Vllm Projects Handbook

vLLM PROJECTS HANDBOOK

Build, Serve, Optimize, and Scale Open LLMs with PagedAttention, Continuous Batching, Quantization, Multi-GPU Inference, OpenAI-Compatible APIs, and Kubernetes

Running an LLM is easy. Serving it efficiently, reliably, and at scale is the real challenge.

vLLM Projects Handbook is a practical, project-driven guide to building high-performance LLM inference systems with vLLM. Instead of isolated commands and unexplained tuning flags, you build one evolving system called RelayServe, taking it from a single-GPU inference engine to a production-ready platform.

Inside this book, you will learn how to:

• Build OpenAI-compatible LLM APIs with vLLM
• Understand PagedAttention and continuous batching
• Measure TTFT, inter-token latency, throughput, queueing, and saturation
• Optimize KV-cache usage, prefix caching, and chunked prefill
• Quantize models and evaluate real performance trade-offs
• Apply speculative decoding and hardware-aware tuning
• Scale across multiple GPUs and multiple nodes
• Use tensor, pipeline, data, and expert parallelism
• Serve LoRA adapters, embeddings, rerankers, structured outputs, tools, and multimodal models
• Secure and harden production inference services
• Add metrics, logs, traces, SLOs, alerts, and capacity planning
• Deploy with containers and Kubernetes
• Implement routing, autoscaling, rolling updates, and rollback

The book follows a clear engineering path:

RUN - SERVE - MEASURE - OPTIMIZE - SCALE - INTEGRATE - HARDEN - OBSERVE - DEPLOY

Every major improvement is measured against a baseline, helping you understand not only what to configure, but why it works and when it should be used.

Whether you are a Python developer, AI or ML engineer, DevOps professional, SRE, platform engineer, or technical learner, this book gives you a practical path from "the model runs" to a system you can benchmark, optimize, scale, secure, observe, deploy, and troubleshoot with confidence.

Build faster LLM inference. Measure what matters. Scale with evidence.