AI Infra Book is an open-source technical book and companion repository focused on the infrastructure behind modern large language models. It approaches inference and training through quantitative analysis of hardware limits, data movement, model architecture, and distributed systems. The book contains twelve chapters supported by formulas, diagrams, experiments, and case studies. Companion tools help readers reproduce resource calculations and inspect the assumptions behind system-design decisions. Additional material covers accelerators, networking, KV caches, inference serving, training systems, mixture-of-experts models, and performance engineering. The source is written in Markdown and automatically built into a XeLaTeX PDF and a searchable website. Research notes, references, calculation scripts, and historical revisions are preserved alongside the main manuscript.
Features
- Twelve-chapter AI infrastructure book
- Quantitative inference and training analysis
- Hardware and distributed-system coverage
- Reproducible calculation tools
- Experiments and system case studies
- Automated PDF and web publication