vLLM High-Performance Inference Framework
vLLM is a high-throughput inference and serving engine for large language models, using PagedAttention to dramatically improve memory efficiency and throughput. It supports continuous batching, tensor parallelism, and OpenAI-compatible API serving, making it a top choice for deploying LLMs at scale.
Overview
"vLLM High-Performance Inference Framework" is a "Open Source" resource curated by AI Resource Hub, filed under the Frameworks category and suited to Advanced-level learners. It is provided by vLLM, was last updated on 2026-06-26, and holds an editorial score of 4.7/5 from our team. Click "Visit Resource" on the right to open the original page.
Our Verdict
vLLM is the de facto engine for serving open-source LLMs in production, and PagedAttention is the reason: memory-efficient paging and continuous batching squeeze several times the throughput from the same GPU when traffic spikes. The OpenAI-compatible server means existing clients and SDKs just work. You will need a GPU and real ops skills to run it well, and it serves models — it doesn't train them. For anything beyond personal tinkering, this is where self-hosted inference should start.
Tags
Key Features
- ▹High-throughput LLM serving
- ▹PagedAttention for efficient memory use
- ▹OpenAI-compatible server
Best For
- ▹High-throughput serving of open LLMs
- ▹Continuous batching under heavy concurrent load
- ▹GPU fleets serving OpenAI-compatible endpoints
Not Ideal For
- ▪Single-user local chat on laptops
- ▪Prototypes without GPU infrastructure
Pros
- +Fast, production-grade inference
- +Efficient GPU utilization
- +High-throughput serving with PagedAttention
Cons
- −Requires a GPU and setup
- −Requires a GPU and setup expertise
- −Focused on serving, not training or fine-tuning
FAQ
Details
- Pricing
- Free and open source
- Author
- vLLM
- Editorial score
- ★ 4.7 / 5
- Last updated
- Jun 26, 2026