vLLM Delivers Day-0 Support for MiniMax-M3.1-Flash

MiniMax-M3.1-Flash is the latest model in the MiniMax-M3 family, with improvements across reasoning, coding, agentic, and visual tasks. It is also built for inference efficiency, with a native NVFP4 KV cache, NVFP4-quantized MoE weights, and a DSpark draft model for speculative decoding. vLLM supports MiniMax-M3.1-Flash on day 0 on NVIDIA Blackwell GPUs, on NVIDIA Rubin GPUs with dedicated Rubin kernels, and on AMD MI350-series GPUs, with strong performance on agentic workloads. TL;DR MiniMax Sparse Attention (MSA) with an NVFP4 KV cache for both the indexer and the attention path, plus NVFP4 MoE weights and a DSpark drafter. Blackwell MSA kernels use the new CUDA 13.4 QMUL4 instruction to dequantize NVFP4 into FP8 inside the kernel: up to 1.7× faster indexer QK decode and 1.3× faster sparse attention decode. NVFP4 MegaMoE with MXFP8 shared-expert fusion and early TMEM release: 20% lower kernel latency in prefill and 7% higher prefill throughput. Indexer decode context parallelism (DCP): up to 3.3× faster indexer per sparse layer at 512K context on 4× GB300. QKV GEMM + norm + RoPE + KV-insert fusion: 1.1–1.6× faster prefill at 512+ tokens and about 19% lower P50 TTFT at TP4. FlashInfer PrimiTS backend for the DSpark drafter attention: 1.3–2× faster than FlashAttention 4, 5% lower total forward time. Rubin (SM107) MSA kernels are also integrated into vLLM with state-of-the-art performance. AMD MI350 series: the NVFP4-native MSA kernel, indexer decode context parallelism, dual-batch overlap, and the mono kernel deliver a 2–3× performance boost across the board. Agentic Serving: on NVIDIA GB300, up to 418.9 tok/s per user at the latency-sensitive end; on NVIDIA Rubin, up to 724.9 tok/s per user and 191.5K total tok/s per GPU; on AMD MI355X, up to 133.3M total tokens per dollar of TCO on the SemiAnalysis AgentX benchmark. Day-0 RL support: end-to-end RL with veRL, FSDP2, and LoRA on the NVFP4 checkpoint. Quick start Prebuilt images are available for both vendors: NVIDIA: vllm/vllm-openai:minimax-m31-flash AMD: vllm/vllm-openai-rocm:minimax-m31-flash Note that the model will only be available in the nightly PyPI wheel after the code is reviewed and merged upstream, which usually takes about a week. Full recipes are at vLLM Recipes, and the checkpoint is at MiniMaxAI/MiniMax-M3.1-Flash. TP4 on NVIDIA We recommend the CUDA 13.4 toolkit for the best performance. vllm serve MiniMaxAI/MiniMax-M3.1-Flash
-tp 4
--reasoning-parser minimax_m3
--block-size 128 …