head to head · open source
vllm vs vllm-omni
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. vllm-omni has 7,494 stars, 2,037 forks, 1,977 open issues and last shipped yesterday. vllm leads on adoption by 1,183% (96,158 vs 7,494 stars). vllm is written in Python under Apache-2.0; vllm-omni is written in Python under Apache-2.0. vllm has attracted 25% as many forks as stars, vllm-omni 27%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (inference), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| vllm | vllm-omni | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 7.5K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 2.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 96K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick vllm-omni if
- You want the vllm-omni feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and vllm-omni fits your workflow better
About vllm
Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…
About vllm-omni
Easy, fast, and cheap omni modality model serving for everyone Latest News 🔥 [2026/08] We released 0.28.0, featuring production ready MiniMax H3 serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full duplex serving for the MiniCPM o series. [2026/08] VeRL Omni v0.2.0 is released: faster diffusion RL powered by vLLM Omni (request level/step wise batching with FA3), rebuilt Qwen3 Omni multimodal training (DPO & GSPO), plus LTX 2.3, Qwen Image Edit support and more. See the release notes. [2026/08] We released 0.26.0 aligned with the vLLM 0.26 release line, featuring MiniMax H3…
read the full vllm-omni overview →
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Frequently asked questions
Is vllm or vllm-omni more popular?
vllm has 96,158 GitHub stars and vllm-omni has 7,494. vllm has the larger community by that measure.
Are vllm and vllm-omni free?
Both are open source. vllm is licensed under Apache-2.0 and vllm-omni under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and vllm-omni?
vllm is written in Python and vllm-omni in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, vllm or vllm-omni?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose vllm-omni if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.