The LLM Stack: Systems Engineering for Generative AI at Scale

The LLM Stack: Systems Engineering for Generative AI at Scale

"The LLM Stack: Systems Engineering for Generative AI at Scale" provides a comprehensive, end-to-end exploration of the infrastructure, tools, and techniques required to build, deploy, and operate large language model applications in the real world. Philosophy: The core philosophy of this book is that Large Language Models are complex, distributed computing problems disguised as mative, focusing on performance, reliability, scalability, and cost-efficiency. My goal is to demystify the entire stack, from the GPU kernel level to the application API, providing readers with a holistic and deep understanding of how production-grade Generative AI systems are built. Key Features: 1. Globally Relevant: The principles and technologies covered are universal, making the book a valuable resource for students and professionals worldwide. 2. End-to-End Coverage: From understanding the Transformer architecture to managing GPU clusters and monitoring production systems, the book covers the entire lifecycle of an LLM application. 3. Deep Dives: Contains dedicated chapters on cutting-edge topics like inference optimization (Quantization, PagedAttention, Speculative Decoding), serving frameworks (vLLM, TGI), and observability for generative systems. 4. Practical Capstone Project: A complete, step-by-step guide to building a Retrieval-Augmented Generation (RAG) system, one of the most popular and powerful LLM application patterns. 5. Vendor-Agnostic Principles: While we use specific open-source tools for examples, the underlying principles taught are applicable across different cloud providers and proprietary platforms. To Whom This Book Is For: 1. B.Tech/M.Tech Computer Science Students: Students specializing in AI/ML or Systems Engineering will find this a vital textbook that prepares them for the next wave of computing. 2. Aspiring MLOps/LLMOps Engineers: This is your comprehensive guide to breaking into and excelling in one of the most sought-after roles in the tech industry. 3. Software and DevOps Engineers: Professionals looking to transition into the AI/ML space will find a structured path to understanding the unique infrastructure challenges of Generative AI. 4. Data Scientists and AI Researchers: This book will help you understand the engineering realities of deploying your models and research into production. Welcome to the world of the LLM Stack!