vLLM
vLLM is an open-source, high-throughput inference and serving engine for large language models, built on the PagedAttention memory management algorithm. It enables efficient deployment of LLMs by eliminating KV cache fragmentation and supporting continuous batching, making it possible to serve many concurrent requests on the same GPU hardware. vLLM exposes an OpenAI-compatible API, making it a drop-in replacement for production systems that already use the OpenAI client.
In 2026, the dominant challenge for AI teams is not training models but serving them cheaply and at low latency at scale — and vLLM has become the de facto open-source standard for that job, adopted in production by organizations ranging from startups to Amazon and LinkedIn. Companies hiring ML engineers, platform engineers, and MLOps practitioners increasingly expect hands-on familiarity with vLLM for model deployment roles. Knowing how to quantize, deploy, and benchmark models with vLLM is now a baseline skill for anyone operating LLMs outside of a managed API.
🎓 Courses
Fast & Efficient LLM Inference with vLLM
by Cedric Clyburn (Red Hat)
The most direct course on vLLM available — walks through the full optimize-deploy-benchmark cycle: quantizing a model with LLM Compressor, serving it with vLLM, and load-testing with GuideLLM. Announced June 2026 in partnership with Red Hat. Free to audit.
Efficient Online Training with GRPO and vLLM in TRL
by Hugging Face team
Hands-on notebook demonstrating how vLLM slots into an online RLHF training loop (GRPO with TRL), including multi-GPU setups where vLLM runs on dedicated generation GPUs. Directly applicable to fine-tuning pipelines.
Quickstart — vLLM Official Documentation
by vLLM core team
The authoritative starting point: covers installation (uv/pip, NVIDIA/AMD/Apple Silicon), offline batch inference with LLM + SamplingParams, and spinning up an OpenAI-compatible server. Free and always up-to-date with the latest release.
Serving LLMs with vLLM: A Practical Inference Guide
by Nebius AI team
Practical production-oriented walkthrough covering continuous batching, PagedAttention internals, tensor parallelism, and benchmarking — useful as a companion reference after the DeepLearning.AI course.
📖 Books
LLM Engineer's Handbook: Master the Art of Engineering Large Language Models from Concept to Production
Paul Iusztin and Maxime Labonne · 2024
The most comprehensive published book covering the full LLM engineering lifecycle including inference optimization and production serving — the chapter on deployment addresses the tooling ecosystem in which vLLM sits. Published October 2024 by Packt, 522 pages.
🛠️ Tutorials & Guides
vLLM Quickstart: High-Performance LLM Serving
A concise third-party tutorial walking through installation, first inference, and OpenAI-API server setup — useful for readers who want a narrative guide alongside the official docs.
vLLM Production Deployment: Complete 2026 Guide
Covers production concerns including the V1 engine's disaggregated prefill/decode, hardware selection (NVIDIA H100, AMD MI300X), and scaling strategies — good bridge between learning the basics and operating vLLM at scale.
vLLM 2024 Retrospective and 2025 Vision
Written by the core team, this post explains the architectural trajectory of vLLM — 100+ model architectures, the V1 engine, PyTorch Foundation governance, and the llm-d Kubernetes-native direction. Essential context for practitioners who want to understand where the project is headed.
Learning resources last updated: June 18, 2026