96785bc0..3f6491f835d 100644
--- a/tests/unit_tests/post_training/test_modelopt_module_spec.py
+++ b/tests/unit_tests/post_training/test_modelopt_module_spec.py
@@ -272,9 +272,14 @@ def test_get_mamba_stack_modelopt_spec_interface():
     expected_params = {
         "local_core_attention": inspect.Parameter.POSITIONAL_OR_KEYWORD,
         "remap_te_layernorm": inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        "use_default_te_spec": inspect.Parameter.POSITIONAL_OR_KEYWORD,
     }
 
-    expected_defaults = {"local_core_attention": False, "remap_te_layernorm": False}
+    expected_defaults = {
+        "local_core_attention": False,
+        "remap_te_layernorm": False,
+        "use_default_te_spec": False,
+    }
 
     # Check expected parameters are in function signature
     for param_name, param_kind in expected_params.items():
@@ -291,3 +296,9 @@ def test_get_mamba_stack_modelopt_spec_interface():
         assert (
             k in sig_defaults and v == sig_defaults[k]
         ), f"Default value of {sig_defaults[k]} does not match the expected value of {v} for parameter {k}."
+
+
+def test_get_mamba_stack_modelopt_spec_use_default_te_spec():
+    """Test that use_default_te_spec=True returns the standard mamba_stack_spec."""
+    spec = get_mamba_stack_modelopt_spec(use_default_te_spec=True)
+    assert spec is mamba_stack_spec
diff --git a/tests/unit_tests/resharding/test_communication_scheduler.py b/tests/unit_tests/resharding/test_communication_scheduler.py
new file mode 100644
index 00000000000..15d25236740
--- /dev/null
+++ b/tests/unit_tests/resharding/test_communication_scheduler.py
@@ -0,0 +1,210 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+import pytest
+
+from megatron.core.resharding.nvshmem_copy_service.nvshmem_types import ScheduledBatch
+from megatron.core.resharding.nvshmem_copy_service.planning.communication_scheduler import (
+    CommunicationScheduler,
+)
+
+
+class TestCommunicationScheduler:
+    """Test suite for CommunicationScheduler."""
+
+    def test_conflict_detection(self):
+        """Test that conflicts are detected correctly."""
+        scheduler = CommunicationScheduler()
+
+        # Create test batches
+        batch1 = ScheduledBatch(src_pe=0, dest_pe=1, batch_index=0, iteration=-1, total_size=1000)
+        batch2 = ScheduledBatch(src_pe=0, dest_pe=2, batch_index=0, iteration=-1, total_size=1000)
+        batch3 = ScheduledBatch(src_pe=2, dest_pe=3, batch_index=0, iteration=-1, total_size=1000)
+        batch4 = ScheduledBatch(src_pe=4, dest_pe=5, batch_index=0, iteration=-1, total_size=1000)
+
+        # batch1 and batch2 conflict (share src PE 0)
+        # batch2 and batch3 conflict (share PE 2)
+        # batch4 doesn't conflict with any
+
+        batches = [batch1, batch2, batch3, batch4]
+        scheduler._assign_iterations(batches)
+
+        # batch1 and batch2 should be in different iterations (conflict on PE 0)
+        assert batch1.iteration != batch2.iteration
+
+        # batch2 and batch3 should be in different iterations (conflict on PE 2)
+        assert batch2.iteration != batch3.iteration
+
+        # batch4 can be in any iteration (no conflicts)
+        # Since we process by degree, batch4 (degree 0) will be placed first
+        assert batch4.iteration == 0
+
+    def test_degree_based_sorting(self):
+        """Test that batches are sorted by conflict degree."""
+        scheduler = CommunicationScheduler()
+
+        # Create batches with different conflict patterns
+        # Central hub pattern: PE 0 connects to many others
+        batches = [
+            ScheduledBatch(src_pe=0, dest_pe=1, batch_index=0, iteration=-1, total_size=1000),
+            ScheduledBatch(src_pe=0, dest_pe=2, batch_index=0, iteration=-1, total_size=1000),
+            ScheduledBatch(src_pe=0, dest_pe=3, batch_index=0, iteration=-1, total_size=1000),
+            ScheduledBatch(
+                src_pe=4, dest_pe=5, batch_index=0, iteration=-1, total_size=1000
+            ),  # isolated
+        ]
+
+        scheduler._assign_iterations(batches)
+
+        # Batches involving PE 0 should be scheduled in different iterations
+        pe0_batches = [b for b in batches if b.src_pe == 0 or b.dest_pe == 0]
+        iterations = [b.iteration for b in pe0_batches]
+        # All PE 0 batches should be in different iterations
+        assert len(iterations) == len(set(iterations))
+
+        # Isolated batch should be in iteration 0 (no conflicts)
+        isolated = [b for b in batches if b.src_pe == 4][0]
+        assert isolated.iteration == 0
+
+    def test_ring_pattern(self):
+        """Test scheduling efficiency for ring communication pattern."""
+        scheduler = CommunicationScheduler()
+
+        n_pes = 8
+        # Ring pattern: each PE sends to next PE (0→1, 1→2, 2→3, ...)
+        batches = [
+            ScheduledBatch(
+                src_pe=i, dest_pe=(i + 1) % n_pes, batch_index=0, iteration=-1, total_size=1000
+            )
+            for i in range(n_pes)
+        ]
+
+        scheduler._assign_iterations(batches)
+
+        # Ring pattern needs 2 iterations because a PE can't send and receive simultaneously
+        # Iteration 0: Even-indexed PEs send (0→1, 2→3, 4→5, 6→7)
+        # Iteration 1: Odd-indexed PEs send (1→2, 3→4, 5→6, 7→0)
+        iterations_used = len(set(b.iteration for b in batches))
+        assert iterations_used == 2, f"Ring should use 2 iterations, got {iterations_used}"
+
+        # Verify no conflicts within each iteration
+        for iteration in range(iterations_used):
+            iter_batches = [b for b in batches if b.iteration == iteration]
+            used_pes = set()
+            for batch in iter_batches:
+                assert batch.src_pe not in used_pes
+                assert batch.dest_pe not in used_pes
+                used_pes.add(batch.src_pe)
+                used_pes.add(batch.dest_pe)
+
+    def test_all_to_all_pattern(self):
+        """Test scheduling for all-to-all communication."""
+        scheduler = CommunicationScheduler()
+
+        n_pes = 4
+        # All-to-all: every PE sends to every other PE
+        batches = []
+        for src in range(n_pes):
+            for dst in range(n_pes):
+                if src != dst:
+                    batches.append(
+                        ScheduledBatch(
+                            src_pe=src, dest_pe=dst, batch_index=0, iteration=-1, total_size=1000
+                        )
+                    )
+
+        scheduler._assign_iterations(batches)
+
+        # Verify schedule is conflict-free
+        for iteration in range(scheduler.num_iterations):
+            iter_batches = [b for b in batches if b.iteration == iteration]
+            used_pes = set()
+            for batch in iter_batches:
+                # No PE should be used twice in same iteration
+                assert (
+                    batch.src_pe not in used_pes
+                ), f"PE {batch.src_pe} used twice in iteration {iteration}"
+                assert (
+                    batch.dest_pe not in used_pes
+                ), f"PE {batch.dest_pe} used twice in iteration {iteration}"
+                used_pes.add(batch.src_pe)
+                used_pes.add(batch.dest_pe)
+
+    def test_empty_workloads(self):
+        """Test handling of empty workloads."""
+        scheduler = CommunicationScheduler()
+        batches = []
+        scheduler._assign_iterations(batches)
+        assert scheduler.num_iterations == 0
+
+    def test_single_batch(self):
+        """Test scheduling with a single batch."""
+        scheduler = CommunicationScheduler()
+        batches = [
+            ScheduledBatch(src_pe=0, dest_pe=1, batch_index=0, iteration=-1, total_size=1000)
+        ]
+        scheduler._assign_iterations(batches)
+        assert scheduler.num_iterations == 1
+        assert batches[0].iteration == 0
+
+    def test_no_self_conflict(self):
+        """Test that a batch doesn't conflict with itself."""
+        scheduler = CommunicationScheduler()
+
+        # Single batch should be scheduled in iteration 0
+        batches = [
+            ScheduledBatch(src_pe=0, dest_pe=1, batch_index=0, iteration=-1, total_size=1000)
+        ]
+        scheduler._assign_iterations(batches)
+        assert batches[0].iteration == 0
+
+    def test_scatter_pattern(self):
+        """Test one-to-many scatter pattern."""
+        scheduler = CommunicationScheduler()
+
+        n_receivers = 7
+        # One PE sends to all others
+        batches = [
+            ScheduledBatch(src_pe=0, dest_pe=i + 1, batch_index=0, iteration=-1, total_size=1000)
+            for i in range(n_receivers)
+        ]
+
+        scheduler._assign_iterations(batches)
+
+        # All batches involve PE 0 as sender, so must be in different iterations
+        iterations_used = len(set(b.iteration for b in batches))
+        assert iterations_used == n_receivers
+
+    def test_gather_pattern(self):
+        """Test many-to-one gather pattern."""
+        scheduler = CommunicationScheduler()
+
+        n_senders = 7
+        # All PEs send to one PE
+        batches = [
+            ScheduledBatch(src_pe=i, dest_pe=7, batch_index=0, iteration=-1, total_size=1000)
+            for i in range(n_senders)
+        ]
+
+        scheduler._assign_iterations(batches)
+
+        # All batches involve PE 7 as receiver, so must be in different iterations
+        iterations_used = len(set(b.iteration for b in batches))
+        assert iterations_used == n_senders
+
+    def test_large_batch_priority(self):
+        """Test that larger batches get priority (tie-breaking by size)."""
+        scheduler = CommunicationScheduler()
+
+        # Create batches with different sizes
+        small_batch = ScheduledBatch(
+            src_pe=0, dest_pe=1, batch_index=0, iteration=-1, total_size=100
+        )
+        large_batch = ScheduledBatch(
+            src_pe=2, dest_pe=3, batch_index=0, iteration=-1, total_size=10000
+        )
+
+        batches = [small_batch, large_batch]
+        scheduler._assign_iterations(batches)
+
+        # Both should be in iteration 0 (no conflicts)
+        assert small_batch.iteration == 0
+        assert large_batch.iteration == 0
diff --git a/tests/unit_tests/resharding/test_dp_balancing.py b/tests/unit_tests/resharding/test_dp_balancing.py
new file mode 100644
index 00000000000..f1f8035171a
--- /dev/null
+++ b/tests/unit_tests/resharding/test_dp_balancing.py
@@ -0,0 +1,344 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+import pytest
+
+from megatron.core.resharding.utils import ParameterMetadata, select_src_metadata_balanced
+
+
+class TestDPBalancing:
+    """Test suite for DP load balancing."""
+
+    def _create_metadata(self, rank, tp_group, dp_group, ep_group=None):
+        """Helper to create ParameterMetadata for testing."""
+        return ParameterMetadata(
+            name="test.weight",
+            shape=(128, 256),
+            dtype="float32",
+            element_size=4,  # 4 bytes for float32
+            owner_rank=rank,
+            tensor_parallel_group_ranks=tp_group,
+            data_parallel_group_ranks=dp_group,
+            expert_parallel_group_ranks=ep_group,
+            is_tp=True,
+            partition_dim=0,
+        )
+
+    def test_dp_balancing_basic(self):
+        """Test basic DP balancing with 2 DP groups."""
+        # Setup: TP=2, DP=2, World=4
+        # TP groups: [[0,1], [2,3]]
+        # DP groups: [[0,2], [1,3]]  (TP-local-0 and TP-local-1)
+
+        # Source metadata from all ranks
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2]),
+            self._create_metadata(rank=1, tp_group=[0, 1], dp_group=[1, 3]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2]),
+            self._create_metadata(rank=3, tp_group=[2, 3], dp_group=[1, 3]),
+        ]
+
+        # Destination metadata (TP=1, DP=4)
+        dst_meta = self._create_metadata(rank=0, tp_group=[0], dp_group=[0, 1, 2, 3])
+
+        # Test each destination rank's selection
+        selections = {}
+        for dst_rank in range(4):
+            selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank)
+            selections[dst_rank] = (selected.owner_rank, tuple(selected.data_parallel_group_ranks))
+
+        # Verify round-robin across DP groups
+        # dst_rank 0: 0 % 2 = 0 -> should select DP group [0,2]
+        # dst_rank 1: 1 % 2 = 1 -> should select DP group [1,3]
+        # dst_rank 2: 2 % 2 = 0 -> should select DP group [0,2]
+        # dst_rank 3: 3 % 2 = 1 -> should select DP group [1,3]
+        assert selections[0][1] in [(0, 2), (1, 3)]  # DP group 0 or 1
+        assert selections[1][1] in [(0, 2), (1, 3)]
+        assert selections[2][1] == selections[0][1]  # Same as rank 0
+        assert selections[3][1] == selections[1][1]  # Same as rank 1
+
+        # Verify different DP groups selected
+        assert selections[0][1] != selections[1][1]
+
+    def test_dp_balancing_non_collocated(self):
+        """Test DP balancing in non-collocated mode (dst ranks not in source ranks)."""
+        # Setup: TP=2, DP=2, World=4 (non-collocated, same config on both sides)
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2]),
+            self._create_metadata(rank=1, tp_group=[0, 1], dp_group=[1, 3]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2]),
+            self._create_metadata(rank=3, tp_group=[2, 3], dp_group=[1, 3]),
+        ]
+
+        # Destination with TP=2 (same as source), dst rank not in source ranks
+        dst_meta = self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[4, 5])
+
+        # Should select via DP balancing (no local copy available)
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=4)
+
+        # dst_rank=4, 4 % 2 = 0 -> selects from first sorted DP group
+        assert selected.owner_rank in [0, 1, 2, 3]
+
+    def test_dp_balancing_distribution(self):
+        """Test that many destination ranks are evenly distributed across source DP groups."""
+        # Setup: TP=2, DP=4, World=8
+        # TP groups: [[0,1], [2,3], [4,5], [6,7]]
+        # DP groups: [[0,2,4,6], [1,3,5,7]]
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=1, tp_group=[0, 1], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=3, tp_group=[2, 3], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=5, tp_group=[4, 5], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=6, tp_group=[6, 7], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=7, tp_group=[6, 7], dp_group=[1, 3, 5, 7]),
+        ]
+
+        dst_meta = self._create_metadata(rank=0, tp_group=[0], dp_group=list(range(8)))
+
+        # Count selections per DP group
+        dp_group_counts = {}
+        for dst_rank in range(16):  # Test with more dst ranks than src
+            selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank)
+            dp_group = tuple(selected.data_parallel_group_ranks)
+            dp_group_counts[dp_group] = dp_group_counts.get(dp_group, 0) + 1
+
+        # Should have exactly 2 DP groups
+        assert len(dp_group_counts) == 2
+
+        # Each should be selected 8 times (16 ranks / 2 groups = 8)
+        assert all(count == 8 for count in dp_group_counts.values())
+
+    def test_dp_balancing_with_ep(self):
+        """Test DP balancing with expert parallelism."""
+        # Setup: TP=2, EP=2, DP=2, World=8
+        # When EP sizes match, should filter by EP local rank
+        #
+        # EP local rank is computed from ep_group.index(owner_rank):
+        #   rank=0 in ep_group=[0, 2] -> EP local 0
+        #   rank=2 in ep_group=[0, 2] -> EP local 1
+        #   rank=4 in ep_group=[4, 6] -> EP local 0
+        #   rank=6 in ep_group=[4, 6] -> EP local 1
+
+        src_meta_list = [
+            # EP local 0 (rank 0 is at index 0 in ep_group [0, 2])
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 4], ep_group=[0, 2]),
+            # EP local 1 (rank 2 is at index 1 in ep_group [0, 2])
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[2, 6], ep_group=[0, 2]),
+            # EP local 0 (rank 4 is at index 0 in ep_group [4, 6])
+            self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 4], ep_group=[4, 6]),
+            # EP local 1 (rank 6 is at index 1 in ep_group [4, 6])
+            self._create_metadata(rank=6, tp_group=[6, 7], dp_group=[2, 6], ep_group=[4, 6]),
+        ]
+
+        # Destination with same EP size=2, EP local rank = 0
+        # (rank 8 is at index 0 in ep_group [8, 9])
+        dst_meta = self._create_metadata(rank=8, tp_group=[8, 9], dp_group=[8, 9], ep_group=[8, 9])
+
+        # When EP sizes match (2->2), should filter by EP local rank
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=8)
+
+        # Should select from EP-local-0 ranks only (0 or 4)
+        ep_local = selected.expert_parallel_group_ranks.index(selected.owner_rank)
+        assert ep_local == 0
+        assert selected.owner_rank in [0, 4]
+
+    def test_dp_balancing_single_dp_group(self):
+        """Test fast path when only one DP group exists."""
+        # Setup: TP=2, DP=1, World=2 (single DP group)
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 1]),
+            self._create_metadata(rank=1, tp_group=[0, 1], dp_group=[0, 1]),
+        ]
+
+        dst_meta = self._create_metadata(rank=0, tp_group=[0], dp_group=[0])
+
+        # Should hit fast path and return first metadata
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=0)
+
+        # Fast path returns first entry (after any TP/EP filtering)
+        assert selected == src_meta_list[0]
+
+    def test_tp_size_mismatch_no_filter(self):
+        """Test DP balancing when TP sizes differ (resharding mode)."""
+        # Setup: TP=4 -> TP=2 (resharding)
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1, 2, 3], dp_group=[0, 4]),
+            self._create_metadata(rank=1, tp_group=[0, 1, 2, 3], dp_group=[1, 5]),
+            self._create_metadata(rank=2, tp_group=[0, 1, 2, 3], dp_group=[2, 6]),
+            self._create_metadata(rank=3, tp_group=[0, 1, 2, 3], dp_group=[3, 7]),
+            self._create_metadata(rank=4, tp_group=[4, 5, 6, 7], dp_group=[0, 4]),
+            self._create_metadata(rank=5, tp_group=[4, 5, 6, 7], dp_group=[1, 5]),
+            self._create_metadata(rank=6, tp_group=[4, 5, 6, 7], dp_group=[2, 6]),
+            self._create_metadata(rank=7, tp_group=[4, 5, 6, 7], dp_group=[3, 7]),
+        ]
+
+        # Destination with TP=2 (different from source TP=4)
+        dst_meta = self._create_metadata(rank=8, tp_group=[8, 9], dp_group=[8, 9])
+
+        # Should only do DP balancing (no TP filtering)
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=8)
+
+        # Since dst_rank % 4 = 0, should select DP group 0
+        # Could be any TP local rank (not filtered)
+        assert selected.owner_rank in [0, 4]  # Both have DP group [0,4]
+
+    def test_ep_size_mismatch_no_filter(self):
+        """Test that EP filtering is skipped when EP sizes differ."""
+        # Setup: EP=4 -> EP=8 (expert parallel resharding)
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0], dp_group=[0, 4], ep_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=1, tp_group=[1], dp_group=[1, 5], ep_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=2, tp_group=[2], dp_group=[2, 6], ep_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=3, tp_group=[3], dp_group=[3, 7], ep_group=[0, 1, 2, 3]),
+        ]
+
+        # Destination with EP=8 (different from source EP=4)
+        # EP sizes differ (4 vs 8), so EP filtering should be skipped
+        dst_meta = self._create_metadata(
+            rank=8, tp_group=[8], dp_group=[8, 9], ep_group=list(range(8, 16))
+        )
+
+        # Should NOT filter by EP local rank (sizes differ)
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=8)
+
+        # Should work without error (no EP filtering when sizes differ)
+        assert selected.owner_rank in [0, 1, 2, 3]
+
+    def test_load_distribution_across_parameters(self):
+        """Test that different dst ranks select different DP groups for load balancing."""
+        # Setup: TP=1, DP=4, World=4
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0], dp_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=1, tp_group=[1], dp_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=2, tp_group=[2], dp_group=[0, 1, 2, 3]),
+            self._create_metadata(rank=3, tp_group=[3], dp_group=[0, 1, 2, 3]),
+        ]
+
+        dst_meta = self._create_metadata(rank=0, tp_group=[0], dp_group=[0, 1, 2, 3, 4, 5, 6, 7])
+
+        # Simulate 8 destination ranks selecting sources
+        # Since there's only 1 DP group with 4 members, all should select the same group
+        # But round-robin based on dst_rank should still distribute across src ranks
+        selections = []
+        for dst_rank in range(8):
+            selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank)
+            selections.append(selected.owner_rank)
+
+        # All should select the same DP group (only one exists)
+        # But within that group, should cycle through available ranks
+        # Since there's only 1 DP group, they all select from it
+        assert all(rank in [0, 1, 2, 3] for rank in selections)
+
+    def test_within_dp_group_distribution(self):
+        """Test that dst ranks distribute across source ranks within a DP group."""
+        # This tests the optimization: when multiple dst ranks map to the same DP group,
+        # they should use different source ranks within that group for load balancing.
+
+        # Setup: TP=2, World=8 -> TP=1, World=8
+        # Source TP groups: [[0,1], [2,3], [4,5], [6,7]]
+        # Source DP groups: [[0,2,4,6], [1,3,5,7]]  (2 DP replicas)
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=1, tp_group=[0, 1], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=3, tp_group=[2, 3], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=5, tp_group=[4, 5], dp_group=[1, 3, 5, 7]),
+            self._create_metadata(rank=6, tp_group=[6, 7], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=7, tp_group=[6, 7], dp_group=[1, 3, 5, 7]),
+        ]
+
+        dst_meta = self._create_metadata(rank=0, tp_group=[0], dp_group=list(range(8)))
+
+        # Test 8 destination ranks
+        selections = {}
+        for dst_rank in range(8):
+            selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank)
+            selections[dst_rank] = (
+                selected.owner_rank,
+                tuple(selected.tensor_parallel_group_ranks),
+            )
+
+        # Verify distribution:
+        # dst_rank 0: DP group 0 ([0,2,4,6]), within-group idx 0 -> rank 0, TP [0,1]
+        # dst_rank 1: DP group 1 ([1,3,5,7]), within-group idx 0 -> rank 1, TP [0,1]
+        # dst_rank 2: DP group 0 ([0,2,4,6]), within-group idx 1 -> rank 2, TP [2,3]
+        # dst_rank 3: DP group 1 ([1,3,5,7]), within-group idx 1 -> rank 3, TP [2,3]
+        # dst_rank 4: DP group 0 ([0,2,4,6]), within-group idx 2 -> rank 4, TP [4,5]
+        # dst_rank 5: DP group 1 ([1,3,5,7]), within-group idx 2 -> rank 5, TP [4,5]
+        # dst_rank 6: DP group 0 ([0,2,4,6]), within-group idx 3 -> rank 6, TP [6,7]
+        # dst_rank 7: DP group 1 ([1,3,5,7]), within-group idx 3 -> rank 7, TP [6,7]
+
+        assert selections[0] == (0, (0, 1))
+        assert selections[1] == (1, (0, 1))
+        assert selections[2] == (2, (2, 3))
+        assert selections[3] == (3, (2, 3))
+        assert selections[4] == (4, (4, 5))
+        assert selections[5] == (5, (4, 5))
+        assert selections[6] == (6, (6, 7))
+        assert selections[7] == (7, (6, 7))
+
+        # Verify ALL source ranks are used (good load distribution!)
+        source_ranks_used = {sel[0] for sel in selections.values()}
+        assert source_ranks_used == {0, 1, 2, 3, 4, 5, 6, 7}, "All source ranks should be used"
+
+        # Verify each TP group used by 2 dst ranks (evenly distributed)
+        tp_group_usage = {}
+        for sel in selections.values():
+            tp_group = sel[1]
+            tp_group_usage[tp_group] = tp_group_usage.get(tp_group, 0) + 1
+
+        # Each of 4 TP groups should be used by exactly 2 destination ranks
+        assert all(count == 2 for count in tp_group_usage.values())
+        assert len(tp_group_usage) == 4  # 4 different TP groups
+
+    def test_local_copy_preference_collocated(self):
+        """Test that collocated mode prefers local copies when available."""
+        # Setup: Collocated TP=2, World=8, DP=4
+        # Each rank has both src and dst models with same configuration
+        # Should always prefer local copy (dst_rank == src_rank)
+
+        # Source metadata from all ranks
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=6, tp_group=[6, 7], dp_group=[0, 2, 4, 6]),
+        ]
+
+        # Destination rank 0 (collocated - has both src and dst)
+        dst_meta = self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2, 4, 6])
+
+        # Should select rank 0 for local copy (not rank 2, 4, or 6)
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=0)
+        assert selected.owner_rank == 0, "Should prefer local copy in collocated mode"
+
+        # Try rank 4
+        dst_meta = self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 2, 4, 6])
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=4)
+        assert selected.owner_rank == 4, "Should prefer local copy for rank 4"
+
+    def test_no_local_copy_non_collocated(self):
+        """Test that non-collocated mode still uses DP balancing."""
+        # Setup: Non-collocated - dst rank 8 not in source ranks [0,2,4,6]
+
+        src_meta_list = [
+            self._create_metadata(rank=0, tp_group=[0, 1], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=2, tp_group=[2, 3], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=4, tp_group=[4, 5], dp_group=[0, 2, 4, 6]),
+            self._create_metadata(rank=6, tp_group=[6, 7], dp_group=[0, 2, 4, 6]),
+        ]
+
+        # Destination rank 8 (not in source ranks - non-collocated)
+        dst_meta = self._create_metadata(rank=8, tp_group=[8, 9], dp_group=[8, 9])
+
+        # Should fall back to DP balancing (not trying to find rank 8 in sources)
+        selected = select_src_metadata_balanced(src_meta_list, dst_meta, dst_rank=8)
+        assert selected.owner_rank in [0, 2, 4, 6], "Should select from available source ranks"
diff --git a/tests/unit_tests/resharding/test_model_swap.py b/tests/unit_tests/resharding/test_model_swap.py
index f5db5cb6185..19cb2306bf7 100644
--- a/tests/unit_tests/resharding/test_model_swap.py
+++ b/tests/unit_tests/resharding/test_model_swap.py
@@ -17,13 +17,20 @@
 )
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.resharding.refit import swap_model_weights
+from megatron.core.resharding.refit import clear_all_caches, swap_model_weights
 from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.cuda_graphs import CudaGraphManager, _CudagraphGlobalRecord
 from megatron.core.transformer.transformer_config import TransformerConfig
 from tests.unit_tests.test_utilities import Utils
 
+try:
+    import nvshmem.core
+
+    has_nvshmem = True
+except Exception:
+    has_nvshmem = False
+
 
 def _build_pg_collection(
     tp_size: int, pp_size: int = None, ep_size: int = 1
@@ -116,7 +123,20 @@ def _set_pg_collection(module, tp_group, dp_group):
     return module
 
