How this book is organized

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 covers the execution model and the compute-bound versus memory-bound boundary that everything later depends on. Kernel optimization applies it to the three computations that matter most in practice: matrix multiplication, low-precision arithmetic, and attention.

Programming models and profiling 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 and Distributed inference move up to the systems layer, where scheduling, batching, and placement decide throughput more than any single kernel does. Current hardware closes with what the chips being deployed right now actually provide.

Two appendices sit outside that order. The glossary defines every term of art once, quoting its source verbatim. The reading list collects the primary sources: papers, specifications, and repositories, grouped the same way the chapters are.