Add model configs: GLM, DeepSeek V4, Inkling, Laguna, Laguna NVFP4 - #39
Add model configs: GLM, DeepSeek V4, Inkling, Laguna, Laguna NVFP4#39rounakbende10 wants to merge 3 commits into
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rounakbende10
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Sep 3, 2026
rounakbende10
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- 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
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WalkthroughThe change adds six model configurations, registers them for lookup, extends ChangesModel serving configuration
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This change adds model configurations and applies platform-specific serving arguments without an identified remaining merge-blocking risk. Sequence Diagram(s)sequenceDiagram
participant start_model
participant InstanceResource
participant vLLM_Docker
start_model->>InstanceResource: Read resource preset and platform
InstanceResource-->>start_model: Return platform and GPU count
start_model->>vLLM_Docker: Start model with platform-specific arguments
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✨ Finishing Touches🧪 Generate unit tests (beta)
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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__.pysrc/coding_agent_bench/models/configs.py
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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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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?
| "--enable-auto-tool-choice", | ||
| "--tokenizer-mode", "deepseek_v4", | ||
| "--tool-call-parser", "deepseek_v4", | ||
| "--reasoning-parser", "deepseek_v4", |
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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?
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--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
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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
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+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
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--attention_config.use_fp4_indexer_cache True worked for both variants added in latest commit also it increased concurrency by +1x on each
| "--enable-auto-tool-choice", | ||
| "--tokenizer-mode", "deepseek_v4", | ||
| "--tool-call-parser", "deepseek_v4", | ||
| "--reasoning-parser", "deepseek_v4", |
| "--kv-cache-dtype", "fp8", | ||
| "--enable-auto-tool-choice", | ||
| "--tool-call-parser", "inkling", | ||
| "--reasoning-parser", "inkling", |
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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?
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tokenizer mode is inkling by default doesn't matter if we mention or not.
--kernel-config.enable_flashinfer_autotune=False can't be used with kv cache dtype as fp8 we are 160MB short of memory to run on BF16 kv cache dtype for 8Xh200
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| class poolside_Laguna_S_2_1_NVFP4(ModelConfig): | ||
| # Verified: 1x B200 183GB, NVFP4, max-model-len 1048576, concurrency 2.33x |
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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
Adds a hardware_extra_args field to ModelConfig so per-platform vLLM flags (e.g. B200-only kernels) can be layered on top of the base args without duplicating whole configs. NebiusInstanceManager.start_model now reads the instance platform and injects the matching extra args. Applies this to DeepSeek V4 Flash (FP8 and NVFP4) for B200-specific MoE backend and indexer cache flags, with concurrency numbers verified on real 8x B200 hardware: - DeepSeek V4 Flash (FP8): 14.02x at 1M (mega_moe + fp4_indexer_cache) - DeepSeek V4 Flash NVFP4: 13.60x at 1M (native FP4 + fp4_indexer_cache) Also documents two Inkling-Small findings from testing: --tokenizer-mode inkling is a no-op, and --kernel-config.enable_flashinfer_autotune=False breaks fp8 KV cache.
Some Nebius regions/tenants expose B200 under a different platform name suffix; match both so the extra args apply regardless of which variant the instance reports.
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