 
-@pytest.mark.parametrize("refit_backend", ["nccl", "gloo"])
+@pytest.mark.parametrize(
+    "refit_backend",
+    [
+        pytest.param(
+            "nvshmem",
+            marks=pytest.mark.skipif(
+                not has_nvshmem,
+                reason="nvshmem.core is not available (NVSHMEM Python bindings not installed)",
+            ),
+        ),
+        "nccl",
+        "gloo",
+    ],
+)
 @pytest.mark.parametrize(
     "src_tp,src_pp,src_ep,dst_tp,dst_pp,dst_ep,num_experts",
     [
@@ -275,4 +295,7 @@ def test_swap_gpt_parametrized(
         dst_logits, ref_logits, atol=1e-4, rtol=1e-4
     ), f"Refit src(TP={src_tp},PP={src_pp})->dst(TP={dst_tp},PP={dst_pp}) GPT outputs differ"
     dist.barrier()
+
+    # Clear refit caches before destroying model parallel to avoid stale plans
+    clear_all_caches()
     Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/resharding/test_task_segmenter.py b/tests/unit_tests/resharding/test_task_segmenter.py
new file mode 100644
index 00000000000..e092f0e1c99
--- /dev/null
+++ b/tests/unit_tests/resharding/test_task_segmenter.py
@@ -0,0 +1,185 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+import pytest
+
+from megatron.core.resharding.nvshmem_copy_service.nvshmem_types import (
+    MAX_SEGMENT_SIZE,
+    ReceiveRequest,
+    SendRequest,
+)
+from megatron.core.resharding.nvshmem_copy_service.planning.task_segmenter import TaskSegmenter
+
+
+class TestTaskSegmenter:
+    """Test suite for TaskSegmenter."""
+
+    def test_segment_small_request(self):
+        """Test segmenting a request smaller than max segment size."""
+        segmenter = TaskSegmenter()
+
+        # Request smaller than 256MB should not be segmented
+        send_req = SendRequest(
+            task_id=1, src_tensor=None, src_pos=0, size=500 * 1024, dest_pe=1
+        )  # 500KB
+        recv_req = ReceiveRequest(
+            task_id=1, dest_tensor=None, dest_pos=0, size=500 * 1024, src_pe=0
+        )
+
+        send_segments = segmenter.segment_send_request(send_req)
+        recv_segments = segmenter.segment_receive_request(recv_req)
+
+        # Should produce exactly one segment (no splitting)
+        assert len(send_segments) == 1
+        assert send_segments[0].task_id == 1
+        assert send_segments[0].size == 500 * 1024
+        assert send_segments[0].dest_pe == 1
+
+        assert len(recv_segments) == 1
+        assert recv_segments[0].task_id == 1
+        assert recv_segments[0].size == 500 * 1024
+
+    def test_segment_large_request(self):
+        """Test segmenting a request larger than max segment size."""
+        segmenter = TaskSegmenter()
+
+        # Request larger than 256MB should be segmented
+        task_size = 3 * MAX_SEGMENT_SIZE  # 768MB
+        send_req = SendRequest(task_id=1, src_tensor=None, src_pos=0, size=task_size, dest_pe=1)
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Should produce 3 segments
+        assert len(send_segments) == 3
+        for segment in send_segments:
+            assert segment.size == MAX_SEGMENT_SIZE  # Each segment is max size
+            assert segment.dest_pe == 1
+
+    def test_segment_not_exact_multiple(self):
+        """Test segmenting when size is not exact multiple of max segment size."""
+        segmenter = TaskSegmenter()
+
+        # 2.5 × 256MB = 640MB -> should produce 3 segments (256MB, 256MB, 128MB)
+        task_size = int(2.5 * MAX_SEGMENT_SIZE)
+        send_req = SendRequest(task_id=1, src_tensor=None, src_pos=0, size=task_size, dest_pe=1)
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Should produce 3 segments
+        assert len(send_segments) == 3
+        # First two segments are full size
+        assert send_segments[0].size == MAX_SEGMENT_SIZE
+        assert send_segments[1].size == MAX_SEGMENT_SIZE
+        # Last segment is remainder
+        assert send_segments[2].size == int(0.5 * MAX_SEGMENT_SIZE)
+
+    def test_segment_send_and_receive_match(self):
+        """Test that send and receive segmentation produces matching segments."""
+        segmenter = TaskSegmenter()
+
+        task_size = int(2.5 * MAX_SEGMENT_SIZE)
+        send_req = SendRequest(task_id=1, src_tensor=None, src_pos=0, size=task_size, dest_pe=1)
+        recv_req = ReceiveRequest(task_id=1, dest_tensor=None, dest_pos=0, size=task_size, src_pe=0)
+
+        send_segments = segmenter.segment_send_request(send_req)
+        recv_segments = segmenter.segment_receive_request(recv_req)
+
+        # Should produce same number of segments
+        assert len(send_segments) == len(recv_segments)
+
+        # Sizes should match
+        for send_seg, recv_seg in zip(send_segments, recv_segments):
+            assert send_seg.size == recv_seg.size
+
+    def test_segment_very_large_request(self):
+        """Test segmenting a very large request."""
+        segmenter = TaskSegmenter()
+
+        # 10 × 256MB = 2.56GB
+        task_size = 10 * MAX_SEGMENT_SIZE
+        send_req = SendRequest(task_id=1, src_tensor=None, src_pos=0, size=task_size, dest_pe=1)
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Should produce 10 segments
+        assert len(send_segments) == 10
+        # All segments should be full size
+        for segment in send_segments:
+            assert segment.size == MAX_SEGMENT_SIZE
+
+    def test_segment_zero_size_request(self):
+        """Test handling of zero-size request."""
+        segmenter = TaskSegmenter()
+
+        send_req = SendRequest(task_id=1, src_tensor=None, src_pos=0, size=0, dest_pe=1)
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Should produce one segment with size 0
+        assert len(send_segments) == 1
+        assert send_segments[0].size == 0
+
+    def test_segment_exactly_max_size(self):
+        """Test segmenting request that is exactly max segment size."""
+        segmenter = TaskSegmenter()
+
+        # Exactly 256MB - should NOT be segmented
+        send_req = SendRequest(
+            task_id=1, src_tensor=None, src_pos=0, size=MAX_SEGMENT_SIZE, dest_pe=1
+        )
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Should produce exactly 1 segment (no splitting needed)
+        assert len(send_segments) == 1
+        assert send_segments[0].size == MAX_SEGMENT_SIZE
+
+    def test_segment_preserves_destination(self):
+        """Test that segmentation preserves destination PE."""
+        segmenter = TaskSegmenter()
+
+        task_size = 2 * MAX_SEGMENT_SIZE
+        send_req = SendRequest(
+            task_id=1, src_tensor=None, src_pos=0, size=task_size, dest_pe=42
+        )  # Non-standard PE
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # All segments should have same destination
+        for segment in send_segments:
+            assert segment.dest_pe == 42
+
+    def test_segment_position_offset(self):
+        """Test that segments have correct position offsets."""
+        segmenter = TaskSegmenter()
+
+        task_size = int(2.5 * MAX_SEGMENT_SIZE)
+        start_pos = 1000
+        send_req = SendRequest(
+            task_id=1, src_tensor=None, src_pos=start_pos, size=task_size, dest_pe=1
+        )
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Check position offsets
+        assert send_segments[0].src_pos == start_pos
+        assert send_segments[1].src_pos == start_pos + MAX_SEGMENT_SIZE
+        assert send_segments[2].src_pos == start_pos + 2 * MAX_SEGMENT_SIZE
+
+    def test_segment_task_id_encoding(self):
+        """Test that segments have encoded task IDs."""
+        segmenter = TaskSegmenter()
+
+        task_size = 2 * MAX_SEGMENT_SIZE
+        original_task_id = 42
+        send_req = SendRequest(
+            task_id=original_task_id, src_tensor=None, src_pos=0, size=task_size, dest_pe=1
+        )
+
+        send_segments = segmenter.segment_send_request(send_req)
+
+        # Segments should have encoded task IDs (different from original)
+        # Based on the encoding: REQUEST_ID_BASE + (task_id * SEGMENT_ID_MULTIPLIER) + segment_index
+        assert len(send_segments) == 2
+        assert send_segments[0].task_id != original_task_id
+        assert send_segments[1].task_id != original_task_id
+        # Segment IDs should be different
+        assert send_segments[0].task_id != send_segments[1].task_id
diff --git a/tests/unit_tests/resharding/test_workload_packer.py b/tests/unit_tests/resharding/test_workload_packer.py
new file mode 100644
index 00000000000..f8fef6cceb1
--- /dev/null
+++ b/tests/unit_tests/resharding/test_workload_packer.py
@@ -0,0 +1,128 @@
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+import pytest
+
+from megatron.core.resharding.nvshmem_copy_service.nvshmem_types import SendRequest
+from megatron.core.resharding.nvshmem_copy_service.planning.workload_packer import WorkloadPacker
+
+
+class TestWorkloadPacker:
+    """Test suite for WorkloadPacker."""
+
+    def test_pack_single_request(self):
+        """Test packing a single send request."""
+        packer = WorkloadPacker()
+
+        requests = [SendRequest(task_id=1, src_tensor=None, src_pos=0, size=1000, dest_pe=1)]
+        workloads = packer.pack_workloads(requests, n_pes=2)
+
+        # Should create one batch for destination PE 1
+        assert 1 in workloads
+        assert len(workloads[1]) == 1
+        assert workloads[1][0].total_size == 1000
+        assert len(workloads[1][0].tasks) == 1
+
+    def test_pack_multiple_requests_same_dest(self):
+        """Test packing multiple requests to the same destination."""
+        packer = WorkloadPacker()
+
+        requests = [
+            SendRequest(task_id=1, src_tensor=None, src_pos=0, size=1000, dest_pe=1),
+            SendRequest(task_id=2, src_tensor=None, src_pos=0, size=2000, dest_pe=1),
+            SendRequest(task_id=3, src_tensor=None, src_pos=0, size=3000, dest_pe=1),
+        ]
+        workloads = packer.pack_workloads(requests, n_pes=2)
+
+        # All requests fit in one batch (under 256MB default limit)
+        assert 1 in workloads
+        assert len(workloads[1]) == 1
+        assert workloads[1][0].total_size == 6000
+        assert len(workloads[1][0].tasks) == 3
+
+    def test_pack_exceeds_batch_size(self):
+        """Test that requests are split when size limit exceeded."""
+        packer = WorkloadPacker()
+
+        # Create requests that exceed 256MB limit
+        mb_256 = 256 * 1024 * 1024
+        requests = [
+            SendRequest(task_id=1, src_tensor=None, src_pos=0, size=mb_256 - 1000, dest_pe=1),
+            SendRequest(task_id=2, src_tensor=None, src_pos=0, size=5000, dest_pe=1),
+            SendRequest(task_id=3, src_tensor=None, src_pos=0, size=2000, dest_pe=1),
+        ]
+        workloads = packer.pack_workloads(requests, n_pes=2)
+
+        # Should create 2 batches (first request alone, others together)
+        assert 1 in workloads
+        assert len(workloads[1]) == 2
+
+    def test_pack_multiple_destinations(self):
+        """Test packing requests to multiple destinations."""
+        packer = WorkloadPacker()
+
+        requests = [
+            SendRequest(task_id=1, src_tensor=None, src_pos=0, size=1000, dest_pe=1),
+            SendRequest(task_id=2, src_tensor=None, src_pos=0, size=2000, dest_pe=2),
+            SendRequest(task_id=3, src_tensor=None, src_pos=0, size=3000, dest_pe=1),
+            SendRequest(task_id=4, src_tensor=None, src_pos=0, size=4000, dest_pe=3),
+        ]
+        workloads = packer.pack_workloads(requests, n_pes=4)
+
+        # PE 1: requests 1 and 3 (4000 total)
+        assert len(workloads[1]) == 1
+        assert workloads[1][0].total_size == 4000
+
+        # PE 2: request 2 (2000 total)
+        assert len(workloads[2]) == 1
+        assert workloads[2][0].total_size == 2000
+
+        # PE 3: request 4 (4000 total)
+        assert len(workloads[3]) == 1
+        assert workloads[3][0].total_size == 4000
+
+    def test_pack_empty_requests(self):
+        """Test packing with no requests."""
+        packer = WorkloadPacker()
+        workloads = packer.pack_workloads([], n_pes=4)
+        # All PEs should have empty lists
+        for pe in range(4):
+            assert pe in workloads
+            assert len(workloads[pe]) == 0
+
+    def test_pack_descending_size_order(self):
+        """Test that packing sorts by size descending (largest first)."""
+        packer = WorkloadPacker()
+
+        requests = [
+            SendRequest(task_id=1, src_tensor=None, src_pos=0, size=1000, dest_pe=1),
+            SendRequest(task_id=2, src_tensor=None, src_pos=0, size=5000, dest_pe=1),
+            SendRequest(task_id=3, src_tensor=None, src_pos=0, size=3000, dest_pe=1),
+            SendRequest(task_id=4, src_tensor=None, src_pos=0, size=2000, dest_pe=1),
+        ]
+        workloads = packer.pack_workloads(requests, n_pes=2)
+
+        # All should be in one batch
+        assert 1 in workloads
+        assert len(workloads[1]) == 1
+
+        # Check that tasks are sorted by size (descending)
+        sizes = [req.size for req in workloads[1][0].tasks]
+        assert sizes == sorted(sizes, reverse=True)
+
+    def test_pack_preserves_task_ids(self):
+        """Test that packing preserves task IDs."""
+        packer = WorkloadPacker()
+
+        requests = [
+            SendRequest(task_id=100, src_tensor=None, src_pos=0, size=1000, dest_pe=1),
+            SendRequest(task_id=200, src_tensor=None, src_pos=0, size=2000, dest_pe=1),
+            SendRequest(task_id=300, src_tensor=None, src_pos=0, size=3000, dest_pe=1),
+        ]
+        workloads = packer.pack_workloads(requests, n_pes=2)
+
+        # All requests should be in one batch
+        assert 1 in workloads
+        assert len(workloads[1]) == 1
+
+        # Check task IDs are preserved (sorted by size descending: 300, 200, 100)
+        task_ids = [req.task_id for req in workloads[1][0].tasks]
+        assert task_ids == [300, 200, 100]
diff --git a/tests/unit_tests/rl/test_rl_utils.py b/tests/unit_tests/rl/test_rl_utils.py
index 8747f2e8c35..6206fa93c61 100644
--- a/tests/unit_tests/rl/test_rl_utils.py
+++ b/tests/unit_tests/rl/test_rl_utils.py
@@ -1,9 +1,9 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 import itertools
-import os
-from types import SimpleNamespace
+from unittest.mock import MagicMock
 
+import numpy as np
 import pytest
 import torch
 
@@ -14,12 +14,24 @@
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator
 from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer
-from megatron.core.pipeline_parallel.utils import is_pp_last_stage
+from megatron.core.pipeline_parallel import get_forward_backward_func
+from megatron.core.pipeline_parallel.utils import is_pp_first_stage, is_pp_last_stage
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.tensor_parallel.random import (
+    initialize_rng_tracker,
+    model_parallel_cuda_manual_seed,
+)
 from megatron.core.transformer import TransformerConfig
+from megatron.core.transformer.cuda_graphs import (
+    CudaGraphManager,
+    _CudagraphGlobalRecord,
+    create_cudagraphs,
+    delete_cuda_graphs,
+)
+from megatron.core.transformer.module import Float16Module
 from megatron.rl import rl_utils
 from megatron.rl.agent.api import TokenRollout
+from megatron.rl.sequence_packing_utils import get_default_packed_seq_params
 from megatron.training.arguments import parse_args, validate_args
 from megatron.training.global_vars import destroy_global_vars, set_global_variables
 from tests.unit_tests.test_utilities import Utils
@@ -66,6 +78,7 @@ def __init__(self):
         self.eod = 43
         self.vocab_size = VOCAB
         self.bos = None
+        self.library = None
 
     def detokenize(self, tokens):
         return [str(tok) for tok in tokens]
@@ -79,10 +92,15 @@ def initialize_model_parallel(request, monkeypatch):
     Skips if world_size < tp * pp.
     """
     monkeypatch.setenv("CUDA_DEVICE_MAX_CONNECTIONS", "1")
+    monkeypatch.setenv("WANDB_MODE", "disabled")
+    monkeypatch.setenv("LOG_TO_WANDB", "false")
+
+    initialize_rng_tracker(use_te_rng_tracker=True, force_reset=True)
 
     tp, pp = request.param
     world_size = Utils.world_size
     Utils.initialize_model_parallel(tensor_model_parallel_size=tp, pipeline_model_parallel_size=pp)
+    model_parallel_cuda_manual_seed(123)
     dp = world_size // (tp * pp)
     yield world_size, dp, tp, pp
     Utils.destroy_model_parallel()
@@ -112,6 +130,7 @@ def create_test_args(self, **kwargs):
         args.hidden_size = 128
         args.max_position_embeddings = 256
         args.seq_length = 256
+        args.wandb_project = None
 
         args.micro_batch_size = 1
 
@@ -274,58 +293,68 @@ def test_grpo_loss_truncation(self):
     def test_prepare_data_for_update(self, initialize_model_parallel):
         """Test that getting logprobs at least does not crash."""
         world_size, dp, tp, pp = initialize_model_parallel
+        # Here I assume that we will be consuming all data in one step.
+        group_size = 2
         self.create_test_args(
             micro_batch_size=2,
             seq_length=4,
             curr_iteration=1,
             tensor_model_parallel_size=tp,
             pipeline_model_parallel_size=pp,
+            global_batch_size=dp * 2,
+            grpo_prompts_per_step=dp,
+            grpo_group_size=group_size,
         )
 
         model = MockModel()
         tokenizer = MockTokenizer()
 
         r1 = TokenRollout(
-            trajectory=[1, 2, 3],
+            trajectory=[[1, 2, 3]],
             reward=3.14,
-            generation_mask=[False, True, True],
-            logprobs=[0.1, 0.2, 0.3],
+            generation_mask=[[False, True, True]],
+            logprobs=[[0.1, 0.2, 0.3]],
             env_id='MEGAENV',
             problem_id="2",
         )
         r2 = TokenRollout(
-            trajectory=[1, 2, 3, 4],
+            trajectory=[[1, 2, 3, 4]],
             reward=0.14,
-            generation_mask=[False, True, True, True],
-            logprobs=[0.1, 0.2, 0.3, -1.2],
+            generation_mask=[[False, True, True, True]],
+            logprobs=[[0.1, 0.2, 0.3, -1.2]],
             env_id='MEGAENV',
             problem_id="2",
         )
+
         rollouts = [[r1, r2] for _ in range(dp)]
         try:
-            rl_utils.prepare_data_for_update([model], {}, rollouts, tokenizer)
+            rl_utils.prepare_data_for_update(
+                [model], {}, rollouts, tokenizer, sequence_packing=False, is_correction=False
+            )
         except AssertionError as e:
             # We expect trajectories to come padded there.
             assert str(e).startswith('Rollout is not the correct length')
 
         r1 = TokenRollout(
-            trajectory=torch.tensor([1, 2, 3, tokenizer.eod], dtype=torch.float).cuda(),
+            trajectory=torch.tensor([[1, 2, 3, tokenizer.eod]], dtype=torch.float).cuda(),
             reward=3.14,
-            generation_mask=torch.tensor([False, True, True, True], dtype=torch.float).cuda(),
-            logprobs=torch.tensor([-0.2, -0.3, -3.2]).cuda(),
+            generation_mask=torch.tensor([[False, True, True, True]], dtype=torch.float).cuda(),
+            logprobs=torch.tensor([[-0.2, -0.3, -3.2]]).cuda(),
             env_id='MEGAENV',
             problem_id="2",
         )
         r2 = TokenRollout(
-            trajectory=torch.tensor([1, 2, 234, tokenizer.eod], dtype=torch.float).cuda(),
+            trajectory=torch.tensor([[1, 2, 234, tokenizer.eod]], dtype=torch.float).cuda(),
             reward=0.14,
-            generation_mask=torch.tensor([False, True, True, True], dtype=torch.float).cuda(),
-            logprobs=torch.tensor([-0.2, -0.3, -1.2]),
+            generation_mask=torch.tensor([[False, True, True, True]], dtype=torch.float).cuda(),
+            logprobs=torch.tensor([[-0.2, -0.3, -1.2]]),
             env_id='MEGAENV',
             problem_id="2",
         )
         rollouts = [[r1, r2] for _ in range(dp)]
-        data_iter = rl_utils.prepare_data_for_update([model], {}, rollouts, tokenizer)
+        data_iter = rl_utils.prepare_data_for_update(
+            [model], {}, rollouts, tokenizer, sequence_packing=False, is_correction=False
+        )
 
         _, _, old_logprobs, _, _, _, _ = next(data_iter)
         # All logits are ones in the MockModel.
@@ -333,7 +362,8 @@ def test_prepare_data_for_update(self, initialize_model_parallel):
         torch.testing.assert_close(old_logprobs.exp(), torch.ones_like(old_logprobs) / VOCAB)
 
     @pytest.mark.parametrize("use_sequence_packing", [True, False])
-    def test_prepare_trajectories(self, use_sequence_packing):
+    @pytest.mark.parametrize("num_turns", [1, 2])
+    def test_prepare_trajectories(self, use_sequence_packing, num_turns):
         """Test that rollouts are properly prepared for training."""
         seq_length = 8
         self.create_test_args(
@@ -347,34 +377,38 @@ def test_prepare_trajectories(self, use_sequence_packing):
 
         # Create rollouts of varying lengths
         r1 = TokenRollout(
-            trajectory=[1, 2, 3, tokenizer.eod],
+            trajectory=[[1, 2, 3, tokenizer.eod]] * num_turns,
             reward=3.14,
-            generation_mask=[False, True, True, True],
-            logprobs=[0.1, 0.2, 0.3, 0.35],
+            generation_mask=[[False, True, True, True]] * num_turns,
+            logprobs=[[0.1, 0.2, 0.3, 0.35]] * num_turns,
             env_id='MEGAENV',
             problem_id="1",
         )
         r2 = TokenRollout(
-            trajectory=[4, 5, 6, 7, tokenizer.eod],
+            trajectory=[[4, 5, 6, 7, tokenizer.eod]] * num_turns,
             reward=0.14,
-            generation_mask=[False, True, True, True, True],
-            logprobs=[0.4, 0.5, 0.6, 0.7, 0.75],
+            generation_mask=[[False, True, True, True, True]] * num_turns,
+            logprobs=[[0.4, 0.5, 0.6, 0.7, 0.75]] * num_turns,
             env_id='MEGAENV',
             problem_id="2",
         )
         r3 = TokenRollout(
-            trajectory=[8, 9, tokenizer.eod],
+            trajectory=[[8, 9, tokenizer.eod]] * num_turns,
             reward=2.71,
-            generation_mask=[False, True, True],
-            logprobs=[0.8, 0.9, 0.95],
+            generation_mask=[[False, True, True]] * num_turns,
+            logprobs=[[0.8, 0.9, 0.95]] * num_turns,
             env_id='MEGAENV',
             problem_id="3",
         )
 
-        rollouts = [[r1, r2, r3]]
+        rollouts = [r1, r2, r3]
 
         trajs, genmask, inference_logprobs = rl_utils.prepare_trajectories(
-            rollouts, tokenizer, seq_length
+            rollouts,
+            tokenizer,
+            seq_length,
+            sequence_packing=use_sequence_packing,
+            skip_bos_token=False,
         )
 
         expected_trajs = torch.tensor(
@@ -385,7 +419,7 @@ def test_prepare_trajectories(self, use_sequence_packing):
             ],
             dtype=torch.long,
             device=trajs.device,
-        )
+        ).repeat_interleave(num_turns, dim=0)
         assert torch.equal(trajs, expected_trajs)
 
         expected_genmask = torch.tensor(
@@ -396,7 +430,7 @@ def test_prepare_trajectories(self, use_sequence_packing):
             ],
             dtype=torch.bool,
             device=genmask.device,
-        )
+        ).repeat_interleave(num_turns, dim=0)
         assert torch.equal(genmask, expected_genmask)
 
         if use_sequence_packing:
@@ -408,7 +442,7 @@ def test_prepare_trajectories(self, use_sequence_packing):
                 ],
                 dtype=torch.float32,
                 device=inference_logprobs.device,
-            )
+            ).repeat_interleave(num_turns, dim=0)
             torch.testing.assert_close(inference_logprobs, expected_logprobs, rtol=0, atol=0)
         else:
             expected_logprobs = [
@@ -416,12 +450,57 @@ def test_prepare_trajectories(self, use_sequence_packing):
                 [0.4, 0.5, 0.6, 0.7, 0.75],
                 [0.8, 0.9, 0.95],
             ]
+            expected_logprobs = [el for el in expected_logprobs for _ in range(num_turns)]
             assert len(inference_logprobs) == len(expected_logprobs)
             for got, exp in zip(inference_logprobs, expected_logprobs):
                 got_t = got if torch.is_tensor(got) else torch.tensor(got, dtype=torch.float32)
                 exp_t = torch.tensor(exp, dtype=torch.float32, device=got_t.device)
                 torch.testing.assert_close(got_t, exp_t, rtol=0, atol=0)
 
+    def test_single_turn_advantage_calculation(self):
+        rewards = [[-1, 1], [4, 4]]
+        num_turns = [[1, 1], [1, 1]]
+        advs = rl_utils.calculate_grpo_advantages(rewards, num_turns)
+        torch.testing.assert_close(
+            torch.tensor(advs), torch.tensor([-1, 1.0, 0.0, 0.0]), atol=1e-4, rtol=1e-5
+        )
+
+    def test_multi_turn_advantage_calculation(self):
+        rewards = [[-1, 1], [4, 4]]
+        num_turns = [[2, 1], [1, 3]]
+        advs = rl_utils.calculate_grpo_advantages(rewards, num_turns)
+        torch.testing.assert_close(
+            torch.tensor(advs),
+            torch.tensor([-1, -1, 1.0, 0.0, 0.0, 0.0, 0.0]),
+            atol=1e-4,
+            rtol=1e-5,
+        )
+
+    def test_pad_list_of_nones(self):
+        with pytest.raises(ValueError) as e_info:
+            rl_utils._pad_nonnull_with_zeros([None] * 3, 42)
+        assert "At least one" in str(e_info)
+
+    def test_pad_with_wrong_params(self):
+        with pytest.raises(ValueError) as e_info:
+            rl_utils._pad_nonnull_with_zeros([torch.zeros(5)], 4)
+        assert "larger length" in str(e_info)
+
+    def test_pad_full_size(self):
+        padded = rl_utils._pad_nonnull_with_zeros([torch.zeros(5), torch.zeros(5)], 5)
+        assert padded.shape == (2, 5)
+
+    def test_pad_some_nones(self):
+        padded = rl_utils._pad_nonnull_with_zeros([None, torch.zeros(5)], 5)
+        assert padded.shape == (2, 5)
+        assert (padded[0] == 0).all()
+
+    def test_pad_normal(self):
+        padded = rl_utils._pad_nonnull_with_zeros(
+            [torch.zeros(2), torch.zeros(3), torch.zeros(4)], 5
+        )
+        assert padded.shape == (3, 5)
+
     @pytest.mark.parametrize(
         "initialize_model_parallel",
         [
@@ -591,3 +670,231 @@ def get_optimizer_state_devices():
             f"Expected GPU memory to increase after restore. "
             f"After offload: {memory_after_offload}, After restore: {memory_after_restore}"
         )
+
+    @pytest.mark.parametrize(
+        "initialize_model_parallel",
+        [
+            pytest.param((tp, pp), id=f"tp{tp}-pp{pp}")
+            for tp, pp in itertools.product([1, 2, 4], [1, 2])
+            if tp * pp <= Utils.world_size
+        ],
+        indirect=["initialize_model_parallel"],
+    )
+    def test_gpt_logprobs(self, initialize_model_parallel):
+        """Test get logprobs on an actual model, not on a mocked one.
+
+        This can be useful for quick benchmarking/analyzing regressions too.
+        """
+
+        world_size, dp, tp, pp = initialize_model_parallel
+        micro_batch_size = 2
+        self.create_test_args(
+            tensor_model_parallel_size=tp,
+            pipeline_model_parallel_size=pp,
+            bf16=True,
+            micro_batch_size=micro_batch_size,
+        )
+        model_parallel_cuda_manual_seed(123)
+
+        transformer_config = TransformerConfig(
+            num_layers=10,
+            hidden_size=128,
+            num_attention_heads=16,
+            use_cpu_initialization=True,
+            embedding_init_method_std=1.0,
+            bf16=True,
+            pipeline_dtype=torch.bfloat16,  # Without this, pp!=1 runs will fail.
+        )
+        vocab_size = 10_000
+        pp_group = ProcessGroupCollection.use_mpu_process_groups().pp
+        gpt_model = GPTModel(
+            config=transformer_config,
+            transformer_layer_spec=get_gpt_layer_with_transformer_engine_spec(),
+            vocab_size=vocab_size,
+            max_sequence_length=4192,
+            pre_process=is_pp_first_stage(pp_group),
+            post_process=is_pp_last_stage(pp_group),
+        ).cuda()
+        sequence_length = gpt_model.max_sequence_length
+
+        gpt_model = Float16Module(gpt_model.config, gpt_model)
+
+        data = list(range(sequence_length))
+        input_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda()
+        position_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda()
+
+        with torch.no_grad():
+            logprobs = rl_utils.compute_logprobs_batch(
+                model=gpt_model,
+                data_loader=[(input_ids, position_ids)],
+                forward_backward_func=get_forward_backward_func(pp_size=pp),
+                packing_context=None,
+                trajs_batch_size=micro_batch_size,
+                seq_length=sequence_length,
+                logprobs_batch_size=micro_batch_size,
+                decoder_seq_length=sequence_length,
+                dtype=torch.bfloat16,
+                pp_group=gpt_model.pg_collection.pp,
+                is_correction=False,
+                collect_non_loss_data=True,
+            )
+        if is_pp_last_stage(pp_group):
+            assert logprobs.shape == (micro_batch_size, sequence_length - 1)
+
+    @pytest.mark.parametrize(
+        "initialize_model_parallel",
+        [pytest.param((1, 1), id="tp1-pp1")],
+        indirect=["initialize_model_parallel"],
+    )
+    def test_get_logprobs_cuda_graphs(self, initialize_model_parallel):
+        """Test that get_logprobs reuses CUDA graphs created during training forward pass.
+
+        This test verifies that rl_utils.get_logprobs can reuse CUDA graphs by:
+        1. Running a training-style forward pass on some model to record CUDA graph runners.
+        2. Creating the CUDA graphs.
+        3. Running `get_logprobs` to verify it reuses the same forward graph from training.
+        """
+
+        num_layers = 2
+
+        world_size, dp, tp, pp = initialize_model_parallel
+        self.create_test_args(
+            tensor_model_parallel_size=tp,
+            pipeline_model_parallel_size=pp,
+            rl_training_cuda_graphs=True,
+            cuda_graph_impl="local",
+            bf16=True,
+            rl_sequence_packing_max_sequences_per_bin=4,
+        )
+
+        # Create a model with training CUDA graphs enabled
+        transformer_config = TransformerConfig(
+            num_layers=num_layers,
+            hidden_size=64,
+            num_attention_heads=4,
+            use_cpu_initialization=True,
+            cuda_graph_impl="local",
+            bf16=True,
+        )
+        model = GPTModel(
+            config=transformer_config,
+            transformer_layer_spec=get_gpt_layer_with_transformer_engine_spec(),
+            vocab_size=256,
+            max_sequence_length=32,
+        ).cuda()
+
+        # Wrap in Float16Module so it accepts fp32_output argument from get_logprobs
+        wrapped_model = Float16Module(transformer_config, model)
+
+        # Create test inputs (batch_size=1 required for thd format with sequence packing)
+        batch_size = 1
+        seq_length = 16
+        tokens = torch.randint(0, 256, (batch_size, seq_length), dtype=torch.long).cuda()
+        position_ids = torch.arange(seq_length).unsqueeze(0).expand(batch_size, -1).cuda()
+
+        # Create packed_seq_params for dummy data
+        packed_seq_params = get_default_packed_seq_params(
+            seq_length=seq_length, max_sequences_per_bin=4, device=tokens.device
+        )
+
+        # Run a single training forward pass to record cudagraphs
+        output = wrapped_model(
+            tokens,
+            position_ids,
+            attention_mask=None,
+            packed_seq_params=packed_seq_params,
+            runtime_gather_output=True,
+            fp32_output=False,
+        )
+
+        # Run backward to reset runner status from BWD_READY back to FWD_READY
+        # This is needed because get_logprobs runs in no_grad mode and expects FWD_READY
+        loss = output.sum()
+        loss.backward()
+
+        # Collect all CudaGraphManager instances and their runners
+        cudagraph_managers = []
+        for module in wrapped_model.modules():
+            if hasattr(module, 'cudagraph_manager') and module.cudagraph_manager is not None:
+                cudagraph_managers.append(module.cudagraph_manager)
+
+        # Record runner count before creating graphs
+        runners_before = {id(mgr): len(mgr.cudagraph_runners) for mgr in cudagraph_managers}
+
+        create_cudagraphs()
+
+        # Verify that each runner has a fwd_graph created
+        for mgr in cudagraph_managers:
+            for runner in mgr.cudagraph_runners:
+                assert runner.fwd_graph is not None, (
+                    f"Expected runner to have fwd_graph created after create_cudagraphs(), "
+                    f"but fwd_graph is None"
+                )
+
+        # Now test `get_logprobs`; this should reuse the existing CUDA graphs
+        # We do not pass packed_seq_params; it should be created within `get_logprobs`
+        logprobs = rl_utils.get_logprobs(
+            wrapped_model, tokens, position_ids=position_ids, sequence_packing=True
+        )
+
+        # Verify that no new runners were created and graph was reused
+        runners_after = {id(mgr): len(mgr.cudagraph_runners) for mgr in cudagraph_managers}
+        for mgr_id, count_before in runners_before.items():
+            count_after = runners_after[mgr_id]
+            assert count_after == count_before, (
+                f"Expected runner count to remain {count_before} after `get_logprobs`, "
+                f"but got {count_after}. `get_logprobs` should not create new runners."
+            )
+
+        # Verify outputs are valid
+        assert output is not None, "Training forward pass should return valid output"
+        assert logprobs is not None, "get_logprobs should return valid output"
+
+        # Destroy all captured graphs deterministically
+        for l in model.decoder.layers:
+            for runner in getattr(l.cudagraph_manager, "cudagraph_runners", []):
+                # Safely delete both graphs if present
+                if hasattr(runner, "fwd_graph"):
+                    del runner.fwd_graph
+                if hasattr(runner, "bwd_graph"):
+                    del runner.bwd_graph
+
+        # Ensure all pending work is complete and graph destruction runs now
+        torch.cuda.synchronize()
+
+        _CudagraphGlobalRecord.cudagraph_created = False
+        _CudagraphGlobalRecord.cudagraph_record = []
+        CudaGraphManager.global_mempool = None
+        CudaGraphManager.fwd_mempools = None
+        CudaGraphManager.bwd_mempools = None
+
+    @pytest.mark.parametrize(
+        "initialize_model_parallel",
+        [pytest.param((1, 1), id="tp1-pp1")],
+        indirect=["initialize_model_parallel"],
+    )
+    def test_prep_wandb_metrics(self, initialize_model_parallel):
+        # This tests the computation and makes us fail noisily if
+        # inputs assumptions are changed, e.g. we expect rewards to come in groups (list[list[int]]).
+        traj_lens = [[3, 3], [1, 2]]
+        turn_lens = [[1, 2, 1, 1, 1], [1, 2]]
+        rewards = [[1, 1], [-1, 2]]
+        num_turns = [[42, 2], [10, 8]]
+        advantages = [0, 1]
+        metrics = rl_utils.prep_wandb_metrics(
+            MagicMock(), traj_lens, turn_lens, rewards, num_turns, advantages
+        )
+        assert metrics["mean_reward"] == 0.75
+        assert metrics["mean_advantage"] == 0.5
+        assert metrics["nonzero_groups_ratio"] == 0.5
+        assert metrics["max_traj_length"] == 3
+        assert metrics["min_traj_length"] == 1
+        assert metrics["mean_traj_length"] == 2.25
+        assert metrics["mean_traj_length_std"] == 0.25
+        assert metrics["max_turn_length"] == 2
+        assert metrics["min_turn_length"] == 1
+        assert metrics["mean_turn_length"] == 1.35
+        assert np.isclose(metrics["mean_turn_length_std"], 0.45)
+        assert metrics["mean_num_turns"] == 15.5
+        assert metrics["max_num_turns"] == 42
+        assert metrics["min_num_turns"] == 2
diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py
index 548aedf55fd..06e63adf217 100644
--- a/tests/unit_tests/rl/test_sequence_packing_utils.py
+++ b/tests/unit_tests/rl/test_sequence_packing_utils.py
@@ -1,5 +1,8 @@
 # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
+from unittest.mock import patch
+
+import pytest
 import torch
 
