AI Coding Live News

On 2026-11-05 Hyperstition open-sourced Hyperkernel’s first generated megakernels (Hyperserve), with vendor-reported gains versus NVIDIA-published vLLM baselines; Alpha, B300-oriented.

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SemiAnalysis revealed the first Google TPUv7 benchmark submission for the 2.8T Kimi K3 MoE model, led end-to-end by Inferact (the creators of vLLM) and Google using the TorchTPU vLLM stack. At a hardware rental cost of just $1.21/chip-hour (vs $2.31 for GB300 and $1.86 for GB200), TPUv7 delivers ~2.2M tokens per $1 TCO in the ultra-high interactivity regime (~390 tok/s/user), outpacing NVIDIA GB200 NVL72, B300, and AMD MI355X on cost-performance.

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vLLM and NVIDIA unveiled a unified CuTe DSL decode megakernel (~12,000 LoC authored by NVIDIA CentML's Benjamin Chislett) for Moonshot AI's frontier Kimi K3 model on NVIDIA GB300 NVL72. By fusing attention, MoE, and cross-GPU communication, the optimization cuts per-layer latency from 130 µs (23 kernels) to 69 µs (13 kernels), delivering 842 tokens/s at batch=1 with DSpark speculative decoding (1.73x faster than stock vLLM) and up to 1.8x higher P90 per-user interactivity (403.2–423 tok/s/user) on SemiAnalysis AgentX, with DeepSeek-V4.1-Flash (PR #61868) and GLM-5.3 support coming next.

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Inferact open-sourced inferact/tpu-megakernels on 2026-09-23: a Pallas megakernel collection for TPU v7. Their blog reports Kimi K3 at 709 tok/s on 16× TPU v7 Ironwood vs 452 tok/s on 16× GB200 under DSpark speculative decoding (acceptance length 6), and roughly 1.4–2× the GB200 baseline at batch sizes 1–8 without speculation. All 92 MoE layers of Kimi K3 run in a single kernel with cross-layer weight prefetch.

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