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This article provides a technical deep dive into constructing an LLM inference runtime from scratch using CUDA graphs, focusing specifically on performance optimization and bug resolution for H100 hardware deployments. It is highly relevant to engineers interested in the underlying infrastructure of machine learning models rather than high-level application usage or policy discussions.

Sources coveringTowards Data Science, InfoQ

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

How To Build Your Own LLM Runtime From Scratch

Towards Data ScienceJuly 22, 20261 min read

If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations. The post How To Build Your Own LLM Runtime From Scratch appeared first on Towards Data Science.

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