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This book teaches you how to design, build and operate large language model inference systems that deliver fast responses under real-world load. You will learn the full stack from tokenization and GPU kernels through distributed serving clusters and production reliability engineering. Every concept is explained intuitively first, then technically, then demonstrated with concrete implementation guidance using modern tools like vLLM, SGLang, TensorRT-LLM, llama.cpp, Kubernetes and NVIDIA GPUs. The book is organized as a progressive journey from understanding how a single request flows through an LLM to architecting geographically distributed platforms serving millions of requests. Measurement-driven engineering runs throughout: you will learn to define latency targets, establish baselines, profile bottlenecks, change one variable at a time, benchmark rigorously and iterate based on evidence rather than assumptions.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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