Document ModelOpt W4A16 NVFP4 Marlin path

[MerkyorLynn]

Summary

Document the Marlin path for ModelOpt W4A16 NVFP4 MoE checkpoints, and make
the Marlin NVFP4 log message accurate for weight-only NVFP4.

Why

ModelOpt mixed-precision MoE checkpoints can mark expert layers as
W4A16_NVFP4. This is a weight-only NVFP4 format: weights are 4-bit NVFP4 but
activations stay fp16/bf16. On this path Marlin is the correct backend; the
current warning implies Marlin is only used because the GPU lacks native FP4,
which can be misleading on GPUs with native FP4 support.

The docs now list W4A16_NVFP4 and MIXED_PRECISION, and include an explicit
Marlin serve example with --linear-backend marlin and --moe-backend marlin
for reproducible debugging and benchmarking.

Duplicate-work check

Searched open PRs for ModelOpt W4A16 NVFP4 Marlin. Related but not duplicate:

• b12x nvfp4 w4a16 use a16 fix #43929, [B12x][NVFP4] Add W4A16 support to FlashInfer SM12x MoE wrapper #43341, [B12x] W4A16 NVFP4 support + Nemotron-3.5 / Qwen3.5 fixes #43333, [Quantization][ModelOpt] W4A16 NVFP4 fused MoE + --override-activation-dtype flag #42428, [Quantization] Add ModelOpt FP8/NVFP4 weight-only embedding methods #42791, Pad FP4 Marlin weights to valid thread tiles #43806, and [Quant][Triton] Support modelopt_mixed on SM80/SM86 (Ampere) #42610 cover W4A16 /
  NVFP4 implementation details, backend support, or related quantization code.

This PR does not add a new W4A16 backend. It is limited to documentation and an
accuracy fix for the Marlin NVFP4 warning message.

Validation

• uv run --no-sync --python 3.12 python -m py_compile vllm/model_executor/kernels/linear/nvfp4/marlin.py
• git diff --check

No GPU validation was run for this docs/log-message patch.

AI assistance was used to prepare this patch.

[claude]

Claude Code Review

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[mergify]

Documentation preview: https://vllm--44672.org.readthedocs.build/en/44672/

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Avoid globally pinning Marlin for mixed FP8 layers

In the MIXED_PRECISION case described above where some non-MoE LinearBase layers are FP8, this global flag also forces those FP8 layers through MarlinFP8ScaledMMLinearKernel; choose_scaled_mm_linear_kernel filters by _get_linear_backend(), and that Marlin FP8 kernel rejects compute capability >= 89 unless VLLM_TEST_FORCE_FP8_MARLIN is set, so the documented SM120 command can fail at startup before reaching the W4A16 MoE path. Prefer only pinning --moe-backend marlin for these checkpoints, or document the extra env/caveat when FP8 linear layers are present.

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[MerkyorLynn]

Hi maintainers, this is ready for review. Could you please add verified or ready if the scope looks appropriate? Thanks!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @MerkyorLynn.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[FreeMark]

Confirming this on hardware, with a detail I think the discussion is missing: on SM121 the warning's literal claim is false, and vLLM's own source says so a few lines earlier in the same code path.

The message contradicts the code that selects the kernel

ModelOptNvFp4W4A16LinearMethod.__init__ in vllm/model_executor/layers/quantization/modelopt.py pins Marlin, and the comment explains why:

# Direct-instantiate the Marlin NVFP4 adapter rather than going through
# init_nvfp4_linear_kernel(): the latter's priority list returns a
# cutlass W4A4 kernel as first-pick on this hardware, which would
# silently try to quantize activations (we have no input_scale). For
# W4A16 there is exactly one valid kernel, so we pin it.
self.kernel = MarlinNvFp4LinearKernel(NvFp4LinearLayerConfig())

"returns a cutlass W4A4 kernel as first-pick on this hardware" — the author is explicit that the hardware supports cutlass FP4, and that the pin is about the scheme having no input_scale. MarlinNvFp4LinearKernel.process_weights_after_loading then immediately emits:

Your GPU does not have native support for FP4 computation but FP4 quantization is being used.

The other route into that kernel selects it for the same kind of reason (vllm/model_executor/kernels/linear/__init__.py):

elif linear_backend == "auto" and use_a16:
    # Force a16 (Marlin) when running weight-only quantization.
    force_kernel = MarlinNvFp4LinearKernel

The warning lives on the kernel class, which cannot know why it was chosen. So it is correct when Marlin is a genuine capability fallback on older hardware, and wrong every time it is pinned for W4A16 — which, per the class docstring ("NVFP4 weight-only GEMM via Marlin (W4A16)"), is what that class is primarily for.

Measured on SM121

NVIDIA DGX Spark (GB10, sm_121), vllm/vllm-openai:v0.26.0:

>>> current_platform.get_device_capability()
DeviceCapability(major=12, minor=1)    # -> 121
>>> cutlass_fp4_supported()
True
>>> is_fp4_marlin_supported()
True

The GPU does have native FP4 support, and the running server says otherwise.

It causes real misdiagnosis

A concrete instance, which is the part I would offer as the argument for merging. Serving a W4A16_NVFP4 ModelOpt checkpoint (a Qwen3.8-27B / qwen3_5 hybrid) I measured ~18.5 tok/s single-stream decode, read this warning, and concluded the GB10 lacked FP4 hardware. It does not. The real cause is that W4A16 keeps activations at 16 bits, so the FP4 tensor-core MMA cannot be used and Marlin dequantises — a property of the checkpoint, and one that can only be changed by re-quantizing with activation scales.

For scale: an NVFP4 (W4A4) checkpoint of the same architecture on the same box measures 34.90 tok/s and logs no such warning. To be honest about that comparison — those are two different finetunes of the same base rather than a controlled A/B, and the W4A16 build also carries +9.7% weight bytes (33.6% BF16 vs 23.3%), which accounts for roughly a tenth of the gap. But the direction and rough magnitude hold.

The two checkpoints differ by exactly one tensor family: 401 input_scale tensors present in the W4A4 build, zero in the W4A16 one. weight, weight_scale and weight_scale_2 are identical in layout. That is a compact way to state what the corrected message should convey — Marlin is selected because the checkpoint has no activation scales, not because the GPU is incapable.

On the --linear-backend marlin review concern

That flag may not be needed in the doc example at all. In the run above nothing was passed, and modelopt.py pinned the Marlin kernel by itself for the W4A16 checkpoint, so the non-MoE-FP8 failure mode raised in review does not arise. Dropping --linear-backend marlin from the serve example (keeping --moe-backend marlin where MoE needs it) may be enough to unblock this.

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Disclosure: the source analysis and the measurements above were done with Claude Code. The numbers are from real runs on the configuration named above and are reproducible independently of that.