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On October 16, 2026, the vLLM blog published a technical retrospective titled "Hyper Optimizing Kimi K3 in vLLM: Moving Fast and Responsibly." According to a TMTPost·AGI report, NVIDIA CentML engineer Benjamin Chislett used a Megakernel written in 12,000 lines of CuTe DSL to optimize inference for Moonshot AI's Kimi K3; the vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x improvement in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog states that agent-optimized inference code is now outpacing human review and maintenance capacity, and the team therefore proposed a fast-track model optimization RFC so that reviewers judge designs from high-level abstractions rather than reviewing code line by line. The report also mentions that NVIDIA is developing optimizations for other models in vLLM, such as DeepSeek V4.1.
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vLLM Blog Publishes Technical Retrospective on Kimi K3 Inference Optimization
Source:TMTPost · AGI
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Oct 30
TMTPost · AGI
NVIDIA uses a 12,000-line Megakernel to optimize Kimi K3 inference, and vLLM says AI code is now faster than human review
Benjamin Chislett, an engineer at NVIDIA CentML, used a 12,000-line Megakernel written in CuTe DSL to optimize Moonshot AI's Kimi K3 inference. The vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x increase in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog says agent-optimized inference code is now outpacing human review and maintenance speed, so the team proposed a fast-track model optimization RFC that lets reviewers judge designs from high-level abstractions rather than reviewing code line by line. The article also mentions that NVIDIA is developing optimizations for other models for vLLM, such as DeepSeek V4.1.