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vLLM and Inferact push vLLM performance on Google TPUs with Kimi K3 megakernel

AIvLLM project thanks SemiAnalysis for highlighting its TPU work, including the Kimi K3 megakernel and vLLM TorchTPU powering AgentX results across GLM-5.2, Qwen3.5 397B, and Kimi K3. The post credits Inferact and Google Cloud for pushing vLLM performance on TPU and points readers to SemiAnalysis's write-up on TPU inference.
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Thanks to @SemiAnalysis_ for highlighting the vLLM ecosystem’s work on TPU, in particular the Kimi K3 megakernel and vLLM TorchTPU powering AgentX results across GLM-5.2, Qwen3.5 397B, and Kimi K3.

Shoutout to @inferact and @googlecloud for pushing vLLM performance on TPU. The @SemiAnalysis_ write-up below is a great read 👇

SemiAnalysis@SemiAnalysis_
The Iron(wood) Throne: Google's TPU Stakes Its Claim on Inference Viable Alternative of CUDA Moat, NVIDIA Working Nights & Weekends, Jensen’s Counter Submission, Kimi K3, Meta Muse Spark Open, GLM5.3, TorchTPU, SGLang, vLLM, llm-d, Ray https://newsletter.semianalysis.com/p/tpu-strikes-the-king
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Original title: Thanks to @SemiAnalysis_ for highlighting the vLLM ecosystem’s work on TPU, in particular the Kimi K3 megakernel and vLLM TorchTPU poweri...