diff --git a/docs.json b/docs.json
index 49168dd..a04b43d 100644
--- a/docs.json
+++ b/docs.json
@@ -229,7 +229,8 @@
               "quantized-gguf-models-cloned",
               "vllm-llm-inference-and-serving",
               "examples/text-generation/minimax-m2",
-              "examples/text-generation/glm-47-flash"
+              "examples/text-generation/glm-47-flash",
+              "examples/text-generation/nemotron-3-super"
             ]
           },
           {
diff --git a/examples/text-generation/nemotron-3-super.mdx b/examples/text-generation/nemotron-3-super.mdx
new file mode 100644
index 0000000..7336959
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@@ -0,0 +1,321 @@
+---
+title: NVIDIA Nemotron 3 Super
+slug: nemotron-3-super-deployment-vast
+createdAt: Wed Mar 12 2026 20:00:00 GMT+0000 (Coordinated Universal Time)
+updatedAt: Wed Mar 12 2026 20:00:00 GMT+0000 (Coordinated Universal Time)
+---
+
+<script type="application/ld+json" dangerouslySetInnerHTML={{
+  __html: JSON.stringify({
+    "@context": "https://schema.org",
+    "@type": "HowTo",
+    "name": "Deploy NVIDIA Nemotron 3 Super on Vast.ai",
+    "description": "Learn how to deploy the NVIDIA Nemotron 3 Super 120B hybrid Mamba-Transformer-MoE model on Vast.ai using SGLang with toggleable reasoning.",
+    "step": [
+      {
+        "@type": "HowToStep",
+        "name": "Set up your Vast.ai account",
+        "text": "Create a Vast.ai account and configure your API key using the CLI."
+      },
+      {
+        "@type": "HowToStep",
+        "name": "Find a suitable instance",
+        "text": "Search for 2x H100 SXM instances with 80GB VRAM, CUDA 12.4+, and 150GB+ disk space."
+      },
+      {
+        "@type": "HowToStep",
+        "name": "Deploy with SGLang",
+        "text": "Launch an instance with the lmsysorg/sglang:v0.5.9 Docker image and the FP8 model checkpoint."
+      },
+      {
+        "@type": "HowToStep",
+        "name": "Wait for model loading",
+        "text": "Monitor logs until the server reports ready."
+      },
+      {
+        "@type": "HowToStep",
+        "name": "Query the API",
+        "text": "Send requests to the OpenAI-compatible API endpoint with reasoning mode control via chat_template_kwargs."
+      }
+    ],
+    "author": {
+      "@type": "Organization",
+      "name": "Vast.ai Team"
+    },
+    "datePublished": "2026-03-12",
+    "dateModified": "2026-03-12"
+  })
+}} />
+
+# Running NVIDIA Nemotron 3 Super on Vast.ai
+
+NVIDIA Nemotron 3 Super is a 120B parameter model that only activates 12B parameters per token, which means you get the quality of a much larger model at a fraction of the compute. It uses a novel hybrid architecture — Mamba-2 for fast sequence processing, Transformer attention where precision matters, and a Latent Mixture-of-Experts layer for efficient routing — and supports context windows up to 1M tokens.
+
+The model is particularly interesting because it ships with a built-in reasoning toggle. You can turn reasoning on for complex tasks like math and coding, switch to a low-effort mode for lighter thinking, or turn it off entirely for fast direct answers — all from the same deployment, controlled per request. It also supports Multi-Token Prediction for faster inference through speculative decoding, and performs well on agentic benchmarks involving tool use and multi-step task execution.
+
+This guide deploys the FP8 variant on Vast.ai using SGLang and queries it via the OpenAI-compatible API.
+
+## Prerequisites
+
+Before getting started, you'll need:
+
+- A Vast.ai account with credits ([Sign up here](https://cloud.vast.ai))
+- Vast.ai CLI installed (`pip install vastai`)
+- Your Vast.ai API key configured
+- Python 3.8+ (for the OpenAI SDK examples)
+
+<Note>
+  Get your API key from the [Vast.ai account page](https://cloud.vast.ai/account/) and set it with `vastai set api-key YOUR_API_KEY`.
