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LLM Inference from Principles to Production (LLM 从原理到生产级推理)

🌐 Read on GitHub Pages (English Version) | 🌐 阅读精美网页版 (中文版)

Foreword

This book is a systematic organization of Large Language Model (LLM) inference technology, originating from the author's study notes and reflections during paternity leave (and welcoming my second baby, Emerson! 👶🍼🧸). With daily work focused on internal clusters supporting inference and other business, the author rarely had time to follow open-source progress. Therefore, the core objectives of this book are:

  1. Build Mental Models (End-to-End): Connect scattered knowledge points to help myself understand core LLM inference principles and serving frameworks end-to-end, establishing a global mindset.
  2. Track Open-Source Progress: Follow frontier progress in the open-source community to look beyond daily work, focusing especially on Kubernetes evolution and how it can better adapt to LLM inference.
  3. Establish a Sustainable Framework: Serve as a foundation for easier continuous updates in the future to stay up-to-date myself.

Disclaimer and Positioning: This book is not a profound mathematical derivation book, nor is it a "frontier tracking" that follows the latest daily papers. We will not get overly entangled in complex mathematical formulas and overly trivial code details. Our focus is on revealing the essential logic behind the technology.

Additionally, this book was deeply assisted by Gemini and Claude. Without AI, it would have been impossible for the author to learn and understand such a broad field in a single month. I would like to thank AI on one hand; on the other hand, this has reinforced my determination to "do inference well" — only by building efficient inference infrastructure can we make AI benefit more people.

Target Audience: The primary audience for this book is the author themselves. It is open-sourced on GitHub for convenient version control, switching between machines, and to potentially benefit other peers interested in this field (such as system architects, backend engineers, AI product managers, and developers curious about underlying mechanisms). If you also hope to establish a global understanding of large model inference from principles to production services, I hope these notes can offer some inspiration. Feel free to point out any errors.

Table of Contents

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