vLLM
A high-throughput and memory-efficient inference and serving engine for LLMs
Our verdict
A solid, actively developed project. This assessment is derived from GitHub's own repository metrics on 2026-08-24, not from hands-on testing.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs, built in Python. It has 89,894 stars and 3,275 contributors, and it was pushed to within the last few weeks. The Apache-2.0 licence puts no real constraint on commercial use. The most recent tagged release is v0.27.1, from 2026-08-11. The open-issue backlog is large for the project’s size, at 7,014. Figures come from the GitHub API on 2026-08-26 and refresh daily.
Reviewed by AI Review Rating from GitHub API data How we score
Repository
- Licence
- Apache-2.0
- Language
- Python
- Last push
- 2026-08-24
Releases
-
v0.27.1v0.27.1 -
v0.27.0v0.27.0 -
v0.26.0v0.26.0 -
v0.25.1v0.25.1 -
v0.25.0v0.25.0 -
v0.24.0v0.24.0
Pros and cons
Pros
Cons
Pricing
- Free tierYes
- LicenceApache-2.0
- Self-hostYes, source is public
- Public price listNot published
vLLM does not publish a web price list. For consumer apps that usually means pricing appears inside the product; for enterprise tools it means talking to sales. Why we do this
Visit vllm.aiTrends
Price points are recorded when a price moves, not on a schedule, so a flat run means the price held. Release cadence comes from GitHub releases. A series appears only once we have watched it for long enough to have more than one observation.
Changelog
On video
What users actually say
- vLLM Production Stack or LLM-d Reddit · 2026
- Inside vLLM: Anatomy of a High-Throughput LLM Inference … Reddit · 2025
- Benchmarking Disaggregated Prefill/Decode in vLLM Serving with NIXL Reddit · N/A
- LLM Inference Benchmarking - genAI-perf and vLLM Reddit · 2026
- Run Pytorch, vLLM, and CUDA on CPU-only environments with remote GPU kernel execution Reddit · N/A
We link to discussions rather than reproducing them. Opinions belong to their authors.
User reviews
No one has reviewed vLLM here yet. Reviews are moderated before they appear, and our own score is editorial and separate from user ratings, though once reviews land they do lift it.
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Side by side
| Tool | Our score | From | Free tier | Best for | |
|---|---|---|---|---|---|
| vLLM this page | 7.5 | - | Yes | ||
| 🥇Transformers | 7.9 | - | Yes | ||
| 🥈LocalAI | 7.8 | - | Yes | ||
| 🥉GPT4Free | 7.7 | - | Yes | ||
| Headroom | 7.5 | - | Yes | ||
| Ollama | 7.3 | - | Yes | ||
| Open WebUI | 7.3 | - | Yes |
Articles on vLLM
Self-Hosting an LLM in 2026: A Small Team Needs 675 Tokens a Second, Nonstop, Before a $244.80 GPU Beats a $0.14 API
A $244.80 a month RTX 4090 on Runpod Community Cloud only beats Together AI Llama 3 8B Instruct Lite above…
Open Source Alternatives to Paid AI Tools in 2026: $560.50 a Month Off a Small Team’s Bill, and the Three Licences That Decide If You Can
A small team on Zapier, Pinecone, Algolia, Datadog, 5 ChatGPT seats and ElevenLabs pays $560.50 a month, $6,726 a year,…
Questions
Is vLLM free?
Yes. vLLM advertises a free tier on its own site.
How much does vLLM cost?
vLLM does not publish pricing openly, which usually means it is quote-based. You will need to contact the vendor.
Is vLLM open source?
Yes. vLLM is a public repository released under Apache-2.0. You can self-host or inspect the source directly.
How is vLLM scored here?
vLLM scores 7.5 out of 10. That comes from a desk review of public evidence, which is capped at 8.5 out of 10: public pricing, documentation, maintenance signals and how much we could actually verify. Affiliate relationships are excluded from scoring entirely. The full rubric is published on our methodology page.