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Large language models have become too big to run on a single GPU. Quantization is the technology that makes them deployable, and it has evolved from an academic curiosity into an engineering discipline with its own algorithms, toolchains, and best practices. This book explains how LLM quantization works, from first principles through production deployment. You will learn the internals of GPTQ, AWQ, GGUF, NF4, and other methods; understand their trade-offs in quality, speed, and memory; and find clear guidance for choosing the right approach for your hardware and use case. Along the way you will encounter real benchmarks, reproducible code examples, and a decision framework for navigating one of the most rapidly evolving areas of applied machine learning.

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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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