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Get started with vLLM

Learning path | 5 resources | 1 hr and 10 mins | Published on July 7, 2026
Cedric Clyburn Michael Santos

Learn how to compress, serve, and benchmark LLMs with vLLM.

Access the Developer Sandbox

  1. Overview: Get started with vLLM
  2. Prerequisites for the vLLM developer environment Page | 10 mins
  3. Optimize a model with LLM Compressor Page | 20 mins
  4. Serve LLMs efficiently with vLLM Page | 20 mins
  5. Measure what matters: benchmarking and evaluation Page | 20 mins

Overview: Get started with vLLM

Get started with vLLM feature share

As AI applications such as agents and retrieval-augmented generation (RAG) become more common, efficiently serving large language models (LLMs) in production with low latency and reasonable cost is more important than ever. Traditional inference setups frequently suffer from memory hoarding (wasting expensive GPU resources via static request allocation), high latency (queues growing exponentially during peak traffic), and scaling bottlenecks that demand prohibitive hardware costs.

To solve these exact challenges, vLLM emerged as a high-throughput, memory-efficient, and easy-to-use engine designed for LLM serving. This learning path provides you a common deployment pattern where you’ll select and optimize a model, serve it via an OpenAI API-compatible endpoint, and benchmark its performance.

Prerequisites:

In this learning path, you will:

  • Understand the fundamentals of efficient LLM inference and optimization.
  • Learn to optimize, serve, benchmark, and evaluate open-source LLMs.
  • Build a complete end-to-end LLM inference workflow.