@Snixtp: The RTX Pro 6000 has two matmul engines, and vLLM was only using one of them when I ran Qwen3.6 27B NVFP4 Marlin is nic…

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Summary

A developer discovered that vLLM only used one of two matmul engines on the RTX Pro 6000 for Qwen3.6 27B models. A plugin by Fable 5 selects the right engine per call, nearly doubling prefill performance.

The RTX Pro 6000 has two matmul engines, and vLLM was only using one of them when I ran Qwen3.6 27B NVFP4 Marlin is nice for decoding, but CUTLASS is 3.5x faster processing large token prompts. For some reason, vLLM ran Marlin for both, because NVIDIA's checkpoint files mislabel what the model can actually do. Fable 5 found out about all this and wrote a vLLM plugin that looks at the batch size and picks the right engine per call. Results is almost 2x increase in prefill at all context lengths:
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Cached at: 07/05/26, 12:55 AM

The RTX Pro 6000 has two matmul engines, and vLLM was only using one of them when I ran Qwen3.6 27B NVFP4

Marlin is nice for decoding, but CUTLASS is 3.5x faster processing large token prompts. For some reason, vLLM ran Marlin for both, because NVIDIA’s checkpoint files mislabel what the model can actually do.

Fable 5 found out about all this and wrote a vLLM plugin that looks at the batch size and picks the right engine per call.

Results is almost 2x increase in prefill at all context lengths:

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@Snixtp: https://x.com/Snixtp/status/2055734339346768225

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A user benchmarks the MTP variant of Qwen3.6 27B against the normal version on a single RTX 3090 using llama.cpp, finding MTP offers up to 2.37x faster generation at long contexts (32k-64k) but with slower prefill and no concurrency support yet.