<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AI on Silicon</title><description>LLM inference through the lens of a GPU: every number measured on real hardware, every claim sourced, every result reproducible from a public repo.</description><link>https://tgarg01.github.io/</link><item><title>Episode 1: To 1x NVIDIA H100 GPU, Qwen3.8-27B isn&apos;t a model. It&apos;s a loop of 902 kernels.</title><link>https://tgarg01.github.io/AIonSilicon/posts/01-a-loop-of-902-kernels/</link><guid isPermaLink="true">https://tgarg01.github.io/AIonSilicon/posts/01-a-loop-of-902-kernels/</guid><description>What one decode step of Qwen3.8-27B looks like on 1x NVIDIA H100 GPU: 902 kernels in 20.55 ms, 92.6% of the GPU time in matrix multiplies. Measured with vLLM 0.31 and Nsight Systems.</description><pubDate>Sun, 18 Oct 2026 00:00:00 GMT</pubDate><category>LLMInference</category><category>GPU</category><category>CUDA</category><category>vLLM</category><category>NVIDIA</category></item></channel></rss>