 from megatron.rl import rl_utils, sequence_packing_utils
@@ -407,3 +410,91 @@ def test_compute_packed_inference_logprobs_stats_shape_mismatch():
 
     # Stats should remain None due to shape mismatch
     assert group_stats.mean_piold_to_inf_prob is None
+
+
+@pytest.mark.parametrize("num_sequences", [1, 10, 48, 49, 50])
+def test_cu_seqlens_size(num_sequences):
+    """Test that cu_seqlens always has a fixed size regardless of how many sequences are packed."""
+    max_sequences_per_bin = 50
+    bin_size = 1024
+
+    seq_len = bin_size // max_sequences_per_bin
+    seq_lengths = [seq_len] * num_sequences
+
+    packing_info = sequence_packing_utils.PackingInfo(
+        bin_seq_indices=[list(range(num_sequences))],
+        seq_starts={0: []},
+        seq_lengths=seq_lengths,
+        seq_to_bin_idx=[0] * num_sequences,
+        packing_algo='fifo',
+    )
+
+    params = sequence_packing_utils.create_packed_seq_params_for_bin(
+        packing_info=packing_info,
+        bin_idx=0,
+        bin_size=bin_size,
+        max_sequences_per_bin=max_sequences_per_bin,
+        device=torch.device('cpu'),
+    )
+
+    expected_size = max_sequences_per_bin + 2
+    assert params.cu_seqlens_q.shape[0] == expected_size, (
+        f"cu_seqlens_q has size {params.cu_seqlens_q.shape[0]} but expected {expected_size} "
+        f"for {num_sequences} sequences"
+    )
+    assert params.cu_seqlens_kv.shape[0] == expected_size
+    assert params.cu_seqlens_q[0] == 0
+    assert params.cu_seqlens_q[-1] == bin_size
+
+
+@pytest.mark.parametrize(
+    "ratio,local_bins,world,expected_bs",
+    [
+        (1.0, 1, 8, 8),  # no stale data (ratio 1.), everything divides perfectly.
+        (1.0, 42, 8, 42 * 8),  # no stale data (ratio 1.), everything divides perfectly, more bins
+        (
+            0.5,
+            1,
+            8,
+            8,
+        ),  # 0.5 means we use half of all seqs per step, they all fit 1 bin -> we should reuse
+        (1 / 3, 4, 8, 16),  # third of the data per step, nonint division
+    ],
+)
+def test_get_bins_bs_and_steps(ratio, local_bins, world, expected_bs):
+    # Make a dummy struct to check only the required fields.
+    # Divide by ratio to make sure the samples are divisible by global_bs in the test.
+    n_seqs = int(world * 7 / ratio)
+    global_bs_in_seq = int(n_seqs * ratio)
+
+    def side_eff(
+        rank,
+        rampup_batch_size,
+        global_batch_size,
+        micro_batch_size,
+        data_parallel_size,
+        decrease_batch_size_if_needed,
+    ):
+        # Inside of the get_microbatch_dataloader, we compute the batch size in bins.
+        # We want to test this variable.
+        global actual_bs
+        actual_bs = global_batch_size
+
+    with patch('megatron.rl.sequence_packing_utils.get_num_microbatches', return_value=1):
+        with patch(
+            'megatron.rl.sequence_packing_utils.reconfigure_num_microbatches_calculator',
+            side_effect=side_eff,
+        ):
+            with patch('megatron.core.mpu.get_data_parallel_world_size', return_value=world):
+                sequence_packing_utils.update_microbatch_calculator(
+                    samples_ratio_per_step=ratio,
+                    num_bins_this_rank=local_bins,
+                    bin_seq_indices=[],
+                    global_batch_size=global_bs_in_seq,
+                    rampup_batch_size=1,
+                    micro_batch_size=1,
+                    decrease_batch_size_if_needed=False,
+                )
+
+    # Iterator is local, batch size is global
+    assert expected_bs == actual_bs
diff --git a/tests/unit_tests/run_ci_test.sh b/tests/unit_tests/run_ci_test.sh
index 81dd3ae2a14..c65f197d2db 100755
--- a/tests/unit_tests/run_ci_test.sh
+++ b/tests/unit_tests/run_ci_test.sh
@@ -117,7 +117,7 @@ export BUCKET
 IGNORE_ARGS=()
 while IFS= read -r line; do
     [[ -n "$line" ]] && IGNORE_ARGS+=("$line")
-done < <(python tests/unit_tests/find_test_cases.py "$BUCKET")
+done < <(python tests/unit_tests/find_test_cases.py "$BUCKET" "h100")
 
 echo "------ARGUMENTS for SLURM ---"
 MASTER_ADDR=${MASTER_ADDR:-localhost}
diff --git a/tests/unit_tests/ssm/test_mamba_context_parallel.py b/tests/unit_tests/ssm/test_mamba_context_parallel.py
index 59cd1fb061c..c232aba9655 100644
--- a/tests/unit_tests/ssm/test_mamba_context_parallel.py
+++ b/tests/unit_tests/ssm/test_mamba_context_parallel.py
@@ -1,7 +1,5 @@
 # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
 
-import math
-
 import pytest
 import torch
 import torch.nn as nn
diff --git a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
index 77d02c69607..77c106c3bee 100644
--- a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
+++ b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py
@@ -6,7 +6,12 @@
 import pytest
 import torch
 
-from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, allocate_layers
+from megatron.core.ssm.mamba_hybrid_layer_allocation import (
+    ParsedHybridPattern,
+    Symbols,
+    allocate_layers,
+    parse_hybrid_pattern,
+)
 
 
 @pytest.mark.internal
@@ -75,3 +80,135 @@ def test_wrong_length_override_pattern(self):
     def test_wrong_number_of_layer_types_in_override_pattern(self):
         # This override_pattern has too many mlps and not enough attention
         layer_types = allocate_layers(8, 0.5, 0.25, "M*--M**-")
+
+
+@pytest.mark.internal
+class TestParseHybridPattern:
+    """Tests for parse_hybrid_pattern with unified pattern syntax."""
+
+    def test_none_pattern(self):
+        """Test that None pattern returns all None values."""
+        result = parse_hybrid_pattern(None)
+        assert result.main_pattern is None
+        assert result.mtp_pattern is None
+        assert result.mtp_num_depths == 0
+
+    def test_main_pattern_only(self):
+        """Test patterns without MTP (no / separator)."""
+        test_cases = [
+            ("M*M*", "M*M*"),
+            ("MMMM", "MMMM"),
+            ("*M*M", "*M*M"),
+            ("MM-*", "MM-*"),
+            ("E", "E"),
+        ]
+        for pattern, expected_main in test_cases:
+            result = parse_hybrid_pattern(pattern)
+            assert result.main_pattern == expected_main, f"Failed for pattern: {pattern}"
+            assert result.mtp_pattern is None
+            assert result.mtp_num_depths == 0
+
+    def test_main_with_single_mtp_depth(self):
+        """Test patterns with 1 MTP depth."""
+        test_cases = [
+            ("M*M*/MM", "M*M*", "MM", 1),
+            ("MMMM/*M", "MMMM", "*M", 1),
+            ("M/M", "M", "M", 1),
+        ]
+        for pattern, expected_main, expected_mtp, expected_depths in test_cases:
+            result = parse_hybrid_pattern(pattern)
+            assert result.main_pattern == expected_main, f"Failed for pattern: {pattern}"
+            assert result.mtp_pattern == expected_mtp, f"Failed for pattern: {pattern}"
+            assert result.mtp_num_depths == expected_depths, f"Failed for pattern: {pattern}"
+
+    def test_main_with_multiple_mtp_depths(self):
+        """Test patterns with multiple MTP depths."""
+        test_cases = [
+            ("M*M*/MM/MM", "M*M*", "MM", 2),
+            ("M*M*/MM/MM/MM", "M*M*", "MM", 3),
+            ("MMMM/*M/*M/*M", "MMMM", "*M", 3),
+            ("M*/*/*/*", "M*", "*", 3),
+            ("M/M/M/M/M", "M", "M", 4),
+        ]
+        for pattern, expected_main, expected_mtp, expected_depths in test_cases:
+            result = parse_hybrid_pattern(pattern)
+            assert result.main_pattern == expected_main, f"Failed for pattern: {pattern}"
+            assert result.mtp_pattern == expected_mtp, f"Failed for pattern: {pattern}"
+            assert result.mtp_num_depths == expected_depths, f"Failed for pattern: {pattern}"
+
+    def test_mtp_patterns_must_be_identical(self):
+        """Test that mismatched MTP patterns raise ValueError."""
+        invalid_patterns = [
+            "M*M*/MM/M*",  # MM != M*
+            "M*M*/MM/MM/M",  # MM != M
+            "MMMM/*M/M*",  # *M != M*
+        ]
+        for pattern in invalid_patterns:
+            with pytest.raises(ValueError, match="All MTP patterns must be identical"):
+                parse_hybrid_pattern(pattern)
+
+    def test_invalid_symbols_in_main_pattern(self):
+        """Test that invalid symbols in main pattern raise ValueError."""
+        invalid_patterns = [
+            "M*X*",  # X is not valid
+            "MaMM",  # a is not valid
+            "M*M*1",  # 1 is not valid
+        ]
+        for pattern in invalid_patterns:
+            with pytest.raises(ValueError, match="not a valid layer symbol"):
+                parse_hybrid_pattern(pattern)
+
+    def test_invalid_symbols_in_mtp_pattern(self):
+        """Test that invalid symbols in MTP pattern raise ValueError."""
+        # Single MTP depth with invalid symbol - should raise "not a valid layer symbol"
+        with pytest.raises(ValueError, match="not a valid layer symbol"):
+            parse_hybrid_pattern("M*M*/MX")  # X is not valid
+
+        # Multiple MTP depths with invalid symbol and matching patterns
+        with pytest.raises(ValueError, match="not a valid layer symbol"):
+            parse_hybrid_pattern("M*M*/Ma/Ma")  # a is not valid
+
+        # Multiple MTP depths with invalid symbol but mismatched patterns
+        # This raises "All MTP patterns must be identical" before checking symbols
+        with pytest.raises(ValueError, match="All MTP patterns must be identical"):
+            parse_hybrid_pattern("M*M*/MM/Ma")
+
+    def test_empty_main_pattern_with_mtp(self):
+        """Test pattern that starts with / (empty main pattern)."""
+        result = parse_hybrid_pattern("/MM/MM")
+        assert result.main_pattern is None
+        assert result.mtp_pattern == "MM"
+        assert result.mtp_num_depths == 2
+
+    def test_trailing_separator(self):
+        """Test patterns with trailing separator."""
+        # "M*M*/" means main="M*M*", one empty MTP pattern
+        result = parse_hybrid_pattern("M*M*/")
+        assert result.main_pattern == "M*M*"
+        # Empty string after separator means no valid MTP pattern
+        assert result.mtp_pattern is None
+        assert result.mtp_num_depths == 0
+
+    def test_complex_patterns(self):
+        """Test more complex realistic patterns."""
+        test_cases = [
+            # Main decoder with attention, MTP with mamba only
+            ("M*M*M*M*/MMM/MMM", "M*M*M*M*", "MMM", 2),
+            # Main decoder with MLP, MTP with attention+mamba
+            ("MM-MM-/*M/*M", "MM-MM-", "*M", 2),
+            # All attention main, mamba MTP
+            ("*****/M/M/M/M", "*****", "M", 4),
+            # MoE in main pattern
+            ("MEME/MM/MM", "MEME", "MM", 2),
+        ]
+        for pattern, expected_main, expected_mtp, expected_depths in test_cases:
+            result = parse_hybrid_pattern(pattern)
+            assert result.main_pattern == expected_main, f"Failed for pattern: {pattern}"
+            assert result.mtp_pattern == expected_mtp, f"Failed for pattern: {pattern}"
+            assert result.mtp_num_depths == expected_depths, f"Failed for pattern: {pattern}"
+
+    def test_dataclass_equality(self):
+        """Test that ParsedHybridPattern supports equality comparison."""
+        p1 = parse_hybrid_pattern("M*M*/MM/MM")
+        p2 = ParsedHybridPattern(main_pattern="M*M*", mtp_pattern="MM", mtp_num_depths=2)
+        assert p1 == p2
diff --git a/tests/unit_tests/ssm/test_mamba_layer.py b/tests/unit_tests/ssm/test_mamba_layer.py
index 25d24443d38..26c7ba4d92f 100644
--- a/tests/unit_tests/ssm/test_mamba_layer.py
+++ b/tests/unit_tests/ssm/test_mamba_layer.py
@@ -5,7 +5,8 @@
 
 from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.ssm.mamba_layer import MambaLayer
+from megatron.core.ssm.mamba_block import MambaStackSubmodules
+from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer import TransformerConfig
 from tests.unit_tests.test_utilities import Utils
@@ -25,9 +26,14 @@ def setup_method(self, method):
             num_attention_heads=1,
             use_cpu_initialization=True,
         )
-        modules = mamba_stack_spec.submodules.mamba_layer.submodules
+        assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules)
+        assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules)
         pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp'])
-        self.layer = MambaLayer(transformer_config, modules, pg_collection=pg_collection)
+        self.layer = MambaLayer(
+            transformer_config,
+            mamba_stack_spec.submodules.mamba_layer.submodules,
+            pg_collection=pg_collection,
+        )
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/ssm/test_mamba_mixer.py b/tests/unit_tests/ssm/test_mamba_mixer.py
index 587cfc66967..f32b62146f8 100644
--- a/tests/unit_tests/ssm/test_mamba_mixer.py
+++ b/tests/unit_tests/ssm/test_mamba_mixer.py
@@ -6,7 +6,9 @@
 from megatron.core.inference.contexts.static_context import StaticInferenceContext
 from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
 from megatron.core.process_groups_config import ProcessGroupCollection
-from megatron.core.ssm.mamba_mixer import MambaMixer
+from megatron.core.ssm.mamba_block import MambaStackSubmodules
+from megatron.core.ssm.mamba_layer import MambaLayerSubmodules
+from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer import TransformerConfig
 from tests.unit_tests.test_utilities import Utils
@@ -35,15 +37,20 @@ def get_mixer(self, tp_size=1, cp_size=1, use_mem_eff_path=True):
             num_layers=1,
             num_attention_heads=1,
             use_cpu_initialization=True,
+            use_mamba_mem_eff_path=use_mem_eff_path,
+        )
+        assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules)
+        assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules)
+        assert isinstance(
+            mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules,
+            MambaMixerSubmodules,
         )
-        modules = mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules
         pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp'])
         mixer = MambaMixer(
             transformer_config,
-            modules,
+            mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules,
             transformer_config.hidden_size,
             layer_number=1,
-            use_mem_eff_path=use_mem_eff_path,
             pg_collection=pg_collection,
         )
         mixer.cuda()
@@ -119,12 +126,17 @@ def test_error_check(self, hidden_size, ngroups, tp_size, expected_error_message
             use_cpu_initialization=True,
             mamba_num_groups=ngroups,
         )
-        submodules = mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules
+        assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules)
+        assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules)
+        assert isinstance(
+            mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules,
+            MambaMixerSubmodules,
+        )
         pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp'])
         with pytest.raises(AssertionError, match=expected_error_message):
             MambaMixer(
                 transformer_config,
-                submodules,
+                mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules,
                 transformer_config.hidden_size,
                 pg_collection=pg_collection,
             )
diff --git a/tests/unit_tests/test_inference.py b/tests/unit_tests/test_inference.py
index 5b2bc07e6e1..9474ac0475a 100644
--- a/tests/unit_tests/test_inference.py
+++ b/tests/unit_tests/test_inference.py
@@ -11,15 +11,19 @@
 pytest.importorskip("flask_restful")
 
 from megatron.core.inference.text_generation_server import MegatronServer
-from megatron.training import tokenizer
+from megatron.core.tokenizers import MegatronTokenizer
 from tests.unit_tests.inference.engines.test_static_engine import StaticInferenceEngineTestHarness
-from tests.unit_tests.test_tokenizer import GPT2_VOCAB_SIZE, gpt2_tiktok_vocab
 from tests.unit_tests.test_utilities import Utils
 
 
 @pytest.fixture(scope="module")
-def gpt2_tiktoken_tokenizer(gpt2_tiktok_vocab):
-    return tokenizer.build_tokenizer(gpt2_tiktok_vocab)
+def gpt2_tiktoken_tokenizer():
+    return MegatronTokenizer.from_pretrained(
+        tokenizer_path="/opt/data/tokenizers/tiktoken/tiktoken.vocab.json",
+        vocab_size=131072,
+        num_special_tokens=1000,
+        pattern="v1",
+    )
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/unit_tests/test_optimizer.py b/tests/unit_tests/test_optimizer.py
index 6b1da8c4e3f..2488900ba72 100644
--- a/tests/unit_tests/test_optimizer.py
+++ b/tests/unit_tests/test_optimizer.py
@@ -28,7 +28,7 @@
 from megatron.core.transformer import TransformerConfig
 from megatron.core.utils import is_te_min_version, is_torch_min_version
 from tests.unit_tests.test_utilities import Utils
-from tests.unit_tests.test_utils import _deinit_distributed, _init_distributed
+from tests.unit_tests.test_utils import _init_distributed
 
 try:
     # Check if FP8 block scaling is available.
@@ -326,6 +326,43 @@ def to_cuda(d):
     assert list(optimizer_2.state.values())[0]["momentum_buffer"].is_cuda
 
 
+def test_chained_optimizer_get_parameters():
+    """Test ChainedOptimizer.get_parameters() aggregates params from all sub-optimizers.
+
+    Regression test: without the get_parameters() override, ChainedOptimizer would
+    access self.optimizer which asserts only one optimizer exists, failing with VPP/MoE.
+    """
+
+    class MockOptimizer:
+        """Mock that mimics MegatronOptimizer's get_parameters() interface."""
+
+        def __init__(self, params):
+            self.params = list(params)
+            self.param_groups = [{"params": self.params}]
+
+        def get_parameters(self):
+            return self.params
+
+    net = Net()
+    all_params = list(net.parameters())
+
+    # Test empty
+    assert ChainedOptimizer([]).get_parameters() == []
+
+    # Test single optimizer
+    opt1 = MockOptimizer(all_params[:3])
+    assert ChainedOptimizer([opt1]).get_parameters() == opt1.params
+
+    # Test multiple optimizers (the case that previously failed)
+    opt2 = MockOptimizer(all_params[3:6])
+    opt3 = MockOptimizer(all_params[6:])
+    chained = ChainedOptimizer([opt1, opt2, opt3])
+    result = chained.get_parameters()
+
+    assert len(result) == len(all_params)
+    assert result == opt1.params + opt2.params + opt3.params
+
+
 def test_precision_aware_fused_adam():
     try:
         from transformer_engine.pytorch.optimizers import FusedAdam
@@ -492,6 +529,136 @@ def run_model(model, input, optim, fp8_recipe, fp8_recipe_settings):
     test_optim.load_state_dict(state_dict)
 
 
+@pytest.mark.parametrize("use_precision_aware", [True, False])
+def test_distrib_optimizer_save_load_with_non_tensor_state(use_precision_aware):
+    """Test that save/load of distributed optimizer handles non-tensor state entries.
+
+    Optimizers may store non-tensor entries (e.g. `found_inf: bool`) in the per-parameter
+    state dict. The distrib_optimizer's _get_main_param_and_optimizer_states and
+    _set_main_param_and_optimizer_states must skip these to avoid crashes when calling
+    tensor operations (.copy_(), get_unscaled_state, set_scaled_state) on non-tensors.
+
+    Tests both the precision-aware path (TE FusedAdam with scaled states) and the
+    non-precision-aware path (standard optimizer with .copy_()).
+    """
+    if use_precision_aware:
+        try:
+            from transformer_engine.pytorch.optimizers import FusedAdam
+        except ImportError:
+            pytest.skip("TE FusedAdam not available")
+
+        import inspect
+
+        adam_args = inspect.signature(FusedAdam).parameters
+        arg_names = [
+            "master_weight_dtype",
+            "exp_avg_dtype",
+            "exp_avg_sq_dtype",
+            "use_decoupled_grad",
+        ]
+        for name in arg_names:
+            if name not in adam_args:
+                pytest.skip("TE FusedAdam does not support precision-aware args")
+
+    world = int(os.getenv('WORLD_SIZE', '1'))
+    rank = int(os.getenv('RANK', '0'))
+
+    _init_distributed(world, rank)
+    Utils.initialize_model_parallel()
+
+    model = torch.nn.Linear(100, 100, bias=False, dtype=torch.bfloat16, device='cuda')
+    model.requires_grad_(True)
+    model.weight.data.fill_(1.0)
+    ddp_config = DistributedDataParallelConfig(use_distributed_optimizer=True)
+    model = DistributedDataParallel(
+        TransformerConfig(num_attention_heads=1, num_layers=1), ddp_config, model
+    )
+
+    optimizer_config = OptimizerConfig(
+        optimizer='adam',
+        lr=0.01,
+        bf16=True,
+        use_distributed_optimizer=True,
+        use_precision_aware_optimizer=use_precision_aware,
+        main_params_dtype=torch.float32,
+        main_grads_dtype=torch.float32,
+        exp_avg_dtype=torch.float32,
+        exp_avg_sq_dtype=torch.float32,
+    )
+    optim = get_megatron_optimizer(optimizer_config, [model])
+
+    # Run a training step to populate optimizer state
+    input_data = torch.randn(8, 100, dtype=torch.bfloat16, device='cuda')
+    output = model(input_data)
+    loss = output.sum()
+    loss.backward()
+    optim.step()
+
+    # Access the underlying distrib_optimizer
+    distrib_optim = optim.chained_optimizers[0]
+    if use_precision_aware:
+        assert distrib_optim.config.use_precision_aware_optimizer_no_fp8_or_ds_fp8
+
+    # Inject non-tensor entries into optimizer state (simulates found_inf, etc.)
+    inner_optimizer = distrib_optim.optimizer
+    for param in inner_optimizer.state:
+        inner_optimizer.state[param]['found_inf'] = False
+        inner_optimizer.state[param]['non_tensor_int'] = 42
+
+    # Test 1: _get_main_param_and_optimizer_states should skip non-tensor entries
+    for gbuf_range_maps in distrib_optim.gbuf_ranges:
+        for gbuf_range_map_for_all_buckets in gbuf_range_maps.values():
+            for gbuf_range_map in gbuf_range_map_for_all_buckets:
+                for model_param in gbuf_range_map["param_map"]:
+                    tensors = distrib_optim._get_main_param_and_optimizer_states(model_param)
+                    for k, v in tensors.items():
+                        assert isinstance(
+                            v, torch.Tensor
+                        ), f"Non-tensor value for key '{k}': {type(v)}"
+
+    # Test 2: Full save/load roundtrip via dp_reshardable path
+    saved_state = distrib_optim.get_parameter_state_dp_reshardable()
+
+    # Verify saved state doesn't contain non-tensor entries (except metadata keys)
+    metadata_keys = {"per_bucket_numel", "per_bucket_numel_unpadded"}
+    for key, value in saved_state.items():
+        if key in metadata_keys:
+            continue
+        for dtype, buckets_state in value.items():
+            for bucket_state in buckets_state:
+                for param_dict in bucket_state:
+                    for k, v in param_dict.items():
+                        if k in ('gbuf_local_start', 'gbuf_local_end', 'padding'):
+                            continue
+                        assert isinstance(
+                            v, torch.Tensor
+                        ), f"Non-tensor in saved state key '{k}': {type(v)}"
+
+    # Test 3: load_parameter_state_from_dp_reshardable should not crash
+    # Add 'padding' key required by the load path (normally added by fully_reshardable save)
+    for key, value in saved_state.items():
+        if key in metadata_keys:
+            continue
+        for dtype, buckets_state in value.items():
+            for bucket_state in buckets_state:
+                for param_dict in bucket_state:
+                    param_dict['padding'] = False
+    distrib_optim.load_parameter_state_from_dp_reshardable(saved_state)
+
+    # Test 4: Inject non-tensor entries directly into the saved state and verify load handles them
+    for key, value in saved_state.items():
+        if key in metadata_keys:
+            continue
+        for dtype, buckets_state in value.items():
+            for bucket_state in buckets_state:
+                for param_dict in bucket_state:
+                    param_dict['found_inf'] = False
+                    param_dict['step_count'] = 42
+
+    # This should not crash - non-tensor entries should be skipped
+    distrib_optim.load_parameter_state_from_dp_reshardable(saved_state)
+
+
 @pytest.mark.parametrize("use_distributed_optimizer", [False, True])
 @pytest.mark.parametrize("precision", ['bf16', 'fp32'])
 def test_optim_sharded_state_dict(use_distributed_optimizer: bool, precision: str):
diff --git a/tests/unit_tests/test_optimizer_cpu_offloading.py b/tests/unit_tests/test_optimizer_cpu_offloading.py
index 1c367100dab..33febbb3eb0 100644
--- a/tests/unit_tests/test_optimizer_cpu_offloading.py
+++ b/tests/unit_tests/test_optimizer_cpu_offloading.py
@@ -39,6 +39,28 @@ def forward(self, x):
         return x
 
