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This book teaches experienced engineers how to build, measure, and optimize production LLM inference systems. Using vLLM as its primary case study, it walks you from the first principles of transformer inference and GPU architecture through vLLM's internal design, advanced performance techniques, distributed deployment, and operational reliability. You will learn not just how to configure vLLM, but how to reason about memory bottlenecks, scheduling trade-offs, parallelism strategies, and cost-performance optimization so you can architect inference infrastructure that scales. The material is grounded in primary sources, source code, and measured benchmarks; version-dependent details are flagged explicitly so the underlying engineering principles endure beyond any single release.

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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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