Quantization and Fast Inference

Today's AI models demand a lot of memory, compute, and server horsepower--which quickly translates into cost. Quantization and Fast Inference 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.