 
+class BigNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 2048)
+        self.fc2 = nn.Linear(2048, 8192)
+        self.fc3 = nn.Linear(8192, 2048)
+        self.fc4 = nn.Linear(2048, 100)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)  # flatten all dimensions except batch
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.relu(self.fc3(x))
+        x = self.fc4(x)
+        return x
+
+
 def setup_seed(seed):
     random.seed(seed)  # Set Python's built-in random seed
     np.random.seed(seed)  # Set NumPy's random seed
@@ -139,3 +161,95 @@ def test_multi_device_hybrid_optimizer(
         assert torch.allclose(
             v, ref_params[k], atol=1e-03
         ), f"Weight {k} value mismatch, max error: {(v - ref_params[k]).abs().max()}"
+
+
+@pytest.mark.skipif(
+    torch.__version__ < '2.3.0',
+    reason=(
+        "Requires PyTorch 2.3.0 or higher, lower versions of pytorch have "
+        "misaligned optimizer accuracy for CPU and GPU."
+    ),
+)
+@pytest.mark.parametrize('n_steps', [1, 10])
+@pytest.mark.parametrize('offload_fraction', [1, 0.5, 0])
+@pytest.mark.parametrize('optimizer', ['adam', 'sgd'])
+@pytest.mark.parametrize('with_param_groups', [False, True])
+def test_overlap_cpu_optimizer_d2h_h2d_sync_correctness(
+    with_param_groups, optimizer, offload_fraction, n_steps
+):
+    setup_seed(42)
+    net1 = BigNet().cuda()
+    net2 = BigNet().cuda()
+    net2.load_state_dict(net1.state_dict())
+    base_lr = 1e-3
+    params = list(net1.parameters())
+    ref_params = list(net2.parameters())
+    if with_param_groups:
+        param_groups = [
+            {"params": params[: len(params) // 2], "wd_mult": 1.0, "lr_mult": 1e-4},
+            {"params": params[len(params) // 2 :], "wd_mult": 0.0, "lr_mult": 2e-4},
+        ]
+        params = param_groups
+        ref_param_groups = [
+            {"params": ref_params[: len(ref_params) // 2], "wd_mult": 1.0, "lr_mult": 1e-4},
+            {"params": ref_params[len(ref_params) // 2 :], "wd_mult": 0.0, "lr_mult": 2e-4},
+        ]
+        ref_params = ref_param_groups
+
+    if optimizer == 'adam':
+        cls_kwargs = dict(cpu_optimizer_cls=Adam, gpu_optimizer_cls=GPUAdam)
+    else:
+        cls_kwargs = dict(cpu_optimizer_cls=SGD, gpu_optimizer_cls=GPUSGD)
+
+    hdo = HybridDeviceOptimizer(
+        params,
+        offload_fraction=offload_fraction,
+        lr=base_lr,
+        overlap_cpu_optimizer_d2h_h2d=True,
+        **cls_kwargs,
+    )
+
+    ref_optimizer = cls_kwargs['gpu_optimizer_cls'](ref_params, lr=base_lr)
+
+    # 1. run step on optimizer, make sure there is state generated
+    assert len(hdo.state_dict()["state"]) == 0  # state is empty
+    input = torch.randn(1, 3, 32, 32).cuda()
+    output = net1(input)
+    output.sum().backward()
+    hdo.step()
+    output = net2(input)
+    output.sum().backward()
+    ref_optimizer.step()
+    # PyTorch SGD will not generate state
+    if optimizer != 'sgd':
+        assert len(hdo.state_dict()["state"]) != 0
+
+    # 2. check the state is on right device
+    if optimizer == 'adam':
+        first_param_id = hdo.state_dict()["param_groups"][0]["params"][0]
+        last_param_id = hdo.state_dict()["param_groups"][-1]["params"][-1]
+        if offload_fraction > 0:
+            assert not hdo.state_dict()["state"][first_param_id]["exp_avg"].is_cuda
+        if offload_fraction < 1:
+            assert hdo.state_dict()["state"][last_param_id]["exp_avg"].is_cuda
+
+    inputs = [torch.randn(1, 3, 32, 32).cuda() for _ in range(1, n_steps)]
+    for i in range(1, n_steps):
+        output = net1(inputs[i - 1])
+        output.sum().backward()
+        hdo.step()
+
+    for i in range(1, n_steps):
+        output = net2(inputs[i - 1])
+        output.sum().backward()
+        ref_optimizer.step()
+
+    params = net1.state_dict()
+    ref_params = net2.state_dict()
+    for k, v in params.items():
+        assert (v.isnan() == ref_params[k].isnan()).all()
+        torch.nan_to_num_(v, 0)
+        torch.nan_to_num_(ref_params[k], 0)
+        assert torch.allclose(
+            v, ref_params[k], atol=1e-03
+        ), f"Weight {k} value mismatch, max error: {(v - ref_params[k]).abs().max()}"
diff --git a/tests/unit_tests/test_optimizer_param_scheduler.py b/tests/unit_tests/test_optimizer_param_scheduler.py
index 9b781694546..670ca92c992 100644
--- a/tests/unit_tests/test_optimizer_param_scheduler.py
+++ b/tests/unit_tests/test_optimizer_param_scheduler.py
@@ -1,10 +1,13 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
 import math
 from unittest.mock import MagicMock
 
 import pytest
 
-from megatron.core.optimizer_param_scheduler import (  # Adjust import according to your module path
+from megatron.core.optimizer_param_scheduler import (
     OptimizerParamScheduler,
+    get_canonical_lr_for_logging,
 )
 
 
@@ -182,6 +185,51 @@ def test_step_function(mock_optimizer):
     assert math.isclose(param_group['weight_decay'], 0.01, rel_tol=1e-5)
 
 
+def test_step_updates_empty_param_groups():
+    """Empty param groups (rank-alignment stubs) must still receive lr updates.
+
+    get_canonical_lr_for_logging reads lr from default_config groups regardless
+    of whether they hold parameters, so step() must not skip them.
+    """
+    optimizer = MagicMock()
+    # lr and weight_decay are set by the scheduler's step() method
+    optimizer.param_groups = [
+        # Non-default group with its own max_lr override (lr will differ from the canonical schedule)
+        {'params': [1, 2], "min_lr": 0.001, "max_lr": 0.2, "default_config": False},
+        # Model parallelism may leave default_config groups empty on some ranks
+        {'params': [], "wd_mult": 0.0, 'default_config': True},
+    ]
+    scheduler = OptimizerParamScheduler(
+        optimizer=optimizer,
+        init_lr=0.01,
+        max_lr=0.1,
+        min_lr=0.001,
+        lr_warmup_steps=100,
+        lr_decay_steps=1000,
+        lr_decay_style='linear',
+        start_wd=0.0,
+        end_wd=0.1,
+        wd_incr_steps=1000,
+        wd_incr_style='linear',
+    )
+
+    scheduler.step(100)
+    non_empty, empty = optimizer.param_groups
+
+    # Verify learning rates: at step 100 warmup is complete so lr == max_lr
+    assert "lr" in non_empty, "non-empty param group must have an lr"
+    assert "lr" in empty, "empty param group must have an lr"
+    assert non_empty['lr'] == pytest.approx(0.2)  # warmup complete → this group's max_lr override
+    assert empty['lr'] == pytest.approx(0.1)  # warmup complete → scheduler's default max_lr
+    assert get_canonical_lr_for_logging(optimizer.param_groups) == pytest.approx(0.1)
+
+    # Verify weight decay: linear from 0.0 to 0.1 over 1000 steps → base wd is 0.01 at step 100
+    assert "weight_decay" in non_empty, "non-empty param group must have a weight decay"
+    assert "weight_decay" in empty, "empty param group must have a weight decay"
+    assert non_empty['weight_decay'] == pytest.approx(0.01)  # base wd, no wd_mult override
+    assert empty['weight_decay'] == pytest.approx(0.0)  # base wd * wd_mult=0.0
+
+
 def test_state_dict(mock_optimizer):
     scheduler = OptimizerParamScheduler(
         optimizer=mock_optimizer,
@@ -249,3 +297,54 @@ def test_load_state_dict(mock_optimizer):
     assert scheduler.end_wd == 0.2
     assert scheduler.wd_incr_steps == 500
     assert scheduler.wd_incr_style == 'cosine'
+
+
+# ── get_canonical_lr_for_logging tests ──────────────────────────────────────
+#
+# Returns the lr of the first default_config=True param group.  In practice
+# the scheduler always sets a valid lr on every group (including empty
+# rank-alignment stubs), so a default_config=True group with a float lr is
+# always present.
+
+
+class TestGetCanonicalLrForLogging:
+    """Tests for get_canonical_lr_for_logging."""
+
+    def test_single_default_config_group(self):
+        """Typical case: one default_config group with a valid lr."""
+        param_groups = [{'lr': 0.05, 'default_config': True}]
+        assert get_canonical_lr_for_logging(param_groups) == 0.05
+
+    def test_default_config_with_non_default_groups(self):
+        """default_config group is returned even when non-default groups are present."""
+        param_groups = [{'lr': 0.001, 'default_config': True}, {'lr': 0.999}]
+        assert get_canonical_lr_for_logging(param_groups) == 0.001
+
+    def test_default_config_after_non_default(self):
+        """default_config group is found even when it is not first in the list."""
+        param_groups = [{'lr': 0.50}, {'lr': 0.01, 'default_config': True}]
+        assert get_canonical_lr_for_logging(param_groups) == 0.01
+
+    def test_no_default_config_groups(self):
+        """Returns None when no group has default_config=True."""
+        param_groups = [{'lr': 0.50}, {'lr': 0.01}]
+        assert get_canonical_lr_for_logging(param_groups) is None
+
+    def test_missing_lr_key(self):
+        """Returns None (not KeyError) when the default_config group lacks an 'lr' key."""
+        param_groups = [{'default_config': True}]
+        assert get_canonical_lr_for_logging(param_groups) is None
+
+    def test_empty_param_groups(self):
+        """Returns None when there are no param groups at all."""
+        assert get_canonical_lr_for_logging([]) is None
+
+    def test_no_default_config_no_lr(self):
+        """Returns None when groups exist but none are default_config."""
+        param_groups = [{'params': []}]
+        assert get_canonical_lr_for_logging(param_groups) is None
+
+    def test_lr_zero_is_valid(self):
+        """lr=0.0 is a legitimate value, not to be confused with None."""
+        param_groups = [{'lr': 0.0, 'default_config': True}]
+        assert get_canonical_lr_for_logging(param_groups) == 0.0
diff --git a/tests/unit_tests/test_tokenizer.py b/tests/unit_tests/test_tokenizer.py
deleted file mode 100644
index 556cef81407..00000000000
--- a/tests/unit_tests/test_tokenizer.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-
-import base64
-import json
-from argparse import Namespace
-from pathlib import Path
-
-import numpy as np
-import pytest
-import requests
-
-from megatron.training import tokenizer
-from megatron.training.tokenizer.gpt2_tokenization import PRETRAINED_VOCAB_ARCHIVE_MAP
-from megatron.training.tokenizer.multimodal_tokenizer import MultimodalTokenizer
-
-TOKENIZER_DIR = Path("~/data/tokenizers").expanduser()
-
-# Copied over from test_preprocess_data.py
-from tests.unit_tests.data.test_preprocess_data import __LOCAL_GPT2_VOCAB
-
-GPT2_VOCAB_SIZE = 32768
-
-
-def offsets_to_substrs(offsets, string):
-    return [string[start:end] for start, end in zip([0] + offsets, offsets + [len(string)])]
-
-
-def local_test_specs():
-    return [
-        Namespace(
-            rank=0,
-            tensor_model_parallel_size=8,
-            make_vocab_size_divisible_by=128,
-            tokenizer_type="GPTSentencePieceTokenizer",
-            tokenizer_model=f"{TOKENIZER_DIR}/nemotron_2_256k.model",
-        ),
-        Namespace(
-            rank=0,
-            vocab_size=131072,
-            make_vocab_size_divisible_by=128,
-            tensor_model_parallel_size=8,
-            tokenizer_type="TikTokenizer",
-            tokenizer_model=f"{TOKENIZER_DIR}/multiMixV8.gpt4o_nc_sd.500000.128k.vocab.json",
-            tiktoken_pattern="v2",
-            tiktoken_num_special_tokens=1000,
-            tokenizer_special_tokens=["<unk>", "<s>", "</s>"],
-        ),
-        Namespace(
-            rank=0,
-            vocab_size=131072,
-            make_vocab_size_divisible_by=128,
-            tensor_model_parallel_size=8,
-            tokenizer_type="TikTokenizer",
-            tokenizer_model=f"{TOKENIZER_DIR}/multiMixV5_fix_default_500000_128k.vocab.json",
-            tiktoken_pattern="v1",
-            tiktoken_num_special_tokens=1000,
-            tokenizer_special_tokens=["<unk>", "<s>", "</s>"],
-        ),
-        Namespace(
-            rank=0,
-            vocab_size=128000,
-            make_vocab_size_divisible_by=128,
-            tensor_model_parallel_size=8,
-            tokenizer_type="HuggingFaceTokenizer",
-            tokenizer_model="meta-llama/Llama-2-7b-hf",
-        ),
-        Namespace(
-            rank=0,
-            vocab_size=128000,
-            make_vocab_size_divisible_by=128,
-            tensor_model_parallel_size=8,
-            tokenizer_type="HuggingFaceTokenizer",
-            tokenizer_model="meta-llama/Meta-Llama-3.1-8B",
-        ),
-    ]
-
-
-@pytest.fixture(scope="session")
-def gpt2_tiktok_vocab(tmp_path_factory):
-
-    if Path(__LOCAL_GPT2_VOCAB).exists():
-        with open(__LOCAL_GPT2_VOCAB, "r", encoding="utf-8") as reader:
-            gpt2_vocab = json.load(reader)
-    else:
-        gpt2_vocab = json.loads(requests.get(PRETRAINED_VOCAB_ARCHIVE_MAP["gpt2"]).content)
-
-    N = 256
-    tiktok_vocab = [
-        {"token_bytes": base64.b64encode(bytes([i])).decode("utf-8"), "token_str": str(i)}
-        for i in range(N)
-    ]
-    tiktok_vocab_bytes = {x["token_bytes"] for x in tiktok_vocab}
-
-    tiktok_vocab += [
-        {"token_bytes": base64.b64encode(token.encode('utf-8')).decode("utf-8"), "token_str": token}
-        for token in gpt2_vocab
-        if base64.b64encode(token.encode('utf-8')).decode("utf-8") not in tiktok_vocab_bytes
-    ]
-
-    for i, entry in enumerate(tiktok_vocab):
-        entry["rank"] = i
-
-    for i, x in enumerate(tiktok_vocab):
-        assert x.keys() == {"rank", "token_bytes", "token_str"}
-        assert x["rank"] == i
-        merge = base64.b64decode(x["token_bytes"])
-        assert i >= 256 or merge == bytes([i]), f"{i} {merge} {bytes([i])}"
-
-    file_name = tmp_path_factory.mktemp("data") / "gpt2_vocab.json"
-    with open(file_name, "w") as f:
-        json.dump(tiktok_vocab, f)
-
-    return Namespace(
-        rank=0,
-        vocab_size=32768,
-        make_vocab_size_divisible_by=128,
-        tensor_model_parallel_size=8,
-        tokenizer_type="TikTokenizer",
-        tokenizer_model=str(file_name),
-        tiktoken_pattern="v1",
-        tiktoken_num_special_tokens=1000,
-        tokenizer_special_tokens=["<unk>", "<s>", "</s>"],
-    )
-
-
-@pytest.mark.parametrize("args", local_test_specs())
-def test_tokenizer(args):
-    if not TOKENIZER_DIR.exists():
-        pytest.skip("Skipping tokenizer tests because the tokenizer directory does not exist")
-
-    tok = tokenizer.build_tokenizer(args)
-    run_tokenizer_tests(tok)
-
-
-def test_gpt2_tiktok_tokenizer(gpt2_tiktok_vocab):
-    tok = tokenizer.build_tokenizer(gpt2_tiktok_vocab)
-    run_tokenizer_tests(tok)
-
-
-def run_tokenizer_tests(tok):
-    string1 = (
-        "The following are multiple choice questions (with answers) about college biology.\n"
-        "Monoclonal antisera are distinguished from polyclonal antisera in which of the "
-        "following ways?\n"
-        "A. Each type of antibody in a monoclonal antiserum reacts against a single region of "
-        "a single antigen; each type of antibody in a polyclonal antiserum reacts against "
-        "multiple regions of different antigens.\n"
-        "B. A monoclonal antibody reacts against multiple regions of a single antigen; a "
-        "polyclonal antibody reacts against a single region of related antigens.\n"
-        "C. A monoclonal antiserum contains antibodies secreted from the descendants of a "
-        "single B lymphocyte; a polyclonal antiserum contains antibodies secreted from the "
-        "descendants of different B lymphocytes.\n"
-        "D. A monoclonal antiserum contains antibodies secreted from the descendants of a "
-        "single B lymphocyte; a polyclonal antiserum contains antibodies secreted from the "
-        "descendants of both B and T lymphocytes.\n"
-        "Answer: C"
-    )
-    string2 = "Жизнь прекрасна и удивительна"
-    string3 = "お誕生日おめでとう"
-    strings = [string1, string2, string3]
-
-    for test_string in strings:
-        toks = tok.tokenize(test_string)
-        offsets = tok.offsets(toks, test_string)
-        dec = offsets_to_substrs(offsets, test_string)
-        detok_str = ''.join(dec)
-        # the following is not necessarily true by construction above,
-        # since the many tokenizers may operate at the byte level and not
-        # only at the character level.
-        assert (
-            detok_str == test_string
-        ), f"Detokenized string {detok_str} does not match original {test_string}"
-        assert len(toks) == len(
-            offsets
-        ), f"Tokenized string {toks} does not match original {offsets}"
-
-
-def test_null_tokenizer():
-    args = Namespace(
-        tokenizer_type="NullTokenizer",
-        rank=0,
-        vocab_size=128000,
-        make_vocab_size_divisible_by=128,
-        tensor_model_parallel_size=8,
-    )
-    tok = tokenizer.build_tokenizer(args)
-    test_string = "1 23 456 789"
-    toks = tok.tokenize(test_string)
-    offsets = tok.offsets(toks, test_string)
-    dec = offsets_to_substrs(offsets, test_string)
-    detok_str = ''.join(dec)
-
-    assert (
-        detok_str == test_string
-    ), f"Detokenized string {detok_str} does not match original {test_string}"
-    assert len(toks) == len(offsets), f"Tokenized string {toks} does not match original {offsets}"
-
-
-class MockUnderlyingTokenizer:
-    """Mock tokenizer for testing purposes."""
-
-    def __init__(self):
-        self.pad_token_id = 256
-
-    def __len__(self):
-        return 256
-
-    def encode(self, text: str) -> list[int]:
-        """Convert text to a list of token IDs."""
-        return [ord(c) for c in text]
-
-    def decode(self, tokens: list[int]) -> str:
-        """Convert list of token IDs to plaintext."""
-        return "".join([chr(t) for t in tokens])
-
-    def apply_chat_template(self, conversation: list[dict], *args, **kwargs) -> list[int]:
-        """Convert a conversation to token IDs."""
-        out = []
-        for turn in conversation:
-            turn_tokens = self.encode(f"{turn['role']}:{turn['content']}")
-            out.extend(turn_tokens)
-
-        if kwargs.get("return_tensors", None) == "np":
-            return [np.array(out)]
-
-        return out
-
-    def convert_tokens_to_ids(self, text: str) -> list[int]:
-        """Convert plaintext to token IDs."""
-        return self.encode(text)
-
-    def add_tokens(self, extra_tokens: list[str], *args, **kwargs) -> int:
-        """Add tokens to the tokenizer. No-op for this mock tokenizer."""
-        return len(extra_tokens)
-
-
-def test_multimodal_tokenizer():
-    """Test MultimodalTokenizer."""
-    underlying = MockUnderlyingTokenizer()
-    prompt_format = "chatml"
-    special_tokens = ["<image>"]
-    image_tag_type = ""
-    tokenizer = MultimodalTokenizer(underlying, prompt_format, special_tokens, image_tag_type)
-
-    # Simple encode - decode roundtrip.
-    assert (
-        tokenizer.detokenize(tokenizer.tokenize("abc")) == "abc"
-    ), "encode-decode roundtrip failed"
-
-    # Apply chat template.
-    conversation = [
-        {"role": "system", "content": "abc"},
-        {"role": "user", "content": "123<image>"},
-        {"role": "assistant", "content": "xyz"},
-    ]
-    conv_tokens = tokenizer.tokenize_conversation(
-        conversation, return_target=False, add_generation_prompt=False
-    )
-    assert len(conv_tokens) > 0, "failed to tokenize conversation"
-
-    conv_tokens, target_tokens = tokenizer.tokenize_conversation(
-        conversation, return_target=True, add_generation_prompt=True
-    )
-    assert len(conv_tokens) > 0 and len(conv_tokens) == len(
-        target_tokens
-    ), "failed to tokenize conversation and return target tokens"
-
-    # Try converting tokens to ids.
-    assert tokenizer.convert_tokens_to_ids("a"), "failed to convert tokens to ids."
-
-    # Try image tags.
-    image_tag_type = "nvlm"
-    tokenizer = MultimodalTokenizer(underlying, prompt_format, special_tokens, image_tag_type)
-
-    assert tokenizer._apply_image_tag("<image>hello") == "<Image><image></Image>hello"
-    assert tokenizer._apply_image_tag([{"role": "user", "content": "<image>hello"}]) == [
-        {"role": "user", "content": "<Image><image></Image>hello"}
-    ]
diff --git a/tests/unit_tests/test_training.py b/tests/unit_tests/test_training.py
index 2fd85724826..6f94296ad89 100644
--- a/tests/unit_tests/test_training.py
+++ b/tests/unit_tests/test_training.py
@@ -6,9 +6,9 @@
 
 import torch
 
+from megatron.core.tokenizers.utils.build_tokenizer import vocab_size_with_padding
 from megatron.training.checkpointing import save_grads
 from megatron.training.global_vars import set_args
-from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
 from megatron.training.training import build_train_valid_test_data_iterators
 from tests.unit_tests.dist_checkpointing import TempNamedDir
 from tests.unit_tests.test_utilities import Utils
@@ -67,16 +67,18 @@ def old_round_impl(after, multiple):
         for vocab in range(1, 600000, 1000):
             for mult in [1, 17, 32, 64, 128]:
                 args.make_vocab_size_divisible_by = mult
-                assert old_round_impl(vocab, mult) == _vocab_size_with_padding(
-                    vocab, args, False
-                ), (vocab, mult)
+                assert old_round_impl(vocab, mult) == vocab_size_with_padding(vocab, args, False), (
+                    vocab,
+                    mult,
+                )
 
         for vocab in range(1, 10_000, 500):
             for mult in range(1, 1024 + 1):
                 args.make_vocab_size_divisible_by = mult
-                assert old_round_impl(vocab, mult) == _vocab_size_with_padding(
-                    vocab, args, False
-                ), (vocab, mult)
+                assert old_round_impl(vocab, mult) == vocab_size_with_padding(vocab, args, False), (
+                    vocab,
+                    mult,
+                )
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/test_typed_torch.py b/tests/unit_tests/test_typed_torch.py
new file mode 100644
index 00000000000..1c3e77fbac1
--- /dev/null
+++ b/tests/unit_tests/test_typed_torch.py
@@ -0,0 +1,184 @@
+# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+import inspect
+from typing import Any
+
+import pytest
+
+from megatron.core.typed_torch import copy_signature, not_none
+
+
+def source_func(a: int, *, b: str) -> str:
+    """Sample function to copy the signature from."""
+    return str(a) + b
+
+
+class SourceClass:
+    """Sample class with a method to copy the signature from."""
+
+    def method(self, a: int, *, b: str) -> str:
+        """Sample method to copy the signature from."""
+        return str(a) + b
+
+
+@copy_signature(source_func)
+def dest_func_from_func(*args: Any, **kwargs: Any) -> list[str]:
+    """Function with copied signature from source_func."""
+    return [source_func(*args, **kwargs)]
+
+
+@copy_signature(source_func, handle_return_type='overwrite')
+def dest_func_from_func_overwrite(*args: Any, **kwargs: Any) -> object:
+    """Function with copied signature from source_func, but overwritten return type."""
+    return source_func(*args, **kwargs)
+
+
+@copy_signature(SourceClass.method, handle_first_src_param='skip')
+def dest_func_from_method(*args: Any, **kwargs: Any) -> int:
+    """Function with copied signature from SourceClass.method."""
+    return len(SourceClass().method(*args, **kwargs))
+
+
+@copy_signature(SourceClass.method, handle_return_type='overwrite', handle_first_src_param='skip')
+def dest_func_from_method_overwrite(*args: Any, **kwargs: Any) -> object:
+    """Function with copied signature from SourceClass.method, but overwritten return type."""
+    return SourceClass().method(*args, **kwargs)
+
+
+class DestClass:
+    """Class with methods that have copied signatures."""
+
+    @copy_signature(source_func, handle_first_dst_param='preserve')
+    def dest_method_from_func(self, *args: Any, **kwargs: Any) -> list[str]:
+        """Method with copied signature from source_func."""
+        return [source_func(*args, **kwargs)]
+
+    @copy_signature(source_func, handle_return_type='overwrite', handle_first_dst_param='preserve')
+    def dest_method_from_func_overwrite(self, *args: Any, **kwargs: Any) -> object:
+        """Method with copied signature from source_func, but overwritten return type."""
+        return source_func(*args, **kwargs)
+
+    @classmethod
+    @copy_signature(
+        SourceClass.method, handle_first_src_param='skip', handle_first_dst_param='preserve'
+    )
+    def dest_method_from_method(cls, *args: Any, **kwargs: Any) -> int:
+        """Class method with copied signature from SourceClass.method."""
+        return len(SourceClass().method(*args, **kwargs))
+
+    @copy_signature(
+        SourceClass.method,
+        handle_return_type='overwrite',
+        handle_first_src_param='skip',
+        handle_first_dst_param='preserve',
+    )
+    def dest_method_from_method_overwrite(self, *args: Any, **kwargs: Any) -> object:
+        """Method with copied signature from SourceClass.method, but overwritten return type."""
+        return SourceClass().method(*args, **kwargs)
+
+
+class TestCopySignature:
+    def test_original_return_type(self):
+        """Test that the original return types are preserved."""
+        f2f: list[str] = dest_func_from_func(1, b='a')
+        assert f2f == ['1a']
+        assert inspect.signature(dest_func_from_func) == inspect.Signature(
+            [
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=list[str],
+        )
+
+        m2f: int = dest_func_from_method(1, b='a')
+        assert m2f == 2
+        assert inspect.signature(dest_func_from_method) == inspect.Signature(
+            [
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=int,
+        )
+
+        f2m: list[str] = DestClass().dest_method_from_func(
+            1, b='a'
+        ) + DestClass.dest_method_from_func(DestClass(), 1, b='a')
+        assert f2m == ['1a', '1a']
+        assert inspect.signature(DestClass.dest_method_from_func) == inspect.Signature(
+            [
+                inspect.Parameter('self', inspect.Parameter.POSITIONAL_OR_KEYWORD),
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=list[str],
+        )
+
+        m2m: int = DestClass.dest_method_from_method(1, b='a')
+        assert m2m == 2
+        assert inspect.signature(DestClass.dest_method_from_method) == inspect.Signature(
+            [
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=int,
+        )
+
+    def test_overwritten_return_type(self):
+        """Test that the return types are overwritten correctly."""
+        f2f: str = dest_func_from_func_overwrite(1, b='a')
+        assert f2f == '1a'
+        assert inspect.signature(dest_func_from_func_overwrite) == inspect.Signature(
+            [
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=str,
+        )
+
+        m2f: str = dest_func_from_method_overwrite(1, b='a')
+        assert m2f == '1a'
+        assert inspect.signature(dest_func_from_method_overwrite) == inspect.Signature(
+            [
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=str,
+        )
+
+        f2m: str = DestClass().dest_method_from_func_overwrite(
+            1, b='a'
+        ) + DestClass.dest_method_from_func_overwrite(DestClass(), 1, b='a')
+        assert f2m == '1a1a'
+        assert inspect.signature(DestClass.dest_method_from_func_overwrite) == inspect.Signature(
+            [
+                inspect.Parameter('self', inspect.Parameter.POSITIONAL_OR_KEYWORD),
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=str,
+        )
+
+        m2m: str = DestClass().dest_method_from_method_overwrite(1, b='a')
+        assert m2m == '1a'
+        assert inspect.signature(DestClass.dest_method_from_method_overwrite) == inspect.Signature(
+            [
+                inspect.Parameter('self', inspect.Parameter.POSITIONAL_OR_KEYWORD),
+                inspect.Parameter('a', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=int),
+                inspect.Parameter('b', inspect.Parameter.KEYWORD_ONLY, annotation=str),
+            ],
+            return_annotation=str,
+        )
+
+
+class TestNotNone:
+    """Tests not_none."""
+
+    def test_none(self):
+        """Test that passing None raises a ValueError."""
+        with pytest.raises(ValueError, match=r'Expected value to be not None'):
+            not_none(None)
+
+    def test_not_none(self):
+        """Test that passing a non-None value returns the value."""
+        value = 42
+        result = not_none(value)
+        assert result == value
diff --git a/tests/unit_tests/test_utils.py b/tests/unit_tests/test_utils.py
index fddde8dd36e..dc554612811 100644
--- a/tests/unit_tests/test_utils.py
+++ b/tests/unit_tests/test_utils.py
@@ -15,7 +15,9 @@
 import megatron.training.utils as training_util
 from megatron.core import config
 from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.moe.moe_layer import MoELayer
@@ -313,12 +315,12 @@ def test_param_norm_moe(use_distributed_optimizer: bool):
         add_bias_linear=False,
         bf16=True,
     )
-    transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
-        num_experts=2, moe_grouped_gemm=True
-    )
-    model = MoELayer(transformer_config, transformer_layer_spec.submodules.mlp.submodules).to(
-        device='cuda'
-    )
+    model = MoELayer(
+        transformer_config,
+        get_gpt_layer_with_transformer_engine_submodules(
+            num_experts=2, moe_grouped_gemm=True
+        ).mlp.submodules,
+    ).to(device='cuda')
     model.requires_grad_(True)
     # Initialize the model with all 1.0 for weights.
     for param in model.parameters():
diff --git a/tests/unit_tests/tokenizers/test_tokenizer.py b/tests/unit_tests/tokenizers/test_tokenizer.py
index bed9f5fef54..f38674c6329 100755
--- a/tests/unit_tests/tokenizers/test_tokenizer.py
+++ b/tests/unit_tests/tokenizers/test_tokenizer.py
@@ -1,5 +1,4 @@
-import json
-import sys
+# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
 
 import pytest
 import torch
@@ -7,6 +6,15 @@
 
 from megatron.core.tokenizers import MegatronTokenizer
 
+try:
+    from megatron.core.tokenizers.text.libraries.huggingface_tokenizer import (
+        HAVE_TRANSFORMERS,
+        HuggingFaceTokenizer,
+    )
+except Exception:
+    HAVE_TRANSFORMERS = False
+    HuggingFaceTokenizer = None
+
 
 def get_conversation():
     return [
@@ -103,6 +111,7 @@ def test_hf_tokenizer():
         "/opt/data/tokenizers/huggingface",
         metadata_path=metadata,
         chat_template=chat_template,
+        include_special_tokens=False,
         **special_tokens,
     )
 
