Supercomputing for Artificial Intelligence - AI Scaling Guide

When code is cheap, performance is expensive. This book serves as a systems-oriented guide to understanding the mechanics of AI code running at scale. It provides the technical foundation needed to train deep learning models and Large Language Models (LLMs) using GPUs, clusters, and supercomputers. Readers will learn how to reason about performance, scalability, and cost in high-performance computing environments. The text focuses on the practical execution trade-offs involved in scaling deep learning systems, moving from basic neural networks to complex LLMs. This guide is designed to help professionals and graduate students master the infrastructures and tools required for modern AI workloads. By focusing on the systems side of artificial intelligence, you can better navigate the challenges of hardware and software integration in large-scale machine learning projects.