Fregly C. AI Systems Performance Engineering (Early Release) 2025

Textbook in PDF format

Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions.
Preface
Introduction and AI System Overview (available)
AI System Hardware Overview (available)
OS, Docker, and Kubernetes Tuning for GPU-based Environments (available)
Distributed Communication and I/O Optimizations (available)
CUDA Programming, Profiling, and Debugging (unavailable)
Optimizing CUDA Performance (unavailable)
PyTorch Profiling and Tuning (unavailable)
Distributed Training at Ultra‑Scale (unavailable)
Multi-Node Inference Optimizations (unavailable)
AI System Optimization Case Studies (available)
Future Trends in Ultra-Scale AI Systems Performance Engineering (available)
AI Systems Performance Checklist (175+ Items) (available)