@@ -112,6 +121,46 @@ def test_hf_tokenizer():
     assert tokenizer.vocab_size == 128258
 
 
+# HuggingFaceTokenizer.ids_to_text and include_special_tokens (--tokenizer-hf-include-special-tokens).
+# Uses same local path as test_hf_tokenizer; tests EOS stripping vs keeping in detokenized output (e.g. RL).
+LOCAL_HF_TOKENIZER_PATH = "/opt/data/tokenizers/huggingface"
+
+
+def _eos_in_text(text: str, eos_token: str) -> bool:
+    return eos_token in text or text.endswith(eos_token.strip())
+
+
+@pytest.mark.skipif(not HAVE_TRANSFORMERS, reason="transformers not installed")
+@pytest.mark.parametrize("include_special_tokens", [True, False])
+@pytest.mark.parametrize("remove_special_tokens", [True, False])
+def test_hf_ids_to_text_eos_with_include_and_remove_special_tokens(
+    include_special_tokens, remove_special_tokens
+):
+    """ids_to_text EOS presence: parametrized on include_special_tokens and remove_special_tokens.
+    When remove_special_tokens=True, EOS is stripped; when False, EOS is kept (explicit overrides default).
+    """
+    try:
+        tok = HuggingFaceTokenizer(
+            LOCAL_HF_TOKENIZER_PATH, include_special_tokens=include_special_tokens
+        )
+    except Exception:
+        pytest.skip("Could not load local HuggingFace tokenizer (path not available)")
+    eos_id = tok.eos_id
+    ids = tok.text_to_ids("hello") + [eos_id]
+    text = tok.ids_to_text(ids, remove_special_tokens=remove_special_tokens)
+    eos_expected = not remove_special_tokens
+    if eos_expected:
+        assert _eos_in_text(text, tok.tokenizer.eos_token), (
+            f"Expected EOS in output for include_special_tokens={include_special_tokens}, "
+            f"remove_special_tokens={remove_special_tokens}. Got: {text!r}"
+        )
+    else:
+        assert tok.tokenizer.eos_token not in text, (
+            f"Expected EOS stripped for include_special_tokens={include_special_tokens}, "
+            f"remove_special_tokens={remove_special_tokens}. Got: {text!r}"
+        )
+
+
 def test_megatron_tokenizer():
     # Load tokenizer with additional special tokens
     special_tokens = {}
@@ -196,7 +245,7 @@ def test_tiktoken_tokenizer():
 
 
 def test_null_tokenizer():
-    metadata = {"library": "null"}
+    metadata = {"library": "null-text"}
     tokenizer = MegatronTokenizer.from_pretrained(metadata_path=metadata, vocab_size=131072)
 
     ids = tokenizer.tokenize("11 325 97")
@@ -255,3 +304,98 @@ class CustomTokenizerClass:
         model_type="gpt",
         overwrite=True,
     )
+
+
+def test_multimodal_tokenizer():
+    """Test MegatronMultimodalTokenizer."""
+    prompt_format = "qwen2p0"
+    special_tokens = ["<image>"]
+    image_tag_type = "nvlm"
+    tokenizer = MegatronTokenizer.from_pretrained(
+        tokenizer_path="/opt/data/tokenizers/multimodal",
+        metadata_path={"library": "multimodal"},
+        prompt_format=prompt_format,
+        special_tokens=special_tokens,
+        image_tag_type=image_tag_type,
+    )
+    # Simple encode - decode roundtrip.
+    assert (
+        tokenizer.detokenize(tokenizer.tokenize("abc")) == "abc"
+    ), "encode-decode roundtrip failed"
+
+    conversation = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello! Can you summarize this image for me?"},
+        {"role": "user", "content": "<image>"},
+        {"role": "assistant", "content": "Sure! The image shows a sunset over a mountain range."},
+        {"role": "user", "content": "Thanks! Can you also give a short poem about it?"},
+    ]
+
+    conv_tokens = tokenizer.tokenize_conversation(
+        conversation, return_target=False, add_generation_prompt=False
+    )
+    assert len(conv_tokens) > 0, "failed to tokenize conversation"
+
+    conv_tokens, target_tokens = tokenizer.tokenize_conversation(
+        conversation, return_target=True, add_generation_prompt=False
+    )
+    assert len(conv_tokens) > 0 and len(conv_tokens) == len(
+        target_tokens
+    ), "failed to tokenize conversation and return target tokens"
+
+    # Try converting tokens to ids.
+    assert tokenizer.convert_tokens_to_ids("a"), "failed to convert tokens to ids."
+
+    assert tokenizer._tokenizer._apply_image_tag("<image>hello") == "<Image><image></Image>hello"
+    assert tokenizer._tokenizer._apply_image_tag([{"role": "user", "content": "<image>hello"}]) == [
+        {"role": "user", "content": "<Image><image></Image>hello"}
+    ]
+
+
+def test_null_multimodal_tokenizer():
+    """Test MegatronNullMultimodalTokenizer."""
+    vocab_size = 10000
+    tokenizer = MegatronTokenizer.from_pretrained(
+        metadata_path={"library": "null-multimodal"}, vocab_size=vocab_size
+    )
+
+    assert tokenizer.vocab_size == (vocab_size + 1), f"expected vocab size is {vocab_size + 1}."
+
+    assert tokenizer.tokenize("1 22 333") == [1, 22, 333], "tokenization is failed."
+
+    assert tokenizer.detokenize([1, 22, 333]) == "1 22 333", "detokenization is failed."
+
+
+def test_sft_tokenizer():
+    """Test SFTTokenizer."""
+    prompt_format = "nemotron-nano-v2"
+    tokenizer = MegatronTokenizer.from_pretrained(
+        tokenizer_path="/opt/data/tokenizers/multimodal",
+        metadata_path={"library": "sft"},
+        prompt_format=prompt_format,
+    )
+
+    # Simple encode - decode roundtrip.
+    assert (
+        tokenizer.detokenize(tokenizer.tokenize("abc")) == "abc"
+    ), "encode-decode roundtrip failed"
+
+    conversation = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello! Can you summarize this image for me?"},
+        {"role": "user", "content": "<image>"},
+        {"role": "assistant", "content": "Sure! The image shows a sunset over a mountain range."},
+        {"role": "user", "content": "Thanks! Can you also give a short poem about it?"},
+    ]
+
+    conv_tokens = tokenizer.tokenize_conversation(
+        conversation, return_target=False, add_generation_prompt=False
+    )
+    assert len(conv_tokens) > 0, "failed to tokenize conversation"
+
+    conv_tokens, target_tokens = tokenizer.tokenize_conversation(
+        conversation, return_target=True, add_generation_prompt=False
+    )
+    assert len(conv_tokens) > 0 and len(conv_tokens) == len(
+        target_tokens
+    ), "failed to tokenize conversation and return target tokens"
diff --git a/tests/unit_tests/transformer/moe/test_a2a_token_dispatcher.py b/tests/unit_tests/transformer/moe/test_a2a_token_dispatcher.py
index aad1fcaca5f..e20e35b84e1 100644
--- a/tests/unit_tests/transformer/moe/test_a2a_token_dispatcher.py
+++ b/tests/unit_tests/transformer/moe/test_a2a_token_dispatcher.py
@@ -95,7 +95,6 @@ def test_capacity_padding_forward_backward(self, tp_size, ep_size, permute_fusio
         )
         container.dispatcher_drop_and_pad_test()
 
-    @pytest.mark.flaky_in_dev
     @pytest.mark.skipif(
         not is_te_min_version("1.7.0"), reason="TE 1.7.0 is required for MoE with FP8."
     )
diff --git a/tests/unit_tests/transformer/moe/test_grouped_mlp.py b/tests/unit_tests/transformer/moe/test_grouped_mlp.py
index f215f9008b2..67543168480 100644
--- a/tests/unit_tests/transformer/moe/test_grouped_mlp.py
+++ b/tests/unit_tests/transformer/moe/test_grouped_mlp.py
@@ -5,8 +5,8 @@
 import torch.nn.functional as F
 
 from megatron.core.models.gpt.gpt_layer_specs import (
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_local_submodules,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.transformer.module import Float16Module
 from megatron.core.transformer.moe import grouped_gemm_util as gg
@@ -69,8 +69,8 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True):
         ## Vanilla sequential GEMM
         # Set random seed for reproducability
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        transformer_layer_spec = get_gpt_layer_local_spec(self.num_experts, moe_grouped_gemm=False)
-        self.sequential_mlp = MoELayer(tf_config, transformer_layer_spec.submodules.mlp.submodules)
+        submodules = get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False)
+        self.sequential_mlp = MoELayer(tf_config, submodules.mlp.submodules)
 
         self.args = parse_args(ignore_unknown_args=True)
         self.args.bf16 = True
@@ -83,10 +83,12 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True):
         ## Grouped GEMM
         _set_random_seed(seed_=123, data_parallel_random_init=False)
         tf_config.moe_grouped_gemm = True
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
-            self.num_experts, moe_grouped_gemm=True
+        self.grouped_mlp = MoELayer(
+            tf_config,
+            get_gpt_layer_with_transformer_engine_submodules(
+                self.num_experts, moe_grouped_gemm=True
+            ).mlp.submodules,
         )
-        self.grouped_mlp = MoELayer(tf_config, transformer_layer_spec.submodules.mlp.submodules)
         self.grouped_mlp = Float16Module(self.grouped_mlp.config, self.grouped_mlp).module
         print("done intializing for grouped gemm")
 
@@ -259,8 +261,8 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True):
         ## Vanilla sequential GEMM
         # Set random seed for reproducability
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        transformer_layer_spec = get_gpt_layer_local_spec(self.num_experts, moe_grouped_gemm=False)
-        self.sequential_mlp = MoELayer(tf_config, transformer_layer_spec.submodules.mlp.submodules)
+        submodules = get_gpt_layer_local_submodules(self.num_experts, moe_grouped_gemm=False)
+        self.sequential_mlp = MoELayer(tf_config, submodules.mlp.submodules)
 
         self.args = parse_args(ignore_unknown_args=True)
         self.args.bf16 = True
@@ -271,11 +273,13 @@ def setup_method(self, method, use_cpu_initialization=False, swiglu=True):
 
         ## Grouped GEMM
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
-            self.num_experts, moe_grouped_gemm=True
-        )
         tf_config.moe_grouped_gemm = True
-        self.grouped_mlp = MoELayer(tf_config, transformer_layer_spec.submodules.mlp.submodules)
+        self.grouped_mlp = MoELayer(
+            tf_config,
+            get_gpt_layer_with_transformer_engine_submodules(
+                self.num_experts, moe_grouped_gemm=True
+            ).mlp.submodules,
+        )
         assert isinstance(self.grouped_mlp.experts, TEGroupedMLP)
         self.grouped_mlp = Float16Module(self.grouped_mlp.config, self.grouped_mlp).module
 
diff --git a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py
new file mode 100644
index 00000000000..f62de67860a
--- /dev/null
+++ b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import pytest
+import torch
+
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_decoder_block_spec,
+    get_gpt_layer_local_spec,
+    get_gpt_layer_with_transformer_engine_spec,
+)
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.moe.moe_layer import MoELayer
+from megatron.core.transformer.moe.router import Router
+from megatron.core.transformer.transformer_block import TransformerBlock
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import is_te_min_version
+from megatron.training.initialize import _set_random_seed
+from tests.unit_tests.test_utilities import Utils
+
+
+class TestLatentMoELayer:
+    def setup_method(self, method):
+        pass
+
+    @pytest.mark.skipif(
+        not is_te_min_version("1.7.0.dev0"),
+        reason="Expert with TE Linear is only supported in TE 1.7.0 and later.",
+    )
+    @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"])
+    @pytest.mark.parametrize("num_moe_experts", [4])
+    @pytest.mark.parametrize("use_te,grouped_gemm", [(True, True), (True, False), (False, False)])
+    @pytest.mark.parametrize("moe_latent_size", [8, 16])
+    def test_latent_moe_layer(
+        self, num_moe_experts, moe_token_dispatcher_type, use_te, grouped_gemm, moe_latent_size
+    ):
+        Utils.initialize_model_parallel(1, 1)
+        _set_random_seed(seed_=123, data_parallel_random_init=False)
+        self.transformer_config = TransformerConfig(
+            num_layers=1,
+            hidden_size=32,
+            num_attention_heads=4,
+            num_moe_experts=num_moe_experts,
+            use_cpu_initialization=True,
+            moe_token_dispatcher_type=moe_token_dispatcher_type,
+            moe_router_topk=2,
+            moe_aux_loss_coeff=0.01,
+            moe_grouped_gemm=grouped_gemm,
+            moe_ffn_hidden_size=128,
+            moe_shared_expert_intermediate_size=128,
+            activation_func=torch.nn.functional.silu,
+            gated_linear_unit=True,
+            add_bias_linear=False,
+            moe_latent_size=moe_latent_size,
+        )
+        if use_te:
+            transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+                num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
+            )
+        else:
+            transformer_layer_spec = get_gpt_layer_local_spec(
+                num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
+            )
+        moe_layer = MoELayer(
+            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
+        )
+        moe_layer.cuda()
+        config = moe_layer.config
+
+        assert (
+            moe_layer.shared_experts.linear_fc1.weight.shape[1] == config.hidden_size
+        ), "Shared expert computation has to happen in hidden dimension."
+        assert (
+            moe_layer.shared_experts.linear_fc2.weight.shape[0] == config.hidden_size
+        ), "Shared expert computation has to happen in hidden dimension."
+        if grouped_gemm:
+            for i in range(num_moe_experts):
+                fc1_weight = getattr(moe_layer.experts.linear_fc1, f"weight{i}")
+                fc2_weight = getattr(moe_layer.experts.linear_fc2, f"weight{i}")
+                assert (
+                    fc1_weight.shape[1] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc1_weight.shape=}"
+                assert (
+                    fc2_weight.shape[0] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc2_weight.shape=}"
+        else:
+            for i in range(num_moe_experts):
+                expert = moe_layer.experts.local_experts[i]
+                assert (
+                    expert.linear_fc1.weight.shape[1] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc1_weight.shape=}"
+                assert (
+                    expert.linear_fc2.weight.shape[0] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc2_weight.shape=}"
+        assert (
+            moe_layer.router.weight.shape[1] == config.hidden_size
+        ), "MoE routing has to happen in hidden dimension."
+
+        # [sequence length, batch size, hidden size]
+        hidden_states = torch.ones((32, 2, config.hidden_size))
+        hidden_states = hidden_states.cuda()
+        output, _ = moe_layer(hidden_states)
+        assert output.shape[2] == config.hidden_size
+
+        Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/transformer/moe/test_moe_layer.py b/tests/unit_tests/transformer/moe/test_moe_layer.py
index 2a2c995257e..0004b7fef98 100644
--- a/tests/unit_tests/transformer/moe/test_moe_layer.py
+++ b/tests/unit_tests/transformer/moe/test_moe_layer.py
@@ -5,12 +5,11 @@
 
 from megatron.core.models.gpt.gpt_layer_specs import (
     get_gpt_decoder_block_spec,
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_local_submodules,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.moe.moe_layer import MoELayer
-from megatron.core.transformer.moe.router import Router
 from megatron.core.transformer.transformer_block import TransformerBlock
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.utils import is_te_min_version
@@ -45,12 +44,10 @@ def test_te_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grouped_
             moe_ffn_hidden_size=128,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
-        moe_layer = MoELayer(
-            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules)
         Utils.destroy_model_parallel()
 
     @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"])
@@ -73,12 +70,10 @@ def test_legacy_moe_layer(self, num_moe_experts, moe_token_dispatcher_type, grou
             moe_grouped_gemm=grouped_gemm,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        transformer_layer_submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
-        moe_layer = MoELayer(
-            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        moe_layer = MoELayer(self.transformer_config, transformer_layer_submodules.mlp.submodules)
         Utils.destroy_model_parallel()
 
     @pytest.mark.skip(
@@ -110,7 +105,7 @@ def test_moe_with_late_initialize(
             bf16=True,
             params_dtype=torch.bfloat16,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
 
@@ -118,9 +113,7 @@ def test_moe_with_late_initialize(
         Utils.fake_initialize_model_parallel(
             tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size
         )
-        moe_layer = MoELayer(
-            transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        ).cuda()
+        moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda()
 
         Utils.initialize_model_parallel(
             tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size
@@ -236,13 +229,11 @@ def test_moe_layer_fp16_forward_backward(
             params_dtype=torch.float16,
         )
 
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
 
-        moe_layer = MoELayer(
-            transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        ).cuda()
+        moe_layer = MoELayer(transformer_config, submodules.mlp.submodules).cuda()
 
         hidden_states = torch.randn(
             sequence_length,
@@ -276,3 +267,121 @@ def test_moe_layer_fp16_forward_backward(
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
+
+
+class TestMoELayerRecompute:
+    """Test MoE layer with recompute enabled (activation checkpointing).
+
+    Tests both code paths:
+    - fp8=False: uses tensor_parallel.checkpoint
+    - fp8=True: uses te_checkpoint (requires TE >= 1.7.0)
+    """
+
+    def setup_method(self, method):
+        pass
+
+    @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"])
+    @pytest.mark.parametrize("num_moe_experts", [2, 4])
+    @pytest.mark.parametrize("with_padding_mask", [True, False])
+    @pytest.mark.parametrize("tp_size,ep_size", [(1, 1), (4, 2)])
+    @pytest.mark.parametrize("fp8", [False, True])
+    def test_moe_layer_recompute_forward_backward(
+        self, num_moe_experts, moe_token_dispatcher_type, with_padding_mask, tp_size, ep_size, fp8
+    ):
+        """Test MoE layer forward and backward pass with recompute enabled.
+
+        When fp8=False, uses tensor_parallel.checkpoint.
+        When fp8=True, uses te_checkpoint (requires TE >= 1.7.0).
+        """
+        # Skip fp8 tests if TE version is not sufficient
+        if fp8 and not is_te_min_version("1.7.0.dev0"):
+            pytest.skip("FP8 MoE recompute requires TE 1.7.0 and later.")
+
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size
+        )
+        _set_random_seed(seed_=123, data_parallel_random_init=False)
+
+        hidden_size = 64
+        sequence_length = 32
+        micro_batch_size = 2
+
+        transformer_config = TransformerConfig(
+            num_layers=1,
+            hidden_size=hidden_size,
+            num_attention_heads=4,
+            num_moe_experts=num_moe_experts,
+            use_cpu_initialization=False,
+            moe_token_dispatcher_type=moe_token_dispatcher_type,
+            moe_router_load_balancing_type="aux_loss",
+            moe_router_topk=2,
+            moe_aux_loss_coeff=0.01,
+            moe_grouped_gemm=False,
+            moe_ffn_hidden_size=256,
+            add_bias_linear=False,
+            # Enable recompute for MoE layer
+            recompute_granularity="selective",
+            recompute_modules=["moe"],
+            tensor_model_parallel_size=tp_size,
+            expert_model_parallel_size=ep_size,
+            sequence_parallel=tp_size > 1,
+            fp8=fp8,
+            bf16=True,
+            params_dtype=torch.bfloat16,
+        )
+
+        # Use TE spec for fp8, local spec otherwise
+        if fp8:
+            transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules(
+                num_experts=num_moe_experts, moe_grouped_gemm=False
+            )
+        else:
+            transformer_layer_submodules = get_gpt_layer_local_submodules(
+                num_experts=num_moe_experts, moe_grouped_gemm=False
+            )
+
+        moe_layer = MoELayer(transformer_config, transformer_layer_submodules.mlp.submodules).cuda()
+
+        hidden_states = torch.randn(
+            sequence_length,
+            micro_batch_size,
+            hidden_size,
+            device=torch.cuda.current_device(),
+            dtype=torch.bfloat16,
+            requires_grad=True,
+        )
+
+        # Create padding mask if needed: shape [batch_size, sequence_length]
+        padding_mask = None
+        if with_padding_mask:
+            padding_mask = torch.ones(
+                micro_batch_size,
+                sequence_length,
+                device=torch.cuda.current_device(),
+                dtype=torch.bool,
+            )
+            # Mark last 4 tokens as padding for each batch
+            padding_mask[:, -4:] = False
+
+        output, _ = moe_layer(hidden_states, padding_mask=padding_mask)
+
+        assert output.dtype == torch.bfloat16, f"Expected bf16 output, got {output.dtype}"
+        assert output.shape == hidden_states.shape, f"Output shape mismatch"
+
+        # Backward pass - this is where recompute/checkpoint is actually used
+        loss = output.sum()
+        loss.backward()
+
+        assert hidden_states.grad is not None, "Input gradients should exist"
+        assert (
+            hidden_states.grad.dtype == torch.bfloat16
+        ), f"Expected bf16 gradients, got {hidden_states.grad.dtype}"
+
+        for name, param in moe_layer.named_parameters():
+            if param.requires_grad:
+                assert param.grad is not None, f"Gradient for {name} should exist"
+
+        Utils.destroy_model_parallel()
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/transformer/moe/test_moe_layer_discrepancy.py b/tests/unit_tests/transformer/moe/test_moe_layer_discrepancy.py
index 4386a844dd2..d60fbe336b8 100644
--- a/tests/unit_tests/transformer/moe/test_moe_layer_discrepancy.py
+++ b/tests/unit_tests/transformer/moe/test_moe_layer_discrepancy.py
@@ -7,10 +7,8 @@
 
 from megatron.core import parallel_state
 from megatron.core.models.gpt.gpt_layer_specs import (
-    get_gpt_layer_local_spec,
-    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
-from megatron.core.transformer.moe.moe_layer import MoELayer
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.transformer_layer import TransformerLayer
 from megatron.training.initialize import _set_random_seed
@@ -49,7 +47,7 @@ def test_moe_layer_dispatcher_discrepancy(
             expert_model_parallel_size=ep_size,
             sequence_parallel=True if (tp_size > 1) else False,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
         # Init input and layer
@@ -58,21 +56,13 @@ def test_moe_layer_dispatcher_discrepancy(
 
         # Init allgather moe layer
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        layer = (
-            TransformerLayer(self.transformer_config, transformer_layer_spec.submodules)
-            .cuda()
-            .float()
-        )
+        layer = TransformerLayer(self.transformer_config, submodules).cuda().float()
         ag_moe_layer = layer.mlp
         ag_moe_layer.eval()
         # Init a2a moe layer
         self.transformer_config.moe_token_dispatcher_type = "alltoall"
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        layer = (
-            TransformerLayer(self.transformer_config, transformer_layer_spec.submodules)
-            .cuda()
-            .float()
-        )
+        layer = TransformerLayer(self.transformer_config, submodules).cuda().float()
         a2a_moe_layer = layer.mlp
         a2a_moe_layer.eval()
 
@@ -134,7 +124,7 @@ def test_moe_layer_ag_dispatcher_discrepancy(
             sequence_parallel=True if (tp_size > 1 and ep_size > 1) else False,
             bf16=True,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
         # Init input and layer
@@ -143,11 +133,7 @@ def test_moe_layer_ag_dispatcher_discrepancy(
 
         # Init allgather moe layer
         _set_random_seed(seed_=123, data_parallel_random_init=False)
-        layer = (
-            TransformerLayer(self.transformer_config, transformer_layer_spec.submodules)
-            .cuda()
-            .bfloat16()
-        )
+        layer = TransformerLayer(self.transformer_config, submodules).cuda().bfloat16()
         ag_moe_layer = layer.mlp
         ag_moe_layer.eval()
 
@@ -205,7 +191,7 @@ def test_moe_layer_a2a_dispatcher_discrepancy(
             sequence_parallel=True if (tp_size > 1 and ep_size > 1) else False,
             bf16=True,
         )
-        transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+        submodules = get_gpt_layer_with_transformer_engine_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
         )
         # Init input and layer
@@ -213,11 +199,7 @@ def test_moe_layer_a2a_dispatcher_discrepancy(
         input = torch.randn(1, 4096, 4096).cuda().bfloat16()
 
         # Init a2a moe layer
-        layer = (
-            TransformerLayer(self.transformer_config, transformer_layer_spec.submodules)
-            .cuda()
-            .bfloat16()
-        )
+        layer = TransformerLayer(self.transformer_config, submodules).cuda().bfloat16()
         a2a_moe_layer = layer.mlp
         a2a_moe_layer.eval()
 
diff --git a/tests/unit_tests/transformer/moe/test_routers.py b/tests/unit_tests/transformer/moe/test_routers.py
index 4d6b5ee2c3e..8f3dbbe96e0 100644
--- a/tests/unit_tests/transformer/moe/test_routers.py
+++ b/tests/unit_tests/transformer/moe/test_routers.py
@@ -1,11 +1,12 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
+
 from typing import cast
 
 import pytest
 import torch
 
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.transformer.moe.moe_layer import MoELayer
 from megatron.core.transformer.moe.moe_utils import get_updated_expert_bias, router_gating_linear
 from megatron.core.transformer.moe.router import Router
@@ -43,12 +44,10 @@ def setup_method(self, method):
             params_dtype=torch.bfloat16,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.sequential_mlp = MoELayer(
-            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        self.sequential_mlp = MoELayer(self.transformer_config, submodules.mlp.submodules)
         self.router = cast(Router, self.sequential_mlp.router)
 
     def teardown_method(self, method):
@@ -314,12 +313,10 @@ def setup_method(self, method):
         )
 
