head to head · open source
Ollama vs vllm-omni
Ollama has 185,702 GitHub stars, 18,873 forks, 3,957 open issues and last shipped yesterday. vllm-omni has 7,494 stars, 2,037 forks, 1,977 open issues and last shipped yesterday. Ollama leads on adoption by 2,378% (185,702 vs 7,494 stars). Ollama is written in Go under MIT; vllm-omni is written in Python under Apache-2.0. Ollama has attracted 10% as many forks as stars, vllm-omni 27%. Ollama was the more recently maintained of the two, and both are self-hostable with no licence fee.
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
| Ollama | vllm-omni | |
|---|---|---|
| GitHub stars | ★ 186K | ★ 7.5K |
| License | MIT | Apache-2.0 |
| Written in | Go | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 19K | ⑂ 2.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Ollama if
- You weight community size — 186K stars and counting
- You want the MIT license terms
- Your stack matches Go
- 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 Ollama
Ollama Start building with open models. Download macOS or download manually Windows or download manually Linux Manual install instructions Docker The official Ollama Docker image ollama/ollama is available on Docker Hub. Libraries ollama python ollama js Community Discord 𝕏 (Twitter) Reddit Get started You'll be prompted to run a model or connect Ollama to your existing agents or applications such as Claude Code , OpenClaw , OpenCode , Codex , Copilot , and more. Coding To launch a specific integration: Supported integrations include Claude Code, Codex, Copilot CLI, DeepSeek Harness, Droid, and OpenCode. AI…
read the full Ollama overview →
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 Ollama or vllm-omni more popular?
Ollama has 185,702 GitHub stars and vllm-omni has 7,494. Ollama has the larger community by that measure.
Are Ollama and vllm-omni free?
Both are open source. Ollama is licensed under MIT and vllm-omni under Apache-2.0. Neither carries a licence fee.
What is the difference between Ollama and vllm-omni?
Ollama is written in Go 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, Ollama or vllm-omni?
Choose Ollama if you want the larger community (185,702 stars) or its MIT licence terms. Choose vllm-omni if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.