+</Note>
+
+## Understanding Nemotron 3 Super
+
+Key capabilities:
+
+- **Efficient MoE Architecture**: 120B total parameters, only 12B active per token
+- **Hybrid Layers**: Mamba-2 (linear-time) + Transformer attention + Latent MoE
+- **Reasoning Toggle**: On, off, or low-effort modes via `chat_template_kwargs`
+- **Long Context**: Up to 1M tokens (256K default)
+- **Commercial License**: NVIDIA Nemotron Open Model License
+
+## Hardware Requirements
+
+The FP8 variant requires:
+
+- **GPUs**: 2× H100 SXM (80GB each) with NVLink for tensor parallelism
+- **Disk Space**: 200GB minimum (model is ~120GB)
+- **CUDA Version**: 12.4 or higher
+- **Docker Image**: `lmsysorg/sglang:v0.5.9`
+
+<Note>
+  H100 SXM GPUs are required (not PCIe) because NVLink is needed for efficient tensor parallelism across 2 GPUs.
+</Note>
+
+## Instance Configuration
+
+### Step 1: Search for Suitable Instances
+
+```bash Bash
+vastai search offers \
+  "gpu_name=H100_SXM num_gpus=2 gpu_ram>=80 cuda_vers>=12.4 \
+   disk_space>=200 direct_port_count>1 inet_down>=500 rentable=true" \
+  --order "dph_base" --limit 10
+```
+
+This searches for:
+- 2× H100 SXM GPUs with at least 80GB VRAM each
+- CUDA 12.4 or higher
+- At least 200GB disk space
+- Direct port access for the API endpoint
+- High download speed for faster model loading
+- Sorted by price (lowest first)
+
+### Step 2: Create the Instance
+
+Select an instance ID from the search results and deploy:
+
+```bash Bash
+vastai create instance <INSTANCE_ID> \
+  --image lmsysorg/sglang:v0.5.9 \
+  --env '-p 5000:5000' \
+  --disk 200 \
+  --onstart-cmd "python3 -m sglang.launch_server \
+    --model-path nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 \
+    --served-model-name nvidia/nemotron-3-super \
+    --host 0.0.0.0 \
+    --port 5000 \
+    --trust-remote-code \
+    --tp 2 \
+    --kv-cache-dtype fp8_e4m3 \
+    --reasoning-parser nano_v3"
+```
+
+**Key parameters explained**:
+- `--image lmsysorg/sglang:v0.5.9` — SGLang stable release with Nemotron 3 Super support
+- `--env '-p 5000:5000'` — Expose port 5000 for the API endpoint
+- `--disk 200` — 200GB for the ~120GB model weights plus overhead
+- `--tp 2` — Tensor parallelism across both H100 GPUs
+- `--kv-cache-dtype fp8_e4m3` — FP8 KV cache for efficient memory usage
+- `--reasoning-parser nano_v3` — Enables reasoning content parsing for thinking mode
+- `--trust-remote-code` — Required for the custom Nemotron architecture
+
+## Monitoring Deployment
+
+### Check Deployment Status
+
+```bash Bash
+vastai logs <INSTANCE_ID>
+```
+
+Look for this message indicating the server is ready:
+
+```text Text
+The server is fired up and ready to roll!
+```
+
+### Get Your Endpoint
+
+Once deployment completes, get your instance details:
+
+```bash Bash
+vastai show instance <INSTANCE_ID> --raw
+```
+
+Look for the `ports` field — it maps internal port 5000 to an external port. Your API endpoint will be:
+
+```text Text
+http://<PUBLIC_IP>:<EXTERNAL_PORT>/v1
+```
+
+## Using the Nemotron 3 Super API
+
+### Quick Test with cURL
+
+Verify the server is responding:
+
+```bash Bash
+curl -X POST http://<IP>:<PORT>/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "nvidia/nemotron-3-super",
+    "messages": [{"role": "user", "content": "What is 25 * 37?"}],
+    "max_tokens": 500,
+    "temperature": 1.0,
+    "top_p": 0.95
+  }'
+```
+
+<Note>
+  NVIDIA requires `temperature=1.0` and `top_p=0.95` for all inference with this model.