         # init MoE layer
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.moe_layer = MoELayer(
-            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        ).cuda()
+        self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules).cuda()
         self.router = cast(Router, self.moe_layer.router)
 
     def teardown_method(self, method):
@@ -421,12 +418,10 @@ def setup_method(self, method):
             params_dtype=torch.bfloat16,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.moe_layer = MoELayer(
-            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        self.moe_layer = MoELayer(self.transformer_config, submodules.mlp.submodules)
         self.router = cast(Router, self.moe_layer.router)
         assert self.router.expert_bias is not None
         assert self.router.local_tokens_per_expert is not None
@@ -469,15 +464,11 @@ def test_router_forward_aux_free(self):
     def test_router_forward_fusion_equivalence(self, score_function):
         with torch.no_grad():
             # Build two fresh routers to avoid bias update interference
-            transformer_layer_spec = get_gpt_layer_local_spec(
+            submodules = get_gpt_layer_local_submodules(
                 num_experts=self.transformer_config.num_moe_experts, moe_grouped_gemm=False
             )
-            moe_layer_ref = MoELayer(
-                self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-            )
-            moe_layer_fused = MoELayer(
-                self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
-            )
+            moe_layer_ref = MoELayer(self.transformer_config, submodules.mlp.submodules)
+            moe_layer_fused = MoELayer(self.transformer_config, submodules.mlp.submodules)
             router_ref = moe_layer_ref.router.cuda()
             router_fused = moe_layer_fused.router.cuda()
 
diff --git a/tests/unit_tests/transformer/moe/test_sequential_mlp.py b/tests/unit_tests/transformer/moe/test_sequential_mlp.py
index a80e2a2ff1b..e618f6a8318 100644
--- a/tests/unit_tests/transformer/moe/test_sequential_mlp.py
+++ b/tests/unit_tests/transformer/moe/test_sequential_mlp.py
@@ -5,7 +5,7 @@
 import torch
 
 from megatron.core.extensions.transformer_engine import TEColumnParallelLinear, TERowParallelLinear
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.mlp import MLPSubmodules
@@ -37,12 +37,10 @@ def setup_method(self, method):
             moe_router_topk=1,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.sequential_mlp = MoELayer(
-            transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        self.sequential_mlp = MoELayer(transformer_config, submodules.mlp.submodules)
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/transformer/moe/test_shared_experts.py b/tests/unit_tests/transformer/moe/test_shared_experts.py
index 6df4d2fd369..d99dc1a0b05 100644
--- a/tests/unit_tests/transformer/moe/test_shared_experts.py
+++ b/tests/unit_tests/transformer/moe/test_shared_experts.py
@@ -3,7 +3,7 @@
 import pytest
 import torch
 
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.moe.moe_layer import MoELayer
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -41,12 +41,10 @@ def test_gpu_forward(self, shared_expert_gate):
             add_bias_linear=False,
             moe_shared_expert_gate=shared_expert_gate,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.moe_layer = MoELayer(
-            transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        self.moe_layer = MoELayer(transformer_config, submodules.mlp.submodules)
 
         assert isinstance(self.moe_layer, MoELayer)
 
@@ -103,12 +101,10 @@ def test_gpu_forward(self):
             moe_router_topk=1,
             add_bias_linear=False,
         )
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=num_moe_experts, moe_grouped_gemm=False
         )
-        self.moe_layer = MoELayer(
-            transformer_config, transformer_layer_spec.submodules.mlp.submodules
-        )
+        self.moe_layer = MoELayer(transformer_config, submodules.mlp.submodules)
 
         assert isinstance(self.moe_layer, MoELayer)
 
diff --git a/tests/unit_tests/transformer/moe/test_token_dispatcher.py b/tests/unit_tests/transformer/moe/test_token_dispatcher.py
index a7941837205..05ebdecfd04 100644
--- a/tests/unit_tests/transformer/moe/test_token_dispatcher.py
+++ b/tests/unit_tests/transformer/moe/test_token_dispatcher.py
@@ -7,7 +7,7 @@
 import torch
 
 from megatron.core import config, parallel_state
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.transformer.moe.moe_layer import MoELayer
 from megatron.core.transformer.moe.moe_utils import get_capacity
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -99,15 +99,11 @@ def __init__(
         self.moe_layer = self.new_moe_layer()
 
     def new_moe_layer(self, **kargs):
-        transformer_layer_spec = get_gpt_layer_local_spec(
+        submodules = get_gpt_layer_local_submodules(
             num_experts=self.config.num_moe_experts, moe_grouped_gemm=self.config.moe_grouped_gemm
         )
         new_config = dataclasses.replace(self.config, **kargs)
-        moe_layer = (
-            MoELayer(new_config, transformer_layer_spec.submodules.mlp.submodules)
-            .cuda()
-            .to(dtype=self.test_dtype)
-        )
+        moe_layer = MoELayer(new_config, submodules.mlp.submodules).cuda().to(dtype=self.test_dtype)
         moe_layer.set_layer_number(0)
         return moe_layer
 
@@ -364,7 +360,6 @@ def setup_method(self, method):
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
 
-    @pytest.mark.flaky_in_dev
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
     @pytest.mark.internal
     @pytest.mark.parametrize("tp_size,ep_size", [(8, 1), (1, 8), (2, 4), (1, 1)])
@@ -383,7 +378,6 @@ def test_forward_backward(self, tp_size, ep_size, permute_fusion):
 
         container.dispatcher_dropless_test()
 
-    @pytest.mark.flaky_in_dev
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
     @pytest.mark.internal
     @pytest.mark.parametrize("permute_fusion", permute_fusion_params)
diff --git a/tests/unit_tests/transformer/moe/test_upcycling.py b/tests/unit_tests/transformer/moe/test_upcycling.py
index 9cb7aa327bd..b4f544b8930 100644
--- a/tests/unit_tests/transformer/moe/test_upcycling.py
+++ b/tests/unit_tests/transformer/moe/test_upcycling.py
@@ -115,7 +115,6 @@ def create_test_args(tp, grouped_gemm, swiglu, squared_relu, use_te):
     args.lr = 3e-5
     args.attention_dropout = 0.0
     args.hidden_dropout = 0.0
-    args.async_tensor_model_parallel_allreduce = False
     args.no_save_optim = True
     args.no_load_optim = True
     args.no_load_rng = True
diff --git a/tests/unit_tests/transformer/test_attention.py b/tests/unit_tests/transformer/test_attention.py
index 0fbc6b4da23..cb69a0b7a9e 100644
--- a/tests/unit_tests/transformer/test_attention.py
+++ b/tests/unit_tests/transformer/test_attention.py
@@ -17,6 +17,7 @@
 from megatron.core.models.gpt.gpt_layer_specs import (
     get_gpt_layer_local_spec,
     get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_with_transformer_engine_submodules,
 )
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.process_groups_config import ProcessGroupCollection
@@ -77,12 +78,11 @@ def setup_method(self, output_gate, transformer_impl, fallback_to_eager_attn):
             fallback_to_eager_attn=fallback_to_eager_attn,
         )
         if transformer_impl == "transformer_engine":
-            layer_spec = get_gpt_layer_with_transformer_engine_spec(
-                fallback_to_eager_attn=fallback_to_eager_attn
+            attn_layer_spec = (
+                get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules
             )
         else:
-            layer_spec = get_gpt_layer_local_spec()
-        attn_layer_spec = layer_spec.submodules.self_attention.submodules
+            attn_layer_spec = get_gpt_layer_local_spec().submodules.self_attention.submodules
         self.parallel_attention = SelfAttention(
             self.transformer_config, attn_layer_spec, layer_number=1
         )
@@ -187,7 +187,7 @@ def test_checkpointed_gpu_forward(self):
         transformer_config.recompute_granularity = 'selective'
         checkpointed_parallel_attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
         config = checkpointed_parallel_attention.config
@@ -237,7 +237,7 @@ def test_clip_qk_disabled_raises_error(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
 
@@ -257,7 +257,7 @@ def test_clip_qk_none_logits_raises_error(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
 
@@ -277,7 +277,7 @@ def test_clip_qk_below_threshold_no_update(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
         attention.cuda()
@@ -311,7 +311,7 @@ def test_clip_qk_above_threshold_updates_weights(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
         attention.cuda()
@@ -346,7 +346,7 @@ def test_clip_qk_gqa_configuration(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
         attention.cuda()
@@ -380,7 +380,7 @@ def test_clip_qk_mixed_logits(self):
         )
         attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
         )
         attention.cuda()
@@ -618,12 +618,14 @@ def run_self_attention(self, pg_collection):
             transformer_impl=self.transformer_impl,
         )
         if self.transformer_impl == "transformer_engine":
-            get_gpt_layer_spec_fn = get_gpt_layer_with_transformer_engine_spec
+            attn_layer_spec = (
+                get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules
+            )
         else:
-            get_gpt_layer_spec_fn = get_gpt_layer_local_spec
+            attn_layer_spec = get_gpt_layer_local_spec().submodules.self_attention.submodules
         self.self_attention = SelfAttention(
             self.transformer_config,
-            get_gpt_layer_spec_fn().submodules.self_attention.submodules,
+            attn_layer_spec,
             layer_number=1,
             attn_mask_type=AttnMaskType.causal,
             pg_collection=pg_collection,
@@ -925,6 +927,39 @@ def test_parallel_attention_correctness(
     )
 
 
+@pytest.mark.parametrize("sp", [True, False])
+@pytest.mark.parametrize("output_gate", [False, True])
+def test_parallel_attention_correctness_num_query_groups_less_than_tp_size(
+    tmp_path_dist_ckpt, sp, output_gate
+):
+    transformer_config = TransformerConfig(
+        num_layers=1,
+        hidden_size=128,
+        num_attention_heads=8,
+        num_query_groups=2,
+        normalization="RMSNorm",
+        bf16=True,
+        attention_output_gate=output_gate,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+    )
+
+    transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec()
+    atol, rtol = 1e-2, 1e-2
+
+    _test_parallel_attention_correctness(
+        transformer_config,
+        transformer_layer_spec,
+        tmp_path_dist_ckpt,
+        atol=atol,
+        rtol=rtol,
+        tp=4,
+        sp=sp,
+        seed=123,
+        sequence_length=256,
+    )
+
+
 def _torch_native_attention(query, key, value, attention_mask, sinks, scaling: float):
     """Torch native attention implementation
     This was not in the original implementation and slightly affect results;
diff --git a/tests/unit_tests/transformer/test_attention_no_rope.py b/tests/unit_tests/transformer/test_attention_no_rope.py
index 30e11609e57..2c79d7a3011 100644
--- a/tests/unit_tests/transformer/test_attention_no_rope.py
+++ b/tests/unit_tests/transformer/test_attention_no_rope.py
@@ -3,7 +3,9 @@
 import pytest
 import torch
 
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.attention import SelfAttention
 from megatron.core.transformer.enums import AttnMaskType
@@ -30,7 +32,7 @@ def setup_method(self, method):
         )
         self.parallel_attention = SelfAttention(
             self.transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
             attn_mask_type=AttnMaskType.causal,
         )
@@ -176,7 +178,7 @@ def test_checkpointed_gpu_forward(self):
 
         checkpointed_parallel_attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
             attn_mask_type=AttnMaskType.causal,
         )
diff --git a/tests/unit_tests/transformer/test_attention_packed_seq.py b/tests/unit_tests/transformer/test_attention_packed_seq.py
index e6e2c847395..9c8f5bd2ce6 100644
--- a/tests/unit_tests/transformer/test_attention_packed_seq.py
+++ b/tests/unit_tests/transformer/test_attention_packed_seq.py
@@ -3,7 +3,9 @@
 import pytest
 import torch
 
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.attention import SelfAttention
@@ -62,7 +64,7 @@ def setup_method(self, method):
         )
         self.parallel_attention = SelfAttention(
             self.transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
             attn_mask_type=AttnMaskType.causal,
         )
@@ -138,7 +140,7 @@ def test_checkpointed_gpu_forward(self):
         transformer_config.recompute_granularity = 'selective'
         checkpointed_parallel_attention = SelfAttention(
             transformer_config,
-            get_gpt_layer_with_transformer_engine_spec().submodules.self_attention.submodules,
+            get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules,
             layer_number=1,
             attn_mask_type=AttnMaskType.causal,
         )
diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py
index d997e8eac39..956b4897bc8 100644
--- a/tests/unit_tests/transformer/test_cuda_graphs.py
+++ b/tests/unit_tests/transformer/test_cuda_graphs.py
@@ -2,32 +2,15 @@
 
 import gc
 import os
-import random
 import sys
-import time
-import types
 
 import pytest
 import torch
 from transformer_engine.pytorch.fp8 import check_fp8_support
 
-from megatron.core import parallel_state
 from megatron.core.enums import ModelType
-from megatron.core.inference.contexts import DynamicInferenceContext
-from megatron.core.inference.engines import DynamicInferenceEngine
-from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
-    GPTInferenceWrapper,
-)
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
-from megatron.core.inference.sampling_params import SamplingParams
-from megatron.core.inference.text_generation_controllers.text_generation_controller import (
-    TextGenerationController,
-)
 from megatron.core.models.gpt.gpt_layer_specs import (
     get_gpt_decoder_block_spec,
-    get_gpt_layer_local_spec,
     get_gpt_layer_with_transformer_engine_spec,
     get_gpt_mtp_block_spec,
 )
@@ -54,6 +37,7 @@
 from megatron.core.transformer.moe.fused_a2a import reset_hybrid_ep_buffer
 from megatron.core.transformer.transformer_block import TransformerBlock
 from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
 from megatron.core.utils import is_fa_min_version, is_te_min_version
 from megatron.training.arguments import core_transformer_config_from_args, parse_args, validate_args
 from megatron.training.global_vars import (
@@ -344,6 +328,7 @@ def setup_method(self, method):
         # Get layer specs
         language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
         vision_layer_spec = get_vit_layer_with_transformer_engine_spec()
+        assert isinstance(language_layer_spec.submodules, TransformerLayerSubmodules)
         vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)
 
         # Set vision model type
@@ -544,204 +529,6 @@ def test_gpu_cudagraph(self):
             del parallel_mamba_block.layers[_].cudagraph_manager.cudagraph_runners[0].fwd_graph
 
 
-class TestCaptureFreezeGC:
-
-    def capture_cuda_graphs(self, cuda_graph_capture_freeze_gc: bool) -> None:
-        """Capture multiple cuda graphs by initializing the `DynamicInferenceEngine`.
-
-        The `DynamicInferenceEngine` is used here because it is currently (as of
-        August 2025) one of the heaviest users of multiple cuda graphs, and so
-        its setup tests a realistic use-case of multi-batch size cuda graphs.
-
-        Args:
-            cuda_graph_capture_freeze_gc (bool): Flag that determines whether to
-                freeze garbage collection.
-        """
-
-        # Set freeze-gc environment variable.
-        os.environ["CUDA_GRAPH_CAPTURE_FREEZE_GC"] = str(int(cuda_graph_capture_freeze_gc))
-
-        # Configuration.
-        random_seed = 123
-        vocab_size = 100
-        num_tokens_to_prompt = 128
-        num_tokens_to_generate = 32
-        max_sequence_length = num_tokens_to_prompt + num_tokens_to_generate
-        num_cuda_graphs = 4
-
-        # Rounder values.
-        rounder = 4
-        DynamicInferenceContext.ROUNDER = rounder  # For backwards compatibility
-        DynamicInferenceContext.TOKEN_ROUNDER = rounder
-        DynamicInferenceContext.REQUEST_ROUNDER = rounder
-
-        # Random state.
-        random.seed(random_seed)
-        torch.manual_seed(random_seed)
-        model_parallel_cuda_manual_seed(
-            seed=random_seed,
-            inference_rng_tracker=True,
-            use_cudagraphable_rng=False,
-            force_reset_rng=True,
-        )
-
-        # Transformer config.
-        transformer_config = TransformerConfig(
-            params_dtype=torch.bfloat16,
-            num_layers=4,
-            hidden_size=32,
-            num_attention_heads=4,
-            use_cpu_initialization=True,
-            cuda_graph_impl="local",
-            inference_rng_tracker=True,
-            tensor_model_parallel_size=1,  # needed?
-        )
-
-        # Sampling params.
-        sampling_params = SamplingParams(num_tokens_to_generate=num_tokens_to_generate)
-
-        # GPT model.
-        model = GPTModel(
-            config=transformer_config,
-            transformer_layer_spec=get_gpt_layer_local_spec(),
-            vocab_size=vocab_size,
-            max_sequence_length=max_sequence_length,
-            parallel_output=True,
-        ).cuda()
-
-        for param in model.parameters():
-            param.data = param.data.to(transformer_config.params_dtype)
-
-        model.eval()
-
-        # Inference config.
-        inference_config = InferenceWrapperConfig(
-            hidden_size=transformer_config.hidden_size,
-            inference_batch_times_seqlen_threshold=400,
-            fp32_residual_connection=False,
-            params_dtype=transformer_config.params_dtype,
-            padded_vocab_size=vocab_size,
-        )
-
-        # Inference context.
-        context = DynamicInferenceContext(
-            params_dtype=transformer_config.params_dtype,
-            num_layers=transformer_config.num_layers,
-            kv_channels=transformer_config.kv_channels,
-            num_attention_heads=transformer_config.num_query_groups,
-            max_sequence_length=max_sequence_length,
-            num_cuda_graphs=num_cuda_graphs,
-            buffer_size_gb=20,
-            buffer_guaranteed_fraction=0.05,
-            block_size_tokens=256,
-            buffer_overflow_factor=1.1,
-            max_requests_override=512,
-            max_tokens_override=8196,
-            tensor_model_parallel_size=transformer_config.tensor_model_parallel_size,
-        )
-
-        # Inference model wrapper.
-        inference_wrapped_model = GPTInferenceWrapper(model, inference_config, context)
-
-        # Note: the following is taken from AbstractModelInferenceWrapper.prep_model_for_inference().
-        inference_wrapped_model.model_is_pipeline_parallel = not (
-            parallel_state.is_pipeline_first_stage() and parallel_state.is_pipeline_last_stage()
-        )
-
-        # Text generation controller.
-        text_generation_controller = TextGenerationController(
-            inference_wrapped_model=inference_wrapped_model,
-            tokenizer=types.SimpleNamespace(vocab_size=vocab_size),
-        )
-
-        # Inference engine.
-        engine = DynamicInferenceEngine(
-            text_generation_controller,
-            context,
-            termination_id=vocab_size - 1,
-            random_seed=random_seed,
-        )
-
-        return engine.capture_stats
-
-    @pytest.mark.flaky_in_dev  # Issue #2855
-    @pytest.mark.flaky
-    @pytest.mark.experimental
-    @pytest.mark.skipif(
-        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
-    )
-    def test_capture_freeze_gc(self):
-        """Test cuda graph capture while freezing the GC."""
-
-        Utils.initialize_model_parallel(
-            tensor_model_parallel_size=1, pipeline_model_parallel_size=1
-        )
-
-        # Run tests with GC freeze off/on.
-        result_map = {}
-        for freeze_gc in (False, True):
-
-            # Reset global cuda graph state.
-            _CudagraphGlobalRecord.cudagraph_created = False
-            _CudagraphGlobalRecord.cudagraph_record = []
-            CudaGraphManager.global_mempool = None
-
-            # Capture multiple cuda graphs by initializing DynamicInferenceEngine.
-            mem_stats_start = torch.cuda.memory_stats()
-            time_start = time.time()
-            internal_stats = self.capture_cuda_graphs(freeze_gc)
-            time_end = time.time()
-            mem_stats_end = torch.cuda.memory_stats()
-
-            # Track local (external) stats, in addition to internal stats.
-            external_stats = {
-                "time": time_end - time_start,
-                "allocated_bytes": (
-                    mem_stats_end["allocated_bytes.all.current"]
-                    - mem_stats_start["allocated_bytes.all.current"]
-                ),
-                "reserved_bytes": (
-                    mem_stats_end["reserved_bytes.all.current"]
-                    - mem_stats_start["reserved_bytes.all.current"]
-                ),
-            }
-
-            # Record results.
-            result_map[freeze_gc] = {"internal": internal_stats, "external": external_stats}
-
-        # Extract results.
-        freeze_off_results = result_map[False]
-        freeze_on_results = result_map[True]
-        print(
-            "test capture | freeze off: internal %.3f, external %.3f."
-            % (freeze_off_results["internal"]["time"], freeze_off_results["external"]["time"])
-        )
-        print(
-            "test capture | freeze on:  internal %.3f, external %.3f."
-            % (freeze_on_results["internal"]["time"], freeze_on_results["external"]["time"])
-        )
-
-        # Validate time and memory usage.
-        assert freeze_on_results["internal"]["time"] < 0.3 * freeze_off_results["internal"]["time"]
-        assert freeze_on_results["external"]["time"] < 0.3 * freeze_off_results["external"]["time"]
-        assert (
-            freeze_on_results["internal"]["allocated_bytes"]
-            <= freeze_off_results["internal"]["allocated_bytes"]
-        )
-        assert (
-            freeze_on_results["external"]["allocated_bytes"]
-            <= freeze_off_results["external"]["allocated_bytes"]
-        )
-        assert (
-            freeze_on_results["internal"]["reserved_bytes"]
-            <= freeze_off_results["internal"]["reserved_bytes"]
-        )
-        assert (
-            freeze_on_results["external"]["reserved_bytes"]
-            <= freeze_off_results["external"]["reserved_bytes"]
-        )
-
-
 # Global storage for comparing unique buffer counts across different num_microbatches,
 # keyed by (pp_size, vpp_size)
 _unique_buffer_counts = {}
@@ -1311,8 +1098,6 @@ def test_moe_partial_cudagraph(self, ep_size, moe_dropless_dispatcher, moe_dispa
     llava_test.test_llava_cudagraph_is_last_layer_logic()
     llava_test.teardown_method(method=None)
 
-    test = TestCaptureFreezeGC()
-    test.test_capture_freeze_gc()
     test = TestPartialCudaGraph()
     test.setup_method(method=None)
     test.test_moe_partial_cudagraph(4, True, "alltoall")
diff --git a/tests/unit_tests/transformer/test_mlp.py b/tests/unit_tests/transformer/test_mlp.py
index d2c25e0cc53..45e0df9ad0a 100644
--- a/tests/unit_tests/transformer/test_mlp.py
+++ b/tests/unit_tests/transformer/test_mlp.py
@@ -1,9 +1,10 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
+
 import pytest
 import torch
 
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.mlp import MLP
 from megatron.core.transformer.transformer_config import TransformerConfig
@@ -18,7 +19,7 @@ def setup_method(self, method):
         transformer_config = TransformerConfig(
             num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True
         )
-        self.mlp = MLP(transformer_config, get_gpt_layer_local_spec().submodules.mlp.submodules)
+        self.mlp = MLP(transformer_config, get_gpt_layer_local_submodules().mlp.submodules)
 
     def teardown_method(self, method):
         Utils.destroy_model_parallel()
diff --git a/tests/unit_tests/transformer/test_multi_latent_attention.py b/tests/unit_tests/transformer/test_multi_latent_attention.py
index bc8514ee561..83c65877fbb 100644
--- a/tests/unit_tests/transformer/test_multi_latent_attention.py
+++ b/tests/unit_tests/transformer/test_multi_latent_attention.py
@@ -1,27 +1,31 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 import os
-from functools import partial
-from importlib.metadata import version
 from inspect import signature
 from unittest import mock
 
 import pytest
 import torch
-import transformer_engine as te
 
 from megatron.core import parallel_state
 from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider
 from megatron.core.models.common.embeddings.rope_utils import (
     get_pos_emb_on_this_cp_rank as get_tensor_on_this_cp_rank,
 )
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_spec,
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.attention import Attention
 from megatron.core.transformer.enums import AttnMaskType
-from megatron.core.transformer.multi_latent_attention import MLASelfAttention, MultiLatentAttention
+from megatron.core.transformer.multi_latent_attention import (
+    MLASelfAttention,
+    MLASelfAttentionSubmodules,
+    MultiLatentAttention,
+)
 from megatron.core.transformer.transformer_config import MLATransformerConfig
 from megatron.core.utils import is_te_min_version, is_torch_min_version
 from megatron.training.arguments import parse_args
@@ -92,9 +96,10 @@ def make_test_packed_seq_params_with_padding(
 
 
 def get_mla_self_attn_submodules(linear_qkv_down_proj=None):
-    submodules = get_gpt_layer_with_transformer_engine_spec(
+    submodules = get_gpt_layer_with_transformer_engine_submodules(
         multi_latent_attention=True
-    ).submodules.self_attention.submodules
+    ).self_attention.submodules
+    assert isinstance(submodules, MLASelfAttentionSubmodules)
     if linear_qkv_down_proj is not None:
         submodules.linear_q_down_proj = linear_qkv_down_proj
         submodules.linear_kv_down_proj = linear_qkv_down_proj
diff --git a/tests/unit_tests/transformer/test_multi_token_prediction.py b/tests/unit_tests/transformer/test_multi_token_prediction.py
index ddfa9bfba16..1b8fc48c415 100644
--- a/tests/unit_tests/transformer/test_multi_token_prediction.py
+++ b/tests/unit_tests/transformer/test_multi_token_prediction.py
@@ -13,6 +13,8 @@
     get_gpt_mtp_block_spec,
 )
 from megatron.core.models.gpt.gpt_model import GPTModel
+from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
+from megatron.core.models.mamba.mamba_model import MambaModel
 from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator
 from megatron.core.packed_seq_params import PackedSeqParams
 from megatron.core.parallel_state import get_context_parallel_group
@@ -94,7 +96,7 @@ def test_constructor_local(self, tp):
             assert mtp.layers[i].hnorm.weight.shape[0] == config.hidden_size
             assert mtp.layers[i].eh_proj.weight.shape[0] == config.hidden_size / tp
             assert mtp.layers[i].eh_proj.weight.shape[1] == config.hidden_size * 2
-            assert mtp.layers[i].transformer_layer is not None
+            assert mtp.layers[i].mtp_model_layer is not None
         num_weights = sum([p.numel() for p in mtp.parameters()])
         if tp == 1:
             assert num_weights == 58560 * config.mtp_num_layers
@@ -120,7 +122,7 @@ def test_constructor_ues_te(self, tp, cp):
             assert mtp.layers[i].hnorm.weight.shape[0] == config.hidden_size
             assert mtp.layers[i].eh_proj.weight.shape[0] == config.hidden_size / tp
             assert mtp.layers[i].eh_proj.weight.shape[1] == config.hidden_size * 2
-            assert mtp.layers[i].transformer_layer is not None
+            assert mtp.layers[i].mtp_model_layer is not None
         num_weights = sum([p.numel() for p in mtp.parameters()])
         if tp == 1:
             assert num_weights == 58560 * config.mtp_num_layers
@@ -162,7 +164,7 @@ def model_provider(
             config=config,
             transformer_layer_spec=transformer_layer_spec,
             mtp_block_spec=mtp_block_spec,
-            vocab_size=args.vocal_size,
+            vocab_size=args.vocab_size,
             max_sequence_length=args.max_position_embeddings,
             pre_process=pre_process,
             post_process=post_process,
@@ -186,7 +188,7 @@ def create_test_args(
         args.num_layers = 2
         args.mtp_num_layers = 2
         args.mtp_loss_scaling_factor = 0.1
-        args.vocal_size = 128800
+        args.vocab_size = 128800
         args.hidden_size = 128
         args.num_attention_heads = 8
         args.max_position_embeddings = 256
@@ -205,7 +207,6 @@ def create_test_args(
         args.lr = 3e-5
         args.attention_dropout = 0.0
         args.hidden_dropout = 0.0
-        args.async_tensor_model_parallel_allreduce = False
         args.no_save_optim = True
         args.no_load_optim = True
         args.no_load_rng = True
@@ -676,10 +677,258 @@ def log(self, metrics, iteration):
 
         # Verify total_loss_dict is populated
         for i in range(num_layers):
-            assert f"mtp_{i+1} loss" in total_loss_dict
-            assert total_loss_dict[f"mtp_{i+1} loss"] == loss * loss_scale
+            assert f"mtp_{i + 1} loss" in total_loss_dict
+            assert total_loss_dict[f"mtp_{i + 1} loss"] == loss * loss_scale
 
