vLLM Delivers Day-0 Support for MiniMax-M3.1-Flash: NVFP4 Kernels Boost Blackwell Inference by Up to 1.7x

Open-source inference engine vLLM announced Day-0 support for the latest MiniMax model, MiniMax-M3.1-Flash, on October 23, 2026. The support covers NVIDIA Blackwell, NVIDIA Rubin, and AMD MI350-series GPUs, with specific optimizations targeting agentic workloads.

MiniMax-M3.1-Flash features a native NVFP4 KV cache, NVFP4-quantized MoE weights, and a DSpark draft model for speculative decoding. vLLM developed specialized kernels for MiniMax Sparse Attention (MSA). On NVIDIA Blackwell GPUs, leveraging the new QMUL4 instruction in CUDA 13.4, the kernel dequantizes NVFP4 to FP8 directly within the chip, resulting in up to a 1.7x speedup in indexer QK decode and a 1.3x speedup in sparse attention decode.

Beyond attention mechanisms, vLLM optimized the MoE computation path. By fusing MXFP8 shared experts into the NVFP4 MegaMoE kernel and releasing TMEM resources early during the prefill phase, kernel latency was reduced by 20%, and overall prefill throughput increased by 7%. For long-context scenarios, Indexer Decode Context Parallelism (DCP) enables up to a 3.3x speedup per sparse layer at 512K context on 4x GB300 GPUs.

Performance benchmarks indicate that on NVIDIA Rubin GPUs, single-user throughput reaches up to 724.9 tokens/s, with total throughput per GPU hitting 191.5K tokens/s. On AMD MI355X, based on the SemiAnalysis AgentX benchmark, the system processes up to 133.3M tokens per dollar of TCO. These optimizations allow MiniMax-M3.1-Flash to accommodate approximately 2.7x more concurrent sequences than the previous FP8 version on identical hardware (e.g., 4x NVIDIA B300 GPUs), directly enhancing serving density and economics.

Prebuilt Docker images for both NVIDIA and AMD are currently available. Note that while model weights are released, full PyPI package support awaits upstream code merging, which is expected to take about one week.