+</Note>
+
+### Python Integration
+
+Using the OpenAI Python SDK:
+
+```python icon="python" Python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://<IP>:<PORT>/v1",
+    api_key="EMPTY"  # SGLang doesn't require an API key
+)
+
+response = client.chat.completions.create(
+    model="nvidia/nemotron-3-super",
+    messages=[{"role": "user", "content": "Explain quantum entanglement in 2 sentences."}],
+    max_tokens=300,
+    temperature=1.0,
+    top_p=0.95
+)
+
+print(response.choices[0].message.content)
+```
+
+## Reasoning Modes
+
+Nemotron 3 Super supports three reasoning modes, controlled via `chat_template_kwargs`. By default, reasoning is enabled.
+
+### Reasoning ON (Default)
+
+The model shows its thinking in `reasoning_content` before giving the final answer in `content`:
+
+```python icon="python" Python
+response = client.chat.completions.create(
+    model="nvidia/nemotron-3-super",
+    messages=[{"role": "user", "content": "Explain quantum entanglement in 2 sentences."}],
+    max_tokens=300,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": True}}
+)
+
+msg = response.choices[0].message
+print("Thinking:", msg.reasoning_content)
+print("Answer:", msg.content)
+```
+
+### Reasoning OFF
+
+Disable reasoning for faster, direct responses:
+
+```python icon="python" Python
+response = client.chat.completions.create(
+    model="nvidia/nemotron-3-super",
+    messages=[{"role": "user", "content": "Explain quantum entanglement in 2 sentences."}],
+    max_tokens=300,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": False}}
+)
+
+msg = response.choices[0].message
+# With reasoning OFF, the answer is in reasoning_content
+print("Answer:", msg.reasoning_content)
+```
+
+<Note>
+  When reasoning is disabled via SGLang's `nano_v3` parser, the response text is returned in `reasoning_content` instead of `content` (which will be `None`). Make sure to read from the correct field based on the mode you're using.
+</Note>
+
+### Low-Effort Reasoning
+
+A middle ground — brief reasoning with fast responses:
+
+```python icon="python" Python
+response = client.chat.completions.create(
+    model="nvidia/nemotron-3-super",
+    messages=[{"role": "user", "content": "What is 25 * 37?"}],
+    max_tokens=300,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": True, "low_effort": True}}
+)
+
+msg = response.choices[0].message
+print("Thinking:", msg.reasoning_content)  # Brief reasoning
+print("Answer:", msg.content)
+```
+
+### Reasoning with cURL
+
+Pass `chat_template_kwargs` at the top level of the JSON body:
+
+```bash Bash
+curl -X POST http://<IP>:<PORT>/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "nvidia/nemotron-3-super",
+    "messages": [{"role": "user", "content": "What is 25 * 37?"}],
+    "max_tokens": 500,
+    "temperature": 1.0,
+    "top_p": 0.95,
+    "chat_template_kwargs": {"enable_thinking": false}
+  }'
+```
+
+## Cleanup
+
+When you're done, destroy the instance to stop billing:
+
+```bash Bash
+vastai destroy instance <INSTANCE_ID>
+```
+
+<Note>
+  Always destroy your instance when you're finished to avoid unnecessary charges.
+</Note>
+
+## Additional Resources
+
+- [NVIDIA Nemotron 3 Super Blog Post](https://developer.nvidia.com/blog/introducing-nemotron-3-super-an-open-hybrid-mamba-transformer-moe-for-agentic-reasoning/) — Architecture details and benchmarks
+- [HuggingFace Model Card (FP8)](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8) — Model card and usage instructions
+- [SGLang Documentation](https://docs.sglang.ai/) — SGLang configuration and usage
+- [Vast.ai CLI Guide](/cli/get-started) — Learn more about the Vast.ai CLI
+- [GPU Instance Guide](/documentation/instances/overview) — Understanding Vast.ai instances
+
+## Conclusion
+
+Nemotron 3 Super delivers frontier-class reasoning performance by activating only 12B of its 120B parameters per token. With SGLang and Vast.ai, you can deploy the model on 2× H100 GPUs and start querying it via the OpenAI-compatible API.
+
+The reasoning toggle is particularly useful: enable it for complex tasks like math, coding, and analysis, or disable it for fast direct answers in production pipelines.