Distributed AI Systems

Preface

Large AI models now reach billions or even trillions of parameters. Training and serving them is no longer a single-GPU exercise—it is a distributed systems problem. You need to reason about GPU memory, high-speed interconnects, collective communication, job schedulers, inference batching, and production observability, often in the same week. Many resources explain one layer well: a training framework here, an inference engine there, a Kubernetes guide somewhere else. What has been missing is a single path from distributed training through inference to production serving, with runnable code you can execute on your own hardware.

This book is that path. Complete with hands-on code examples, it guides you to build distributed AI systems ...