[Feature] Enable DeepGEMM Linear on B200; 1.5% E2E throughput improvement

[yewentao256]

Purpose

Enable DeepGEMM Linear on B200

This should also fix some cutlass linear acc error since the weight is quantized to e8m0

Test

VLLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=Qwen/Qwen3-30B-A3B-FP8,max_model_len=32768,enforce_eager=True"   --trust_remote_code   --tasks gsm8k   --num_fewshot 5   --batch_size auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8529|±  |0.0098|
|     |       |strict-match    |     5|exact_match|↑  |0.8855|±  |0.0088|

# main
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8590|±  |0.0096|
|     |       |strict-match    |     5|exact_match|↑  |0.8969|±  |0.0084|

VLLM_USE_DEEP_GEMM=1 vllm bench throughput --model Qwen/Qwen3-30B-A3B-FP8 --load-format dummy --input-len 1000 --output-len 100 --trust_remote_code --enable-expert-parallel
Throughput: 40.59 requests/s, 44557.64 total tokens/s, 4059.10 output tokens/s
# main
Throughput: 39.97 requests/s, 43880.62 total tokens/s, 3997.42 output tokens/s

[gemini-code-assist]

Code Review

This pull request enables DeepGEMM for FP8 linear layers on B200 GPUs by updating the device capability check. The changes also include refactoring the DeepGEMM eligibility check into the vllm.utils.deep_gemm module for better code organization. The implementation looks good, but I've found a small redundancy in the new check function that can be improved for clarity and maintainability.

[github-actions]

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[yewentao256]

@mgoin CC

[mgoin]

LGTM. Should we also take the opportunity to refactor the logic in fp8.py? It seems there is no logic about deepgemm in Fp8LinearMethod and Fp8MoEMethod has this possibly different local logic

        # Check for DeepGemm support.
        self.allow_deep_gemm = False
        if envs.VLLM_USE_DEEP_GEMM:
            if not has_deep_gemm():
                logger.warning_once("Failed to import DeepGemm kernels.")
            elif not self.block_quant:
                logger.warning_once("Model is not block quantized. Not using "
                                    "DeepGemm kernels")
            elif (is_deep_gemm_supported()):
                logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.")
                self.allow_deep_gemm = True
            else:
                logger.warning_once(
                    "DeepGemm not supported on the current platform.")

[yewentao256]

LGTM. Should we also take the opportunity to refactor the logic in fp8.py? It seems there is no logic about deepgemm in Fp8LinearMethod and Fp8MoEMethod has this possibly different local logic

        # Check for DeepGemm support.
        self.allow_deep_gemm = False
        if envs.VLLM_USE_DEEP_GEMM:
            if not has_deep_gemm():
                logger.warning_once("Failed to import DeepGemm kernels.")
            elif not self.block_quant:
                logger.warning_once("Model is not block quantized. Not using "
                                    "DeepGemm kernels")
            elif (is_deep_gemm_supported()):
                logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.")
                self.allow_deep_gemm = True
            else:
                logger.warning_once(
                    "DeepGemm not supported on the current platform.")

Sounds good, I record this done and will have a separate PR for this.