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vLLM in Production: Running LLMs at Scale with GPUs, High-Performance Inference & Modern AI Infrastructure
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Publisher: Independently Published
Published: Jan 26 2026
Pages: 250
Weight: 1.30
Height: 0.53 Width: 8.50 Depth: 11.00
Language: English
LLM inference is no longer experimental-it is production infrastructure.
As models grow larger, applications become agent-driven, and real users arrive, the true bottleneck shifts from training to serving models reliably, securely, and at scale.
vLLM in Production is a hands-on, operator-first guide to running large language models in real environments-where GPUs are finite, latency matters, failures happen, and cost must be controlled.
This book is not about prompt engineering or theoretical AI. It is about engineering discipline: how inference actually behaves under load, why naïve deployments collapse, and how to design systems that remain stable when traffic, context length, and concurrency collide.
You will learn how vLLM achieves high throughput through architectural choices like PagedAttention and continuous batching, and-more importantly-how to deploy, tune, and operate it safely in production. The book walks you from single-node GPU servers to full-stack inference platforms with APIs, authentication, agents, retrieval-augmented generation (RAG), monitoring, and failure recovery.
Every chapter is practical. Every concept is validated through labs. Every design choice is grounded in real operational tradeoffs.
What You'll Learn- Why LLM inference breaks at scale and how to avoid common failure modes
- How GPU memory, KV cache behavior, and context length define real capacity
- How vLLM works internally-and what actually makes it fast
- How to deploy vLLM on bare metal, virtualized GPU hosts, and private clouds
- How to serve models through secure, OpenAI-compatible APIs
- How to run agent and RAG workloads without runaway cost or instability
- How to load-test inference systems and identify safe operating limits
- How to monitor GPU utilization, latency, and throughput that truly matter
- How to troubleshoot OOMs, throughput collapse, and latency spikes
- How to plan capacity, estimate inference cost, and scale responsibly
This book is built around lab-first, failure-driven learning:
- Chapter-based practice labs reinforce every major concept
- A full-stack capstone project guides you through designing, deploying, and operating a production-grade inference platform using vLLM, GPUs, APIs, agents, RAG, and monitoring
- Operator-grade appendices provide cheat sheets, runbooks, security checklists, and 2026-ready roadmaps you can reuse in real systems
- Backend and platform engineers running LLMs in production
- Infrastructure and DevOps teams managing GPU-backed services
- AI engineers building agent-based and RAG-powered applications
- Technical founders and builders operating private or on-prem inference platforms
If you are responsible for uptime, latency, cost, or reliability, this book is for you.
Who This Book Is Not ForThis is not an introductory AI book.
It does not cover prompt engineering, model training, or high-level AI theory.
It assumes you want to operate LLM inference systems-not experiment with them.
Most resources stop at it works.
This book starts where demos fail.
vLLM in Production teaches you how to:
- Enforce limits instead of hoping for stability
- Reject traffic safely instead of crashing GPUs
- Measure reality instead of guessing performance
- Recover from failure instead of restarting blindly
LLM inference is now core infrastructure.
This book shows you how to run it like one.
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