Get the eBook free when you register your print book at Manning. Today's AI models demand a lot of memory, compute, and server horsepower--which quickly translates into cost. This book show you how you can optimize AI models without architectural redesigns or task-specific compression. It reveals practical techniques for quantization, systematically reducing numerical precision to achieve faster inference, lower memory usage, and cheaper deployment--all with minimal accuracy loss. From quantization fundamentals to runtime packaging, the book gives you a complete and comprehensive overview of the full quantization pipeline. It starts by deriving quantization mapping from first principles, and then builds your knowledge and skill through techniques for production-tested PTQ and QAT workflows and a fully compressed deployment. You'll learn to apply post-training quantization to production models, run quantization-aware training using fake quantization and straight-through estimators, and handle subtle tradeoffs like activation outliers in LLMs, KV cache pressure, and sub-8-bit formats like NF4 and FP4. What's inside * Applying post-training quantization to production models * Deploying efficiently on CPUs, edge devices, and mobile * Framework-agnostic techniques and real cross-framework parity testing * Flowcharts and checklists for efficient decision making About the reader For ML engineers and researchers experienced in Python.

About the author Vivek Kalyanarangan is an AI/ML architect, researcher, and educator with over twelve years of experience designing and deploying large-scale machine learning systems.