         # Verify tracker is cleaned
         assert torch.all(MTPLossLoggingHelper.tracker["values"] == 0)
         assert MTPLossLoggingHelper.tracker["reduce_group"] is None
         assert MTPLossLoggingHelper.tracker["avg_group"] is None
+
+
+class TestMultiTokenPredictionMamba:
+    """Test Multi-Token Prediction with Mamba hybrid models."""
+
+    def setup_method(self, method):
+        self.seq_length = 32
+        self.micro_batch_size = 2
+        os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1'
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+        destroy_global_vars()
+        destroy_num_microbatches_calculator()
+        MTPLossLoggingHelper.tracker = {}
+
+    def model_provider(self, pre_process=True, post_process=True, **config_kwargs):
+        """Model provider for Mamba hybrid models with MTP.
+
+        Uses the unified pattern syntax where MTP is configured via hybrid_override_pattern:
+        Format: "<main_pattern>/<mtp_pattern>/<mtp_pattern>/..."
+        Example: "M*M*/M*/M*" = main decoder "M*M*", MTP pattern "M*" with 2 depths
+        """
+        model_parallel_cuda_manual_seed(_SEED)
+        args = get_args()
+        config = core_transformer_config_from_args(args)
+
+        # MTP is configured via unified pattern in hybrid_override_pattern
+        # MambaModel creates the MTP block internally based on the parsed pattern
+        model = MambaModel(
+            config=config,
+            mamba_stack_spec=mamba_stack_spec,
+            vocab_size=args.vocab_size,
+            max_sequence_length=args.max_position_embeddings,
+            pre_process=pre_process,
+            post_process=post_process,
+            hybrid_attention_ratio=args.hybrid_attention_ratio,
+            hybrid_mlp_ratio=args.hybrid_mlp_ratio,
+            hybrid_override_pattern=args.hybrid_override_pattern,
+            fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
+            parallel_output=True,
+            share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
+            position_embedding_type=args.position_embedding_type,
+            rotary_percent=args.rotary_percent,
+        )
+        return model
+
+    def create_test_args(
+        self, tp, cp, sequence_length, micro_batch_size, fp8=None, full_recompute=False
+    ):
+        destroy_global_vars()
+        destroy_num_microbatches_calculator()
+
+        sys.argv = ['test_multi_token_prediction_mamba.py']
+        args = parse_args()
+        args.num_layers = 4
+        args.mtp_num_layers = 2
+        args.mtp_loss_scaling_factor = 0.1
+        args.vocab_size = 128800
+        args.hidden_size = 128
+        args.num_attention_heads = 8
+        args.num_query_groups = 8
+        args.mamba_num_groups = 4
+        args.max_position_embeddings = 256
+        args.micro_batch_size = micro_batch_size
+        args.create_attention_mask_in_dataloader = True
+        args.seq_length = sequence_length
+        args.tensor_model_parallel_size = tp
+        args.sequence_parallel = True if tp > 1 else False
+        args.context_parallel_size = cp
+        args.position_embedding_type = 'rope'
+        args.train_iters = 1
+        args.ckpt_format = 'torch_dist'
+        args.lr = 3e-5
+        args.attention_dropout = 0.0
+        args.hidden_dropout = 0.0
+        args.no_save_optim = True
+        args.no_load_optim = True
+        args.no_load_rng = True
+        args.bf16 = True
+        args.hybrid_attention_ratio = 0.5
+        args.hybrid_mlp_ratio = 0.0
+        # Unified pattern: "main/mtp/mtp" - main decoder "M*M*", MTP pattern "M*" with 2 depths
+        args.hybrid_override_pattern = "M*M*/M*/M*"
+        args.spec = "megatron.core.models.mamba.mamba_layer_specs.mamba_stack_spec"
+
+        if fp8 is not None:
+            args.fp8 = 'e4m3'
+        if full_recompute:
+            args.recompute_granularity = 'full'
+            args.recompute_method = 'uniform'
+            args.recompute_num_layers = 1
+        else:
+            args.recompute_granularity = None
+        args.add_bias_linear = False
+        args.swiglu = True
+
+        validate_args(args)
+        set_global_variables(args, False)
+        return args
+
+    def get_batch(self, seq_length, micro_batch_size):
+        data = list(range(seq_length))
+        input_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda()
+        labels = 1 + torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda()
+        position_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda()
+        attention_mask = torch.ones(
+            (micro_batch_size, 1, seq_length, seq_length), dtype=bool
+        ).cuda()
+        loss_mask = torch.ones(seq_length).repeat((micro_batch_size, 1)).cuda()
+        batch = {
+            'tokens': input_ids,
+            'labels': labels,
+            'loss_mask': loss_mask,
+            'attention_mask': attention_mask,
+            'position_ids': position_ids,
+        }
+        return batch
+
+    @pytest.mark.skipif(not HAVE_TE, reason="transformer_engine not available")
+    @pytest.mark.parametrize(("tp", "cp"), [(1, 1), (2, 1)])
+    def test_sharded_state_dict_mamba(self, tp, cp):
+        """Test MTP with Mamba hybrid model - sharded state dict."""
+        args = self.create_test_args(tp, cp, self.seq_length, self.micro_batch_size)
+        set_args(args)
+        torch.manual_seed(_SEED)
+        Utils.initialize_model_parallel(tensor_model_parallel_size=tp, context_parallel_size=cp)
+        mamba_model = get_model(self.model_provider, ModelType.encoder_or_decoder)
+        mamba_model = unwrap_model(mamba_model)
+        sharded_state_dict = mamba_model[0].sharded_state_dict()
+
+        # Verify MTP layers are in the state dict
+        for i in range(args.mtp_num_layers):
+            assert f"mtp.layers.{i}.enorm.weight" in sharded_state_dict.keys()
+            assert f"mtp.layers.{i}.hnorm.weight" in sharded_state_dict.keys()
+            assert f"mtp.layers.{i}.eh_proj.weight" in sharded_state_dict.keys()
+
+    @pytest.mark.skipif(not HAVE_TE, reason="transformer_engine not available")
+    @pytest.mark.parametrize(("tp", "cp"), [(1, 1), (2, 1)])
+    def test_forward_backward_mamba(self, tmp_path_dist_ckpt, tp, cp):
+        """Test MTP forward and backward with Mamba hybrid model."""
+        tp_ref = 1
+        cp_ref = 1
+        args = self.create_test_args(tp_ref, cp_ref, self.seq_length, self.micro_batch_size)
+        set_args(args)
+        torch.manual_seed(_SEED)
+        Utils.initialize_model_parallel(
+            tensor_model_parallel_size=tp_ref, context_parallel_size=cp_ref
+        )
+        batch = self.get_batch(self.seq_length, self.micro_batch_size)
+        tokens, labels, loss_mask, attention_mask, position_ids = batch.values()
+
+        mamba_model_ref, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+            self.model_provider, ModelType.encoder_or_decoder
+        )
+
+        output_ref = mamba_model_ref[0].forward(
+            input_ids=tokens,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            loss_mask=loss_mask,
+        )
+        tracker = MTPLossLoggingHelper.tracker
+        mtp_loss_ref = None
+        assert "values" in tracker
+        mtp_loss_ref = tracker['values'].clone()
+        MTPLossLoggingHelper.clean_loss_in_tracker()
+
+        iteration = 123
+        num_floating_point_operations_so_far = 456
+
+        def set_ckpt_path(ckpt_path):
+            args.save = ckpt_path
+            args.load = ckpt_path
+
+        with TempNamedDir(tmp_path_dist_ckpt / 'test_mtp_mamba_model_reconfiguration') as ckpt_dir:
+            set_ckpt_path(ckpt_dir)
+            save_checkpoint(
+                iteration,
+                mamba_model_ref,
+                optimizer,
+                opt_param_scheduler,
+                num_floating_point_operations_so_far,
+            )
+
+            expected_ckpt_path = args.save / "iter_0000123" / ".metadata"
+            assert os.path.exists(expected_ckpt_path)
+
+            Utils.destroy_model_parallel()
+            args = self.create_test_args(tp, cp, self.seq_length, self.micro_batch_size)
+            set_args(args)
+            set_ckpt_path(ckpt_dir)
+            torch.manual_seed(_SEED)
+            Utils.initialize_model_parallel(tensor_model_parallel_size=tp, context_parallel_size=cp)
+            mamba_model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+                self.model_provider, ModelType.encoder_or_decoder
+            )
+            load_checkpoint(mamba_model, optimizer, opt_param_scheduler, strict=False)
+
+            batch["output_ref"] = output_ref
+            batch = get_batch_on_this_cp_rank(batch)
+            tokens, labels, loss_mask, attention_mask, position_ids, output_ref = batch.values()
+            output = mamba_model[0].forward(
+                input_ids=tokens,
+                position_ids=position_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                loss_mask=loss_mask,
+            )
+            tracker = MTPLossLoggingHelper.tracker
+            assert "values" in tracker
+            mtp_loss = tracker['values'].clone()
+            pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['cp'])
+            torch.distributed.all_reduce(
+                mtp_loss, group=pg_collection.cp, op=torch.distributed.ReduceOp.AVG
+            )
+            MTPLossLoggingHelper.clean_loss_in_tracker()
+            assert torch.allclose(output_ref, output, rtol=1e-03, atol=1e-03)
+            assert torch.allclose(mtp_loss, mtp_loss_ref, rtol=1e-02, atol=1e-02)
+
+            assert output.shape[0] == self.micro_batch_size
+            assert output.shape[1] == self.seq_length / cp
+
+            loss = output.mean()
+            loss.backward()
+            for name, param in mamba_model[0].named_parameters():
+                assert param.main_grad is not None
+
+    @pytest.mark.skipif(not HAVE_TE, reason="transformer_engine not available")
+    def test_attention_mask_validation_mamba(self):
+        """Test that attention mask type validation works for Mamba hybrid models."""
+        tp = 1
+        cp = 1
+        args = self.create_test_args(tp, cp, self.seq_length, self.micro_batch_size)
+        set_args(args)
+        torch.manual_seed(_SEED)
+        Utils.initialize_model_parallel(tensor_model_parallel_size=tp, context_parallel_size=cp)
+        try:
+            mamba_model = get_model(self.model_provider, ModelType.encoder_or_decoder)
+            mamba_model = unwrap_model(mamba_model)
+            assert isinstance(mamba_model[0], MambaModel)
+            assert mamba_model[0].mtp is not None
+        except AssertionError as e:
+            if "Multi-Token Prediction (MTP) is not yet supported" in str(e):
+                pytest.fail(f"Attention mask validation failed for Mamba hybrid model: {e}")
+            else:
+                raise
diff --git a/tests/unit_tests/transformer/test_spec_customization.py b/tests/unit_tests/transformer/test_spec_customization.py
index d6b2a2eb623..84fb2ee706c 100755
--- a/tests/unit_tests/transformer/test_spec_customization.py
+++ b/tests/unit_tests/transformer/test_spec_customization.py
@@ -1,9 +1,8 @@
 # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
 
 import sys
-from dataclasses import dataclass, fields
+from dataclasses import fields
 
-import pytest
 import torch
 import transformer_engine as te
 
@@ -14,12 +13,11 @@
     TERowParallelLinear,
 )
 from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules
 from megatron.core.parallel_state import get_context_parallel_group, get_tensor_model_parallel_group
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
-from megatron.core.transformer.dot_product_attention import DotProductAttention
 from megatron.core.transformer.enums import AttnMaskType
 from megatron.core.transformer.identity_op import IdentityFuncOp, IdentityOp
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module, import_module
@@ -203,20 +201,18 @@ def test_transformer_block_custom(self):
         transformer_config = TransformerConfig(
             num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True
         )
-        layer_local_spec = get_gpt_layer_local_spec()
+        submodules = get_gpt_layer_local_submodules()
 
         # The following way can be used to pass a different `TransformerLayer`
         # and internally the `TransformerBlock` would fan out the single
         # `ModuleSpec` layer spec provided to all the layers of the block.
-        layer_spec1 = ModuleSpec(module=TransformerLayer, submodules=layer_local_spec.submodules)
+        layer_spec1 = ModuleSpec(module=TransformerLayer, submodules=submodules)
         model_parallel_cuda_manual_seed(123)
         torch.manual_seed(0)
         parallel_transformer_block1 = TransformerBlock(transformer_config, layer_spec1)
 
         layer_spec2 = TransformerBlockSubmodules(
-            layer_specs=[
-                ModuleSpec(module=TransformerLayer, submodules=layer_local_spec.submodules)
-            ]
+            layer_specs=[ModuleSpec(module=TransformerLayer, submodules=submodules)]
             * transformer_config.num_layers,
             layer_norm=TENorm,
         )
@@ -252,12 +248,10 @@ def test_transformer_block_custom(self):
 
     def test_l2_qk_norm(self):
         """Test L2 normalization for QK vectors using local spec."""
-        layer_spec = get_gpt_layer_local_spec(qk_l2_norm=True)
+        submodules = get_gpt_layer_local_submodules(qk_l2_norm=True)
 
         # Build the self-attention module from the spec
-        self_attention = build_module(
-            layer_spec.submodules.self_attention, config=self.config, layer_number=1
-        )
+        self_attention = build_module(submodules.self_attention, config=self.config, layer_number=1)
 
         assert isinstance(self_attention, SelfAttention)
         # Verify that q_layernorm and k_layernorm are L2Norm instances
diff --git a/tests/unit_tests/transformer/test_submodule_callables.py b/tests/unit_tests/transformer/test_submodule_callables.py
index 73059495c06..03e2d751a52 100644
--- a/tests/unit_tests/transformer/test_submodule_callables.py
+++ b/tests/unit_tests/transformer/test_submodule_callables.py
@@ -3,7 +3,9 @@
 import torch
 
 from megatron.core.models.gpt.fine_grained_callables import build_layer_callables
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.transformer.transformer_layer import TransformerLayer
 from megatron.core.utils import is_te_min_version
 from tests.unit_tests.a2a_overlap.utils import (
@@ -140,13 +142,13 @@ def test_1f1b_overlap(self, dispatcher_type, grouped_gemm, permute_fusion):
         config = get_test_config(extra_kwargs=extra_kwargs, moe_grouped_gemm=grouped_gemm)
         microbatches = 4
         with deterministic_mode():
-            transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+            transformer_layer_submodules = get_gpt_layer_with_transformer_engine_submodules(
                 num_experts=8,
                 moe_grouped_gemm=grouped_gemm,
                 qk_layernorm=True,
                 multi_latent_attention=True,
             )
-            model = TransformerLayer(config, transformer_layer_spec.submodules)
+            model = TransformerLayer(config, transformer_layer_submodules)
 
             params = reset_model(model)
             input_tensors = [build_data() for _ in range(microbatches)]
diff --git a/tests/unit_tests/transformer/test_transformer_block.py b/tests/unit_tests/transformer/test_transformer_block.py
index 83b29613157..1cb50c82daa 100644
--- a/tests/unit_tests/transformer/test_transformer_block.py
+++ b/tests/unit_tests/transformer/test_transformer_block.py
@@ -144,6 +144,180 @@ def _run_selective_checkpoint_test(self, fp8):
         assert hidden_states.shape[1] == micro_batch_size
         assert hidden_states.shape[2] == config.hidden_size
 
+    def test_feature_extraction_without_checkpoint(self):
+        """Test feature extraction in normal forward mode (no checkpointing)."""
+        parallel_transformer_block = self.parallel_transformer_block
+        config = parallel_transformer_block.config
+
+        sequence_length = 32
+        micro_batch_size = 2
+        parallel_transformer_block.cuda()
+
+        # [sequence length, batch size, hidden size]
+        hidden_states = torch.ones((sequence_length, micro_batch_size, config.hidden_size))
+        hidden_states = hidden_states.cuda()
+
+        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
+
+        # Test with None (should return just hidden_states)
+        output = parallel_transformer_block(
+            hidden_states=hidden_states, attention_mask=attention_mask, extract_layer_indices=None
+        )
+        assert isinstance(output, torch.Tensor)
+        assert output.shape == (sequence_length, micro_batch_size, config.hidden_size)
+
+        # Test with single layer extraction
+        extract_indices = {0}
+        result = parallel_transformer_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            extract_layer_indices=extract_indices,
+        )
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        output_hidden_states, intermediate_hidden_states = result
+        assert output_hidden_states.shape == (sequence_length, micro_batch_size, config.hidden_size)
+        assert len(intermediate_hidden_states) == 1
+        assert intermediate_hidden_states[0].shape == (
+            sequence_length,
+            micro_batch_size,
+            config.hidden_size,
+        )
+
+        # Test with multiple layer extraction (config has 2 layers: indices 0, 1)
+        extract_indices = {0, 1}
+        result = parallel_transformer_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            extract_layer_indices=extract_indices,
+        )
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        output_hidden_states, intermediate_hidden_states = result
+        assert output_hidden_states.shape == (sequence_length, micro_batch_size, config.hidden_size)
+        assert len(intermediate_hidden_states) == 2
+        for intermediate in intermediate_hidden_states:
+            assert intermediate.shape == (sequence_length, micro_batch_size, config.hidden_size)
+
+    def test_feature_extraction_full_checkpoint_block(self):
+        """Test feature extraction with full checkpoint using block method."""
+        transformer_config = self.transformer_config
+        config = transformer_config
+        config.recompute_granularity = 'full'
+        config.recompute_method = 'block'
+        config.recompute_num_layers = config.num_layers  # checkpoint all layers
+        block_transformer_block = TransformerBlock(
+            config, get_gpt_layer_with_transformer_engine_spec()
+        )
+        block_transformer_block.cuda()
+
+        sequence_length = 32
+        micro_batch_size = 2
+
+        # [sequence length, batch size, hidden size]
+        hidden_states = torch.ones((sequence_length, micro_batch_size, config.hidden_size))
+        hidden_states = hidden_states.cuda()
+        hidden_states.requires_grad = True
+
+        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
+
+        # Test with None (should return just hidden_states)
+        output = block_transformer_block(
+            hidden_states=hidden_states, attention_mask=attention_mask, extract_layer_indices=None
+        )
+        assert isinstance(output, torch.Tensor)
+        assert output.shape == (sequence_length, micro_batch_size, config.hidden_size)
+
+        # Test with single layer extraction
+        extract_indices = {0}
+        result = block_transformer_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            extract_layer_indices=extract_indices,
+        )
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        output_hidden_states, intermediate_hidden_states = result
+        assert output_hidden_states.shape == (sequence_length, micro_batch_size, config.hidden_size)
+        assert len(intermediate_hidden_states) == 1
+        assert intermediate_hidden_states[0].shape == (
+            sequence_length,
+            micro_batch_size,
+            config.hidden_size,
+        )
+
+        # Test with multiple layer extraction (config has 2 layers: indices 0, 1)
+        # Unlike uniform, block method supports extraction at every layer
+        extract_indices = {0, 1}
+        result = block_transformer_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            extract_layer_indices=extract_indices,
+        )
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        output_hidden_states, intermediate_hidden_states = result
+        assert output_hidden_states.shape == (sequence_length, micro_batch_size, config.hidden_size)
+        assert len(intermediate_hidden_states) == 2
+        for intermediate in intermediate_hidden_states:
+            assert intermediate.shape == (sequence_length, micro_batch_size, config.hidden_size)
+
+    def test_feature_extraction_full_checkpoint_uniform(self):
+        """Test feature extraction with full checkpoint using uniform method.
+
+        With recompute_num_layers=2 (chunk size 2) and num_layers=2, all layers
+        fall into a single chunk [0, 1]. The uniform method can only extract
+        features at chunk boundaries (the last layer of each chunk), so
+        requesting {0, 1} returns only 1 embedding (layer 1's output).
+        Layer 0's activation is discarded during the forward pass.
+        """
+        transformer_config = self.transformer_config
+        config = transformer_config
+        config.recompute_granularity = 'full'
+        config.recompute_method = 'uniform'
+        config.recompute_num_layers = 2  # chunk size 2: single chunk [0, 1]
+        uniform_transformer_block = TransformerBlock(
+            config, get_gpt_layer_with_transformer_engine_spec()
+        )
+        uniform_transformer_block.cuda()
+
+        sequence_length = 32
+        micro_batch_size = 2
+
+        # [sequence length, batch size, hidden size]
+        hidden_states = torch.ones((sequence_length, micro_batch_size, config.hidden_size))
+        hidden_states = hidden_states.cuda()
+        hidden_states.requires_grad = True
+
+        attention_mask = torch.ones((1, 1, sequence_length, sequence_length), dtype=bool).cuda()
+
+        # Test with None (should return just hidden_states)
+        output = uniform_transformer_block(
+            hidden_states=hidden_states, attention_mask=attention_mask, extract_layer_indices=None
+        )
+        assert isinstance(output, torch.Tensor)
+        assert output.shape == (sequence_length, micro_batch_size, config.hidden_size)
+
+        # With chunk size 2, layers [0, 1] form one chunk.
+        # Only the chunk boundary (layer 1) can be extracted; layer 0 is inside the chunk.
+        extract_indices = {0, 1}
+        result = uniform_transformer_block(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            extract_layer_indices=extract_indices,
+        )
+        assert isinstance(result, tuple)
+        assert len(result) == 2
+        output_hidden_states, intermediate_hidden_states = result
+        assert output_hidden_states.shape == (sequence_length, micro_batch_size, config.hidden_size)
+        # Only 1 embedding returned: layer 1 (the chunk boundary). Layer 0 is skipped.
+        assert len(intermediate_hidden_states) == 1
+        assert intermediate_hidden_states[0].shape == (
+            sequence_length,
+            micro_batch_size,
+            config.hidden_size,
+        )
+
 
 class TestPipelineParallelTransformerBlock:
     @pytest.mark.parametrize(
diff --git a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py
index bb64efe7449..c55babe35ca 100644
--- a/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py
+++ b/tests/unit_tests/transformer/test_transformer_block_custom_pgs.py
@@ -68,7 +68,7 @@ def __init__(
         # Temporarily replace attention and MLP with IdentityOp,
         # This is a temporary workaround for the test until we have a better interface
         # will rebuild them with custom process groups after super init
-        def _modify_submodules(submodules):
+        def _modify_submodules(submodules: TransformerLayerSubmodules):
             submodules.self_attention = IdentityOp
             submodules.mlp = IdentityOp
             return submodules
diff --git a/tests/unit_tests/transformer/test_transformer_layer.py b/tests/unit_tests/transformer/test_transformer_layer.py
index 7db9aa30fec..da1f9ce5860 100644
--- a/tests/unit_tests/transformer/test_transformer_layer.py
+++ b/tests/unit_tests/transformer/test_transformer_layer.py
@@ -7,7 +7,9 @@
 from megatron.core import parallel_state
 from megatron.core.dist_checkpointing.mapping import ShardedObject, ShardedTensor
 from megatron.core.inference.contexts import StaticInferenceContext
-from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_layer_with_transformer_engine_submodules,
+)
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer.transformer_config import TransformerConfig
 from megatron.core.transformer.transformer_layer import (
@@ -26,7 +28,7 @@ def setup_method(self, method):
             num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True
         )
         self.parallel_transformer_layer = TransformerLayer(
-            transformer_config, get_gpt_layer_with_transformer_engine_spec().submodules
+            transformer_config, get_gpt_layer_with_transformer_engine_submodules()
         )
 
     def teardown_method(self, method):
@@ -81,7 +83,7 @@ def test(
                     use_cpu_initialization=True,
                 )
                 parallel_transformer_layer = TransformerLayer(
-                    transformer_config, get_gpt_layer_with_transformer_engine_spec().submodules
+                    transformer_config, get_gpt_layer_with_transformer_engine_submodules()
                 )
 
                 parallel_transformer_layer.cuda()
@@ -265,7 +267,7 @@ def test_sharded_state_dict(self, tp_pp, order):
             num_layers=2, hidden_size=128, num_attention_heads=8, use_cpu_initialization=True
         )
         parallel_transformer_layer = TransformerLayer(
-            transformer_config, get_gpt_layer_with_transformer_engine_spec().submodules
+            transformer_config, get_gpt_layer_with_transformer_engine_submodules()
         )
 
         sharded_state_dict = parallel_transformer_layer.sharded_state_dict()
diff --git a/tools/checkpoint/saver_base.py b/tools/checkpoint/saver_base.py
index 4958dc99ed7..19cdf9af729 100644
--- a/tools/checkpoint/saver_base.py
+++ b/tools/checkpoint/saver_base.py
@@ -62,7 +62,7 @@ def _load_checkpoint_args(self, margs):
             args_to_keep = ['tensor_model_parallel_size', 'pipeline_model_parallel_size', 'expert_model_parallel_size', 'world_size', 'params_dtype',
                             'num_layers_per_virtual_pipeline_stage', 'virtual_pipeline_model_parallel_size',
                             'masked_softmax_fusion', 'bias_gelu_fusion', 'bias_dropout_fusion',
-                            'sequence_parallel', 'async_tensor_model_parallel_allreduce',
+                            'sequence_parallel',
                             'no_load_optim', 'no_load_rng', 'no_save_optim', 'no_save_rng',
                             'vocab_file', 'tokenizer_model',
                             'save_interval', 'save',
@@ -170,9 +170,15 @@ def initialize_megatron_env(self):
         
         # For backward compatibility during local parallel states refactoring
         fake_tp_group = _ConverterFakeProcessGroup(size=self.args.target_tensor_parallel_size)
+        fake_pp_group = _ConverterFakeProcessGroup(size=self.args.target_pipeline_parallel_size)
         fake_ep_group = _ConverterFakeProcessGroup(size=self.args.target_expert_parallel_size)
+        fake_dp_group = _ConverterFakeProcessGroup(size=1)
         mpu._TENSOR_MODEL_PARALLEL_GROUP = fake_tp_group
+        mpu._PIPELINE_MODEL_PARALLEL_GROUP = fake_pp_group
         mpu._EXPERT_MODEL_PARALLEL_GROUP = fake_ep_group
+        mpu._DATA_PARALLEL_GROUP = fake_dp_group
+        mpu._DATA_PARALLEL_GROUP_WITH_CP = fake_dp_group
+        mpu._INTRA_PARTIAL_DATA_PARALLEL_GROUP_WITH_CP = fake_dp_group
         fused_kernels.load(self.margs)
         
         try:
@@ -232,7 +238,6 @@ def build_sys_argv(self):
                     '--no-masked-softmax-fusion',
                     '--no-bias-gelu-fusion',
                     '--no-bias-dropout-fusion',
-                    '--no-async-tensor-model-parallel-allreduce',
                     '--use-cpu-initialization',
                     '--micro-batch-size', '1',
                     '--no-load-optim',
@@ -356,7 +361,7 @@ def receive_lm(self, schema, prefix=None):
         """
         try:
             from megatron.core import mpu
-            from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
+            from megatron.core.tokenizers.utils.build_tokenizer import vocab_size_with_padding
         except ModuleNotFoundError as e:
             print(f"Unable to import required Megatron modules: {e}")
             sys.exit(1)
@@ -375,7 +380,7 @@ def pad_weight(orig_word_embed, true_vocab_size):
             if true_vocab_size is not None:
                 # figure out what our padded vocab size is
                 orig_vocab_size = orig_word_embed.shape[0]
-                self.margs.padded_vocab_size = _vocab_size_with_padding(true_vocab_size, self.margs)
+                self.margs.padded_vocab_size = vocab_size_with_padding(true_vocab_size, self.margs)
 
                 # Cut out extra padding we don't need
                 if orig_vocab_size > self.margs.padded_vocab_size:
diff --git a/tools/checkpoint/saver_legacy.py b/tools/checkpoint/saver_legacy.py
index 0e582bccf89..38d78ed5559 100644
--- a/tools/checkpoint/saver_legacy.py
+++ b/tools/checkpoint/saver_legacy.py
@@ -39,7 +39,7 @@ def save_checkpoint(queue, args):
         from megatron.training.checkpointing import save_checkpoint
         from megatron.training.global_vars import set_global_variables, get_args
         from megatron.core.enums import ModelType
-        from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
+        from megatron.core.tokenizers.utils.build_tokenizer import vocab_size_with_padding
         from megatron.legacy import fused_kernels
         from megatron.core import mpu
     except ModuleNotFoundError:
@@ -109,7 +109,6 @@ def check_message(msg):
                 '--no-masked-softmax-fusion',
                 '--no-bias-gelu-fusion',
                 '--no-bias-dropout-fusion',
-                '--no-async-tensor-model-parallel-allreduce',
                 '--use-cpu-initialization',
                 '--micro-batch-size', '1',
                 '--no-load-optim',
@@ -146,7 +145,7 @@ def check_message(msg):
         args_to_keep = ['tensor_model_parallel_size', 'pipeline_model_parallel_size', 'world_size', 'params_dtype',
                         'num_layers_per_virtual_pipeline_stage', 'virtual_pipeline_model_parallel_size',
                         'masked_softmax_fusion', 'bias_gelu_fusion', 'bias_dropout_fusion',
-                        'sequence_parallel', 'async_tensor_model_parallel_allreduce',
+                        'sequence_parallel',
                         'no_load_optim', 'no_load_rng', 'no_save_optim', 'no_save_rng',
                         'vocab_file', 'tokenizer_model',
                         'save_interval', 'save',
@@ -234,7 +233,7 @@ def get_models(count, dtype, pre_process, post_process):
     if md.true_vocab_size is not None:
         # figure out what our padded vocab size is
         orig_vocab_size = orig_word_embed.shape[0]
-        margs.padded_vocab_size = _vocab_size_with_padding(md.true_vocab_size, margs)
+        margs.padded_vocab_size = vocab_size_with_padding(md.true_vocab_size, margs)
 
