vLLM
docs.vllm.ai
High-throughput open-source LLM inference engine.
Quick Information
- Category:
- MLOps & Infrastructure
- Pricing:
- Free
- Platforms:
- Linux, Python, Cloud
- Free Plan:
- Yes
- API Available:
- Yes
- Last Verified:
- October 2026
What Is vLLM?
vLLM is a high-throughput, memory-efficient open-source engine for serving large language models. It is free and licensed under Apache 2.0, per Wikipedia and a comparison article. The most recent release found is v0.32.0, dated October 18, 2026, which PyPI and GitHub both list as latest. The release notes highlight low-precision KV cache across more GPUs, with 814 commits from 363 contributors, and the project site also mentions NVFP4 KV cache, Model Runner V2 with Elastic EP and n-gram speculation, and faster startup. Releases come roughly every two weeks: PyPI history shows 0.31.0 on October 5 and 0.30.0 on September 22, 2026. One comparison article still cites v0.24.0 as current, so it is out of date.
On governance, Wikipedia says the PyTorch Foundation welcomed vLLM as a hosted project on May 7, 2025, and cites a TechCrunch report from January 22, 2026 that Inferact raised $150 million to commercialize vLLM. I did not find detail on Inferact's current offering, so I cannot say what a paid vLLM product costs. For most people the cost is just the GPUs, and you can rent those from providers such as RunPod or Lambda. Hugging Face has pointed TGI users toward vLLM, SGLang and llama.cpp for new builds, which signals where the ecosystem is heading.
vLLM suits engineering teams self-hosting open models who need high throughput and an OpenAI-compatible server. Compare Text Generation Inference, TensorRT-LLM and llama.cpp. Browse the MLOps & Infrastructure tools directory for more.
What Can You Use vLLM For?
Production LLM serving
High throughput.
OpenAI-compatible endpoints
Self-hosted.
vLLM Features
- v0.32.0 (Oct 18, 2026) — Low-precision KV cache.
- Two-week release cadence — Rapid iteration.
- PyTorch Foundation project — Since May 2025.
Who Is vLLM For?
- ML platform teams — Throughput.
- Open-model deployers — Broad model support.
vLLM Pros & Limitations
What We Like
- Free Apache 2.0
- Very active development
- Wide hardware and model support
Limitations
- Needs GPU operations knowledge
- Fast releases can break setups
- Commercial offering unclear
vLLM Pricing
| Plan | Price | Main Features |
|---|---|---|
| Open source | Free (Apache 2.0) | You pay for GPUs; commercial offering from Inferact not detailed |
Pricing was last verified October 2026. Check the official website for the latest pricing.
AI Listing Jungle Ranking
- Global Rank:
- #420
- Overall Score:
- 67.8/100
- Monthly Users:
- –
- Rank Change:
- ▲ 1158
This ranking reflects AI Listing Jungle's own independent scoring methodology and is not an endorsement by vLLM or its company. How Our Rankings Work
Frequently Asked Questions
Is vLLM free?
Yes, Apache 2.0.
Latest version?
v0.32.0, October 18, 2026.
Who backs it?
PyTorch Foundation; Inferact raised $150M.
Is it OpenAI-compatible?
It provides an OpenAI-compatible server.
Our Take
The default open-source LLM inference engine.
Pin versions in production.
vLLM Alternatives
vLLM Comparisons
Ready to try vLLM?
Visit the official website to get started.
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