# How this book is organized · Introduction

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The book is ordered the way the problem is: from one inference request down to a single kernel, then back out to a fleet of machines. [Foundations](https://learn-kernels.com/chapters/foundations) covers the execution model and the compute-bound versus memory-bound boundary that everything later depends on. [Kernel optimization](https://learn-kernels.com/chapters/kernel-optimization) applies it to the three computations that matter most in practice: matrix multiplication, low-precision arithmetic, and attention.

[Programming models and profiling](https://learn-kernels.com/chapters/programming-models) is about the tools you actually write and measure kernels with, since very little of this work is done in raw CUDA C++ any more. [Inference engines](https://learn-kernels.com/chapters/inference-engines) and [Distributed inference](https://learn-kernels.com/chapters/distributed-inference) move up to the systems layer, where scheduling, batching, and placement decide throughput more than any single kernel does. [Current hardware](https://learn-kernels.com/chapters/hardware) closes with what the chips being deployed right now actually provide.

Two appendices sit outside that order. The [glossary](https://learn-kernels.com/chapters/glossary) defines every term of art once, quoting its source verbatim. The [reading list](https://learn-kernels.com/chapters/reading) collects the primary sources: papers, specifications, and repositories, grouped the same way the chapters are.

On sources

Every factual claim here traces to a citable source, linked in a sidenote at the end of each section. Where a number appears, it came from a datasheet, a specification, or a measurement someone published, not from an estimate.