vLLM reports further Rubin performance gains on MiniMax M3.1 model

AIvLLM says its engine optimizations and Rubin-optimized kernels, including MiniMax Sparse Attention and decode-specialized kernels, have pushed performance further since its M3.1 blog post. The gains build on earlier Rubin work covered in the M3.1 and Vera Rubin preview posts. vLLM says it is "just getting started" on further optimizations.

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These gains come from optimizations across the vLLM engine (e.g., eliminating CPU overheads) and Rubin-optimized kernels, including MiniMax Sparse Attention and decode-specialized kernels.

Our M3.1 blog covered the first wave of Rubin optimizations. We’ve pushed performance even further since then. And we’re just getting started! 🚀

Check out our blogs ⬇️

M3.1: https://vllm.ai/blog/2026-10-23-minimax-m31-flash
Rubin: https://vllm.ai/blog/2026-10-09-vera-rubin-preview

Special thanks to @NVIDIAAI and @MiniMax_AI!

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vLLM · x.comPublished Nov 11, 6:25 PM ET · added here 6:35 PM ET

Original title: These gains come from optimizations across the vLLM engine (e.g., eliminating CPU overheads) and Rubin-optimized kernels, including MiniM...

vLLM reports further Rubin performance gains on MiniMax M3.1 model

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