AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch

Unlock the full potential of modern artificial intelligence with *AI Systems Performance Engineering*, a practical guide to optimizing AI infrastructure for speed, scalability, reliability, and cost efficiency. Written by Chris Fregly, this book provides engineers, researchers, and developers with actionable techniques for improving the performance of AI systems across both model training and inference. As generative AI models continue to grow in size and complexity, efficient system design has become increasingly important. This book explores how hardware, software, and algorithms can be optimized together to maximize throughput, reduce latency, and control operating costs. Through step-by-step methodologies, readers will learn to optimize GPU CUDA kernels, improve PyTorch-based algorithms, and scale distributed training and inference across multiple nodes. The book also explains how to use performance profiling, identify bottlenecks, and apply industry-standard tools and frameworks to build efficient AI pipelines. Key topics include GPU cluster optimization, distributed model training, high-performance inference servers, full-stack performance engineering, and practical strategies for improving system resilience and reliability. A comprehensive checklist featuring more than 175 optimization techniques provides a valuable reference for applying proven improvements to real-world AI workloads. Ideal for AI engineers, machine learning researchers, infrastructure specialists, and performance-focused developers, this book offers a practical foundation for designing and operating high-performance AI systems at scale.