Add model configs: GLM, DeepSeek V4, Inkling, Laguna, Laguna NVFP4 by rounakbende10 · Pull Request #39 · redhat-et/coding_agent_bench

rounakbende10 · 2026-09-03T15:28:39Z

GLM 5.2 FP8: 262K context, v0.24.0
DeepSeek V4 Flash: 1M context
DeepSeek V4 Flash NVFP4: 1M context, native FP4 on B200
Inkling Small BF16: 1M context
Laguna S 2.1 BF16: 1M context, 30.61x concurrency on 8x H200
Laguna S 2.1 NVFP4: 1M context, 2.33x concurrency on 1x B200

- GLM 5.2 FP8: 262K context, v0.24.0 - DeepSeek V4 Flash: 1M context - DeepSeek V4 Flash NVFP4: 1M context, native FP4 on B200 - Inkling Small BF16: 1M context - Laguna S 2.1 BF16: 1M context, 30.61x concurrency on 8x H200 - Laguna S 2.1 NVFP4: 1M context, 2.33x concurrency on 1x B200 - Qwen 3.6 27B: updated max-model-len 131K to 262K

coderabbitai · 2026-09-03T15:28:58Z

📝 Summary

Summary by CodeRabbit

New Features
- Added support for six additional AI model configurations, including GLM, DeepSeek, Inkling, and Laguna variants.
- Added configuration for quantized and specialized runtime variants with appropriate context limits and tool-calling support.
- Registered the new models for use in benchmarking.

Walkthrough

Changes

Model configuration registry

Layer / File(s)	Summary
Add model serving configurations `src/coding_agent_bench/models/configs.py`	Added GLM, DeepSeek, Inkling, and Laguna configurations with model limits, container images, parsers, quantization, expert parallelism, and chat-template settings.
Register model configurations `src/coding_agent_bench/models/__init__.py`	Imported the new configuration classes and added them to `MODEL_CONFIGS` for lookup.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: 🟡 Moderate · up to fb4fc

The added models are registered, but Qwen still cannot serve the promised 262K context, and the new remote-code model configurations can change upstream without review. Correct the context limit and pin immutable model revisions before merging.

Suggested reviewers: taagarwa-rh

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 2…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly and concisely identifies the added GLM, DeepSeek V4, Inkling, and Laguna model configurations.
Description check	✅ Passed	The description accurately summarizes the model configurations, context lengths, hardware, and performance details in the changeset.

Full details: Docstring Coverage

Explanation

No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 2 files.

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Actionable comments posted: 2

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Inline comments:
In `@src/coding_agent_bench/models/configs.py`:
- Line 81: Update the Qwen model configuration values represented by
model_max_len and the corresponding setting at the additional location from
131072 to 262144, ensuring NebiusManager.start_model receives the increased
context limit.
- Line 105: Pin the mutable model identifiers to immutable, reviewed revisions
in each configuration using --trust-remote-code. Apply this to
src/coding_agent_bench/models/configs.py at lines 105-105, 124-124, 145-145,
165-165, 181-181, and 197-197; update each affected model reference while
preserving the existing launcher behavior.

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src/coding_agent_bench/models/__init__.py
src/coding_agent_bench/models/configs.py

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taagarwa-rh · 2026-09-03T17:06:04Z

+
+
+class RedHatAI_GLM_5_2_FP8(ModelConfig):
+    # Verified: 8x H200 141GB, concurrency 2.23x at 262K context


I don't think this is enough concurrency for us to consider using vLLM over OpenRouter for GLM 5.2. Let's keep it in the PR though for informational purposes. How does it do on 8x B200?

taagarwa-rh · 2026-09-03T17:07:19Z

+        "--enable-auto-tool-choice",
+        "--tokenizer-mode", "deepseek_v4",
+        "--tool-call-parser", "deepseek_v4",
+        "--reasoning-parser", "deepseek_v4",


https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Flash mentions a couple flags I don't see here:

--attention_config.use_fp4_indexer_cache True \ --moe-backend deep_gemm_mega_mo

Did you test these and find they are not needed?

--moe-backend deep_gemm_mega_moe is B200-only (SM100) crashes on H200 (SM90). We tested on H200 initially, so excluded it. On B200 we did test with mega_moe and got 12.81x vs 9.91x on H200 without it. We can add it as a comment noting it's B200-only, or add a separate B200 config with it enabled. what do you think?

--attention_config.use_fp4_indexer_cache True is used only for NVFP4 variant. I will confirm once again with this flag if theres any change in concurrency

Oh that is something I hadn't considered. Should we add a per-hardware args section to the ModelConfigs? E.g.

from coding_agent_bench.nebius_utils import B200, B200x8 class RedHatAI_DeepSeek_V4_Flash(ModelConfig): ... hardware_extra_args: dict[str, list[str]] = { B200.name: ["--moe-backend", "deep_gemm_mega_moe"], B200x8.name: ["--moe-backend", "deep_gemm_mega_moe"], } ...

Then somewhere when building the vLLM command it can reference

args += model_config.hardware_extra_args.get(hardware.name, [])

Regarding --attention_config.use_fp4_indexer_cache True, ~~I'm seeing that listed on the FP8 model as well, so I think it's needed for both~~ oh that's only on the B200 as well. That would be another one to add to the B200 specific args

+1 on the hardware_extra_args approach. start_model already extracts the GPU preset from the instance so we can match on that. I'll implement it. DeepSeek gets --moe-backend deep_gemm_mega_moe on B200s and --attention_config.use_fp4_indexer_cache True

taagarwa-rh · 2026-09-03T17:07:30Z

+        "--enable-auto-tool-choice",
+        "--tokenizer-mode", "deepseek_v4",
+        "--tool-call-parser", "deepseek_v4",
+        "--reasoning-parser", "deepseek_v4",


See comment above

taagarwa-rh · 2026-09-03T17:10:44Z

+        "--kv-cache-dtype", "fp8",
+        "--enable-auto-tool-choice",
+        "--tool-call-parser", "inkling",
+        "--reasoning-parser", "inkling",


https://recipes.vllm.ai/thinkingmachines/Inkling-Small?variant=bf16 has a couple flags I don't see here:

--tokenizer-mode inkling \ --kernel-config.enable_flashinfer_autotune=False \

Did you test these and find they are not needed?

taagarwa-rh · 2026-09-03T17:12:45Z

+    ]
+
+class poolside_Laguna_S_2_1_NVFP4(ModelConfig):
+    # Verified: 1x B200 183GB, NVFP4, max-model-len 1048576, concurrency 2.33x


Thank you for testing this, same as above looks like this won't be enough concurrency so we'll prefer the BF16 version you added. No changes needed here. Thanks again for trying this, this will serve as justification for using the larger node to run this model

coderabbitai Bot reviewed Sep 3, 2026

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Comment thread src/coding_agent_bench/models/configs.py

Comment thread src/coding_agent_bench/models/configs.py

taagarwa-rh reviewed Sep 3, 2026

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Add model configs: GLM, DeepSeek V4, Inkling, Laguna, Laguna NVFP4 - #39

Add model configs: GLM, DeepSeek V4, Inkling, Laguna, Laguna NVFP4#39
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		class RedHatAI_GLM_5_2_FP8(ModelConfig):
		# Verified: 8x H200 141GB, concurrency 2.23x at 262K context

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