         # Cut out extra padding we don't need
         if orig_vocab_size > margs.padded_vocab_size:
diff --git a/tools/checkpoint/saver_llava.py b/tools/checkpoint/saver_llava.py
index 3b7f7891a9f..326e3ea665c 100644
--- a/tools/checkpoint/saver_llava.py
+++ b/tools/checkpoint/saver_llava.py
@@ -57,7 +57,7 @@ def _load_checkpoint_args(self, margs):
             args_to_keep = ['tensor_model_parallel_size', 'pipeline_model_parallel_size', 'expert_model_parallel_size', 'world_size', 'params_dtype',
                             'num_layers_per_virtual_pipeline_stage', 'virtual_pipeline_model_parallel_size',
                             'masked_softmax_fusion', 'bias_gelu_fusion', 'bias_dropout_fusion',
-                            'sequence_parallel', 'async_tensor_model_parallel_allreduce',
+                            'sequence_parallel',
                             'no_load_optim', 'no_load_rng', 'no_save_optim', 'no_save_rng',
                             'vocab_file',
                             'save_interval', 'save',
@@ -99,7 +99,6 @@ def build_sys_argv(self):
                     '--no-masked-softmax-fusion',
                     '--no-bias-gelu-fusion',
                     '--no-bias-dropout-fusion',
-                    '--no-async-tensor-model-parallel-allreduce',
                     '--use-cpu-initialization',
                     '--micro-batch-size', '1',
                     '--no-load-optim',
diff --git a/tools/preprocess_data.py b/tools/preprocess_data.py
index 03e5329459c..3ac06c412b6 100644
--- a/tools/preprocess_data.py
+++ b/tools/preprocess_data.py
@@ -22,9 +22,7 @@
     PunktLanguageVars = object  # Fallback to the built-in object class
     nltk_available = False
 
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer as build_new_tokenizer
-from megatron.core.tokenizers import MegatronTokenizer
-from megatron.training.tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.training.arguments import _add_tokenizer_args
 from megatron.core.datasets import indexed_dataset
 
@@ -53,10 +51,7 @@ def __init__(self, args):
 
     def initializer(self):
         # Use Encoder class as a container for global data
-        if self.args.legacy_tokenizer:
-            Encoder.tokenizer = build_tokenizer(self.args)
-        else:
-            Encoder.tokenizer = build_new_tokenizer(self.args)
+        Encoder.tokenizer = build_tokenizer(self.args)
         if self.args.split_sentences:
             if not nltk_available:
                 print("NLTK is not available to split sentences.")
@@ -156,10 +151,7 @@ def process_json_file(self, file_name):
 
         startup_start = time.time()
         encoder = Encoder(self.args)
-        if self.args.legacy_tokenizer:
-            tokenizer = build_tokenizer(self.args)
-        else:
-            tokenizer = build_new_tokenizer(self.args)
+        tokenizer = build_tokenizer(self.args)
         pool = multiprocessing.Pool(self.workers, initializer=encoder.initializer)
         encoded_docs = pool.imap(encoder.encode, fin, 32)
 
@@ -227,8 +219,6 @@ def get_args():
     group.add_argument('--keep-sequential-samples', action='store_true',
                        help='Ensure ordering of samples in .jsonl files is '
                             'preserved when using partitions>1.')
-    # group.add_argument('--legacy-tokenizer', action='store_true',
-    #                    help='Use legacy tokenizer system.')
     args = parser.parse_args()
     args.keep_empty = False
 
@@ -381,10 +371,7 @@ def main():
     output_bin_files = {}
     output_idx_files = {}
     builders = {}
-    if args.legacy_tokenizer:
-        tokenizer = build_tokenizer(args)
-    else:
-        tokenizer = build_new_tokenizer(args)
+    tokenizer = build_tokenizer(args)
 
     for key in args.json_keys:
         output_bin_files[key] = "{}_{}_{}.bin".format(args.output_prefix,
diff --git a/tools/preprocess_data_nmt.py b/tools/preprocess_data_nmt.py
index 13a04f6ee28..f753c54f578 100644
--- a/tools/preprocess_data_nmt.py
+++ b/tools/preprocess_data_nmt.py
@@ -11,7 +11,7 @@
                                              os.path.pardir)))
 import time
 import torch
-from megatron.training.tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.datasets import indexed_dataset
 
 
diff --git a/tools/preprocess_mmdata.py b/tools/preprocess_mmdata.py
index 8ab2c2b8673..b63c9e99cad 100755
--- a/tools/preprocess_mmdata.py
+++ b/tools/preprocess_mmdata.py
@@ -20,7 +20,7 @@
 except ImportError:
     PunktLanguageVars = object  # Fallback to the built-in object class
 
-from megatron.training.tokenizer import build_tokenizer
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.datasets.indexed_dataset import IndexedDatasetBuilder
 
 
@@ -101,6 +101,17 @@ def get_args():
                        help='Language to use for NLTK-powered sentence splitting.')
     group.add_argument('--tokenizer-model', type=str, default=None,
                        help='sentencepeice tokenizer model.')
+    group.add_argument('--tokenizer-metadata', type=str, default=None,
+                       help='Path to tokenizer metadata in json format.')
+    group.add_argument('--tokenizer-special-tokens', type=str, nargs='+', default=None,
+                       help='List of special tokens. For TikTokenizer needs to have '
+                            '["<unk>", "<s>", "</s>", "<mask>", "<pad>", "<cls>", "<sep>"]')
+    group.add_argument('--tokenizer-hf-no-use-fast', action='store_true', default=False,
+                       help='Whether to use fast HuggingFace tokenizer.')
+    group.add_argument('--tokenizer-hf-no-include-special-tokens', action='store_true', default=False,
+                       help='Converting text to ids will not include special for HuggingFace tokenizer.')
+    group.add_argument("--trust-remote-code", action="store_true", default=False,
+                       help='Whether or not to allow PreTrainedTokenizer to execute remote code')
 
     group = parser.add_argument_group(title='output data')
     group.add_argument('--output-prefix', type=str, required=True,
@@ -110,6 +121,7 @@ def get_args():
                        help='Number of worker processes to launch')
     group.add_argument('--log-interval', type=int, default=100,
                        help='Interval between progress updates')
+
     args = parser.parse_args()
     args.keep_empty = False
 
diff --git a/tools/run_dynamic_text_generation_server.py b/tools/run_dynamic_text_generation_server.py
index 615073b8fd0..d054b542f90 100644
--- a/tools/run_dynamic_text_generation_server.py
+++ b/tools/run_dynamic_text_generation_server.py
@@ -5,26 +5,21 @@
 
 import torch
 
-from examples.inference.gpt.gpt_dynamic_inference import (
-    add_dynamic_inference_args,
-    get_inference_context,
-    get_inference_controller,
-    get_model,
-)
 from megatron.core.inference.engines import DynamicInferenceEngine
 from megatron.core.inference.text_generation_server.dynamic_text_gen_server import run_flask_server
-from megatron.core.tokenizers.text.utils.build_tokenizer import build_tokenizer
-from megatron.core.utils import get_mamba_inference_state_config_from_model, trace_async_exceptions
+from megatron.core.utils import trace_async_exceptions
+from megatron.inference.utils import add_inference_args, get_dynamic_inference_engine
 from megatron.post_training.arguments import add_modelopt_args
-from megatron.training import get_args, get_tokenizer
+from megatron.training import get_args
 from megatron.training.initialize import initialize_megatron
 
 
 def add_text_generation_server_args(parser: argparse.ArgumentParser):
     """Adds the required command line arguments for running the text generation server."""
     parser = add_modelopt_args(parser)
-    parser = add_dynamic_inference_args(parser)
+    parser = add_inference_args(parser)
     parser.add_argument("--port", type=int, default=5000, help="Port for Flask server to run on")
+    parser.add_argument("--parsers", type=str, nargs="+", default=[], help="Parsers to use for parsing the response")
     return parser
 
 
@@ -42,7 +37,7 @@ async def run_text_generation_server(
 
     rank = torch.distributed.get_rank()
 
-    await engine.start_listening_to_data_parallel_coordinator(
+    coordinator_addr = await engine.start_listening_to_data_parallel_coordinator(
         inference_coordinator_port=coordinator_port, launch_inference_coordinator=True
     )
 
@@ -50,10 +45,12 @@ async def run_text_generation_server(
     if rank == 0:
         server_task = asyncio.create_task(
             run_flask_server(
-                coordinator_port=coordinator_port,
+                coordinator_addr=coordinator_addr,
                 tokenizer=engine.controller.tokenizer,
+                parsers=args.parsers,
                 rank=rank,
                 flask_port=flask_port,
+                verbose=args.inference_flask_server_logging,
             )
         )
     engine_task = engine.engine_loop_task
@@ -74,36 +71,12 @@ async def run_text_generation_server(
             args_defaults={'no_load_rng': True, 'no_load_optim': True},
         )
 
-        args = get_args()
-        model = get_model()
-
-        if args.legacy_tokenizer:
-            tokenizer = get_tokenizer()
-        else:
-            tokenizer = build_tokenizer(args)
-
-        mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
-
         # Enable return_log_probs to allow prompt logprobs computation for echo=True requests
         # This sets materialize_only_last_token_logits=False in the inference context,
         # which is required for lm-eval compatibility (loglikelihood evaluation tasks)
+        args = get_args()
         args.return_log_probs = True
 
-        context = get_inference_context(
-            None,
-            None,
-            calculate_max_sequence_length_from_requests=False,
-            mamba_inference_state_config=mamba_inference_state_config,
-        )
-
-        controller = get_inference_controller(model, context)
-
-        engine = DynamicInferenceEngine(
-            controller,
-            context,
-            enable_cuda_graph=args.cuda_graph_impl == "local",
-            random_seed=args.seed,
-            enable_chunked_prefill=not args.disable_chunked_prefill,
-        )
+        engine = get_dynamic_inference_engine()
 
         asyncio.run(run_text_generation_server(engine, args.inference_coordinator_port, args.port))
diff --git a/tools/run_inference_performance_test.py b/tools/run_inference_performance_test.py
index 32d61444530..a1feef380ef 100644
--- a/tools/run_inference_performance_test.py
+++ b/tools/run_inference_performance_test.py
@@ -10,33 +10,32 @@
 
 from gpt_builders import gpt_builder
 from mamba_builders import mamba_builder
-from megatron.core.inference.contexts import DynamicInferenceContext
+from megatron.core.inference.contexts import StaticInferenceContext
 from megatron.core.inference.engines import DynamicInferenceEngine, StaticInferenceEngine
 from megatron.core.inference.engines.abstract_engine import AbstractEngine
-from megatron.core.inference.inference_request import InferenceRequest
+from megatron.core.inference.inference_request import (
+    DynamicInferenceRequestRecord,
+    InferenceRequest,
+)
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
 )
+from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
 from megatron.core.transformer.module import MegatronModule
-from megatron.core.utils import get_mamba_inference_state_config_from_model
+from megatron.inference.utils import add_inference_args, get_dynamic_inference_engine, get_model_for_inference
 from model_provider import model_provider
 
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
 )
 
-import asyncio
 from functools import partial
-from typing import List, Union
+from typing import List
 
-from examples.inference.gpt.utils import add_common_inference_args
 from megatron.core import mpu
 from megatron.training import get_args, get_model, get_tokenizer
 from megatron.training.checkpointing import load_checkpoint
@@ -47,7 +46,7 @@
 
 def add_inference_benchmarking_args(parser):
     """Inference benchmarking arguments."""
-    parser = add_common_inference_args(parser)
+    parser = add_inference_args(parser)
 
     group = parser.add_argument_group(title='inference_benchmarking')
 
@@ -60,7 +59,6 @@ def add_inference_benchmarking_args(parser):
     group.add_argument(
         "--benchmark-profile", action="store_true", default=False, help="If set, profile"
     )
-    group.add_argument('--stream', action="store_true", default=False, help="If set, stream tokens")
     return parser
 
 
@@ -74,24 +72,13 @@ def get_inference_engine(args: argparse.Namespace, model: MegatronModule) -> Abs
     Returns:
         AbstractBackend: The chosen backend
     """
-    tokenizer = get_tokenizer()
-
-    inference_wrapper_config = InferenceWrapperConfig(
-        hidden_size=args.hidden_size,
-        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
-        fp32_residual_connection=args.fp32_residual_connection,
-        params_dtype=args.params_dtype,
-        padded_vocab_size=args.padded_vocab_size,
-        inference_max_requests=args.inference_max_batch_size,
-        inference_max_seq_length=args.inference_max_seq_length,
-        nccl_all_reduce_for_prefill=args.nccl_all_reduce_for_prefill,
-        moe_pad_experts_for_cuda_graph_inference=args.moe_pad_experts_for_cuda_graph_inference,
-    )
-
-    mamba_inference_state_config = get_mamba_inference_state_config_from_model(model)
 
     if args.engine_type == "static":
-        inference_wrapped_model = GPTInferenceWrapper(model, inference_wrapper_config)
+        tokenizer = get_tokenizer()
+        context = StaticInferenceContext(
+            args.inference_max_requests, args.inference_max_sequence_length
+        )
+        inference_wrapped_model = GPTInferenceWrapper(model, context)
         inference_wrapped_model.model_is_pipeline_parallel = not (
             mpu.is_pipeline_first_stage() and mpu.is_pipeline_last_stage()
         )
@@ -100,98 +87,7 @@ def get_inference_engine(args: argparse.Namespace, model: MegatronModule) -> Abs
         )
         return StaticInferenceEngine(text_generation_controller=text_generation_controller)
     elif args.engine_type == "dynamic":
-        context = DynamicInferenceContext(
-            params_dtype=args.params_dtype,
-            num_layers=args.num_layers,
-            kv_channels=args.kv_channels,
-            num_attention_heads=(
-                args.num_query_groups if args.group_query_attention else args.num_attention_heads
-            ),
-            max_sequence_length=args.inference_max_seq_length,
-            num_cuda_graphs=(
-                args.inference_dynamic_batching_num_cuda_graphs
-                if args.cuda_graph_impl == "local"
-                else None
-            ),
-            buffer_size_gb=args.inference_dynamic_batching_buffer_size_gb,
-            buffer_guaranteed_fraction=args.inference_dynamic_batching_buffer_guaranteed_fraction,
-            buffer_overflow_factor=args.inference_dynamic_batching_buffer_overflow_factor,
-            max_requests_override=args.inference_dynamic_batching_max_requests_override,
-            max_tokens_override=args.inference_dynamic_batching_max_tokens_override,
-            block_size_tokens=args.inference_dynamic_batching_block_size,
-            tensor_model_parallel_size=args.tensor_model_parallel_size,
-            pipeline_model_parallel_size=args.pipeline_model_parallel_size,
-            materialize_only_last_token_logits=not args.return_log_probs,
-            mamba_inference_state_config=mamba_inference_state_config,
-            cache_mla_latent=args.multi_latent_attention and args.cache_mla_latents,
-            kv_lora_rank=args.kv_lora_rank if args.multi_latent_attention else None,
-            qk_pos_emb_head_dim=args.qk_pos_emb_head_dim,
-            use_cuda_graphs_for_non_decode_steps=not args.decode_only_cuda_graphs,
-            use_flashinfer_fused_rope=args.use_flashinfer_fused_rope,
-            unified_memory_level=args.inference_dynamic_batching_unified_memory_level,
-        )
-        inference_wrapped_model = GPTInferenceWrapper(
-            model, inference_wrapper_config, inference_context=context
-        )
-        inference_wrapped_model.model_is_pipeline_parallel = not (
-            mpu.is_pipeline_first_stage() and mpu.is_pipeline_last_stage()
-        )
-        text_generation_controller = TextGenerationController(
-            inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
-        )
-        return DynamicInferenceEngine(
-            text_generation_controller,
-            context,
-            termination_id=-1,
-            enable_cuda_graph=args.cuda_graph_impl == "local",
-            random_seed=args.seed,
-        )
-
-
-async def generate(
-    inference_engine: Union[StaticInferenceEngine, DynamicInferenceEngine],
-    sampling_params: SamplingParams,
-    prompts: List[str],
-    inference_requests: List[InferenceRequest] = None,
-) -> List[InferenceRequest]:
-    async def collect_stream(prompt, request_id, stream_generator):
-        async for output in stream_generator:
-            pass
-
-    if inference_requests is None:
-        assert prompts is not None
-        inference_requests = [None for _ in range(len(prompts))]
-    elif prompts is None:
-        assert inference_requests is not None
-        tokenizer = get_tokenizer()
-        prompts = [tokenizer.detokenize(request.prompt_tokens) for request in inference_requests]
-
-    request_ids: List[int] = [
-        inference_engine.add_request(
-            prompt=prompt,
-            inference_request=inference_request,
-            inference_parameters=sampling_params,
-            streaming=True,
-        )
-        for prompt, inference_request in zip(prompts, inference_requests)
-    ]
-    stream_generators = [
-        inference_engine.get_stream_generator(request_id) for request_id in request_ids
-    ]
-
-    tasks = [
-        asyncio.create_task(collect_stream(prompt, request_id, stream_generator))
-        for (prompt, request_id, stream_generator) in zip(prompts, request_ids, stream_generators)
-    ]
-
-    await inference_engine.run_engine_async()
-    await asyncio.gather(*tasks)
-
-    results: List[InferenceRequest] = [
-        inference_engine.scheduler.completed_request_pool[request_id] for request_id in request_ids
-    ]
-
-    return results
+        return get_dynamic_inference_engine(model=model)
 
 
 def get_random_prompt_tokens(tokenizer, num_input_tokens) -> List[int]:
@@ -232,14 +128,12 @@ def generate_dynamic(
         request_id = REQUEST_ID
         REQUEST_ID += 1
         prompt_tokens = request.prompt_tokens
-        inference_engine.add_request(
-            request_id, prompt_tokens, request.inference_parameters,
-        )
+        inference_engine.add_request(request_id, prompt_tokens, request.inference_parameters)
 
     start_time = time.perf_counter()
     all_finished_requests = []
     while inference_engine.has_unfinished_requests():
-        result = inference_engine.step(verbose=False)
+        result = inference_engine.step()
         finished_requests = result["finished_requests"]
         for request in finished_requests:
             req_id = request.request_id
@@ -257,8 +151,6 @@ def generate_dynamic(
 def main():
     """Main program."""
 
-    # Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file)
-    # Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument)
     initialize_megatron(
         extra_args_provider=add_inference_benchmarking_args,
         args_defaults={
@@ -271,19 +163,9 @@ def main():
 
     args = get_args()
 
-    # Set up model and load checkpoint
-    if args.model_provider == "gpt":
-        model_builder = gpt_builder
-    elif args.model_provider == "mamba":
-        model_builder = mamba_builder
-    else:
-        raise ValueError(f"Invalid model provider {args.model_provider}")
+    model = get_model_for_inference()
 
-    model = get_model(partial(model_provider, model_builder), wrap_with_ddp=False)
-    tokenizer = get_tokenizer()
-    load_checkpoint(model, None, None)
-    model = model[0]
-    model.eval()
+    tokenizer = build_tokenizer(args)
 
     assert (args.prompts is None) ^ (
         args.num_input_tokens is None
@@ -298,13 +180,14 @@ def main():
         return_log_probs=args.return_log_probs,
         top_n_logprobs=args.top_n_logprobs,
         num_tokens_to_generate=args.num_tokens_to_generate,
+        termination_id=-1,
     )
     sampling_params.add_attributes({"no_early_termination": True})
 
     requests = []
     if args.num_input_tokens is not None:
         assert args.prompts is None
-        batch_size = args.inference_max_batch_size
+        batch_size = args.inference_max_requests
         for i in range(batch_size):
             prompt_tokens = get_random_prompt_tokens(tokenizer, args.num_input_tokens)
             requests.append(
@@ -327,33 +210,27 @@ def main():
                 )
             )
 
-    if args.cuda_graph_impl == "local":
-        print(f"Running warmup for CUDA graphs...")
-        warmup_sampling_params = SamplingParams(num_tokens_to_generate=10)
-        warmup_sampling_params.add_attributes({"no_early_termination": True})
+    # TODO(ksanthanam): Use a command line argument for warmup iterations
+    for i in range(3):
+        print(f"Running warmup iteration {i+1}...")
+        warmup_sampling_params = SamplingParams(num_tokens_to_generate=10, termination_id=-1)
         inference_engine.generate(prompts=["warmup"], sampling_params=warmup_sampling_params)
 
     if args.benchmark_profile:
         torch.cuda.cudart().cudaProfilerStart()
 
     start_time = time.perf_counter()
-    if args.stream:
-        if args.engine_type == "dynamic":
-            raise NotImplementedError("Streaming not supported with DynamicInferenceEngine")
-        results: List[InferenceRequest] = asyncio.run(
-            generate(
-                inference_engine, sampling_params, prompts=args.prompts, inference_requests=requests
-            )
+    if args.engine_type == "static":
+        results: List[InferenceRequest] = inference_engine.generate(
+            prompts=args.prompts, inference_requests=requests, sampling_params=sampling_params
         )
     else:
-        if args.engine_type == "static":
-            results: List[InferenceRequest] = inference_engine.generate(
-                prompts=args.prompts, inference_requests=requests, sampling_params=sampling_params
-            )
-        elif args.engine_type == "dynamic":
-            results: List[InferenceRequest] = generate_dynamic(
-                args, requests, inference_engine,
-            )
+        prompts = [request.prompt_tokens for request in requests]
+        records: List[DynamicInferenceRequestRecord] = inference_engine.generate(
+            prompts=prompts, sampling_params=sampling_params
+        )
+        results: List[InferenceRequest] = [record.merge() for record in records]
+
     end_time = time.perf_counter()
     latency = end_time - start_time
 
@@ -378,6 +255,10 @@ def main():
                 result_dict['generated_output'] = tokenizer.detokenize(result.generated_tokens)
             print(result_dict)
 
+    total_output_tokens = args.num_tokens_to_generate * args.inference_max_requests
+    throughput = total_output_tokens / latency
+    print(f"Throughput: {throughput} output tokens / second")
+
 
 if __name__ == "__main__":
     main()
diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py
index 350173dc16f..83a0bbc2369 100644
--- a/tools/run_text_generation_server.py
+++ b/tools/run_text_generation_server.py
@@ -22,9 +22,6 @@
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
     GPTInferenceWrapper,
 )
-from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
-    InferenceWrapperConfig,
-)
 from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.text_generation_controllers.text_generation_controller import (
     TextGenerationController,
@@ -63,27 +60,15 @@ def get_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngi
 
     tokenizer = get_tokenizer()
 
-    inference_wrapper_config = InferenceWrapperConfig(
-        hidden_size=args.hidden_size,
-        inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
-        fp32_residual_connection=args.fp32_residual_connection,
-        params_dtype=args.params_dtype,
-        padded_vocab_size=args.padded_vocab_size,
-        inference_max_seq_length=args.inference_max_seq_length,
-        inference_max_requests=args.inference_max_batch_size,
-        nccl_all_reduce_for_prefill=args.nccl_all_reduce_for_prefill,
-        moe_pad_experts_for_cuda_graph_inference = args.moe_pad_experts_for_cuda_graph_inference
-    )
-    inference_context = StaticInferenceContext.from_config(inference_wrapper_config)
+    inference_context = StaticInferenceContext(args.inference_max_requests, args.inference_max_sequence_length)
     inference_wrapped_model = GPTInferenceWrapper(
-        model, inference_wrapper_config, inference_context
+        model, inference_context
     )
     text_generation_controller = TextGenerationController(
         inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
     )
     return StaticInferenceEngine(
         text_generation_controller=text_generation_controller,
-        max_batch_size=args.inference_max_batch_size,
     )
 
 
@@ -113,7 +98,7 @@ def add_text_generate_args(parser):
         metavar='N',
         type=str,
         nargs='+',
-        help='Input prompts with each prompt within quotes and seperated by space',
+        help='Input prompts with each prompt within quotes and separated by space',
     )
     group.add_argument(
         "--max-batch-size",
@@ -166,14 +151,6 @@ def main(model_type: str = "gpt"):
     model = model[0]
     model.eval()
 
-    if args.max_batch_size is not None:
-        assert args.inference_max_batch_size is not None
-        args.inference_max_batch_size = max(args.inference_max_batch_size, args.max_batch_size)
-        warnings.warn(
-            "`--max-batch-size` has been deprecated in favor of `--inference-max-requests`, "
-            f"setting maximum batch size to {args.inference_max_batch_size}"
-        )
-
     inference_engine = get_inference_engine(args, model)
 
     if args.cuda_graph_impl == "local":
diff --git a/train_rl.py b/train_rl.py
index 299843bcff3..645e78ba986 100644
--- a/train_rl.py
+++ b/train_rl.py
@@ -33,7 +33,6 @@
 
 logging.basicConfig(level=logging.INFO, force=True)
 
-
 def _gpt_builder(args, pre_process, post_process, vp_stage=None, config=None, pg_collection=None):
     # TODO(Peter): This is a hack to get around the fact that we are activation recomputation for training but not
     # for inference with cuda graphs. Without out this the post checks in the transformer config will assert error.
@@ -260,6 +259,7 @@ def forward_step(data_iterator, model: GPTModel, loss_only: bool = False):
     if packed_seq_params is None:
         packed_seq_params = get_default_packed_seq_params(
             seq_length=tokens.shape[1],
+            max_sequences_per_bin=args.rl_sequence_packing_max_sequences_per_bin,
             device=tokens.device,
         )
 
@@ -369,6 +369,8 @@ def __getitem__(self, idx):
 
 if __name__ == "__main__":
 
+    from megatron.inference.utils import add_inference_args
+
     # Temporary for transition to core datasets
     train_valid_test_datasets_provider.is_distributed = True
 
@@ -400,4 +402,5 @@ def _model_builder(
         ModelType.encoder_or_decoder,
         forward_step,
         args_defaults={},
+        extra_args_provider=add_inference_args,
     )
diff --git a/uv.lock b/uv.lock
index 1867e8aaddf..1009b804630 100644
--- a/uv.lock
+++ b/uv.lock
@@ -5,11 +5,19 @@ resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and sys_platform == 'linux'",
     "python_full_version == '3.12.*' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and sys_platform != 'linux'",
-    "python_full_version == '3.13.*' and sys_platform != 'linux'",
-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version < '3.11' and sys_platform == 'linux'",
     "python_full_version < '3.11' and sys_platform != 'linux'",
 ]
@@ -27,11 +35,30 @@ overrides = [
 
 [[package]]
 name = "absl-py"
-version = "2.3.1"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
+]
+
+[[package]]
+name = "accelerate"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
 ]
 
 [[package]]
@@ -50,20 +77,61 @@ wheels = [
 name = "aiobotocore"
 version = "2.26.0"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version == '3.13.*' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'linux'",
+]
 dependencies = [
-    { name = "aiohttp" },
-    { name = "aioitertools" },
-    { name = "botocore" },
-    { name = "jmespath" },
-    { name = "multidict" },
-    { name = "python-dateutil" },
-    { name = "wrapt" },
+    { name = "aiohttp", marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "aioitertools", marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "botocore", version = "1.41.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "jmespath", marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "multidict", marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "python-dateutil", marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
+    { name = "wrapt", version = "1.17.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' or sys_platform != 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/4d/f8/99fa90d9c25b78292899fd4946fce97b6353838b5ecc139ad8ba1436e70c/aiobotocore-2.26.0.tar.gz", hash = "sha256:50567feaf8dfe2b653570b4491f5bc8c6e7fb9622479d66442462c021db4fadc", size = 122026, upload-time = "2025-11-28T07:54:59.956Z" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/58/3bf0b7d474607dc7fd67dd1365c4e0f392c8177eaf4054e5ddee3ebd53b5/aiobotocore-2.26.0-py3-none-any.whl", hash = "sha256:a793db51c07930513b74ea7a95bd79aaa42f545bdb0f011779646eafa216abec", size = 87333, upload-time = "2025-11-28T07:54:58.457Z" },
 ]
 
+[[package]]
+name = "aiobotocore"
+version = "3.1.3"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'linux'",
+]
+dependencies = [
+    { name = "aiohttp", marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "aioitertools", marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "botocore", version = "1.42.49", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "jmespath", marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "multidict", marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "python-dateutil", marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+    { name = "wrapt", version = "2.1.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'linux' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/18/94/332629387f4a9fc691cac9c0cb078af877bfaba415b1a16411377f6ea310/aiobotocore-3.1.3.tar.gz", hash = "sha256:b1b6a95aa4c17410090f4adf16fd45e45a898140c83d4e9d554602f9310408c0", size = 122675, upload-time = "2026-02-14T12:11:01.745Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/14/29/a3e75834009121ebb695dc24f9fe804566b1bcc9b7d46f6fbe56fe972c6a/aiobotocore-3.1.3-py3-none-any.whl", hash = "sha256:3afc93bf14de304dbd4a2c90f36fb3ce6348b06a5a1ec7f87261be628d7876d9", size = 87717, upload-time = "2026-02-14T12:10:59.898Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -193,6 +261,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b4/63/278a98c715ae467624eafe375542d8ba9b4383a016df8fdefe0ae28382a7/aiohttp-3.13.3-cp314-cp314t-win_amd64.whl", hash = "sha256:44531a36aa2264a1860089ffd4dce7baf875ee5a6079d5fb42e261c704ef7344", size = 499694, upload-time = "2026-01-03T17:32:24.546Z" },
 ]
 
+[[package]]
+name = "aiohttp-cors"
+version = "0.8.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+]
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@@ -2244,9 +2770,10 @@ dev = [
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     { name = "onnxscript" },
-    { name = "opentelemetry-api" },
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@@ -2254,7 +2781,8 @@ dev = [
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@@ -2265,13 +2793,14 @@ lts = [
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     { name = "wget" },
 ]
 mlm = [
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     { name = "sentencepiece" },
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@@ -2290,15 +2819,18 @@ build = [
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@@ -2328,11 +2860,13 @@ test = [
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     { name = "tensorboard" },
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     { name = "av", marker = "extra == 'lts'" },
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@@ -2348,7 +2882,9 @@ requires-dist = [
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