Compare / Ollama vs vLLM
Ollama vs vLLM
Pick Ollama if you want an MIT CLI/daemon and an API on a machine you host (pairs with /stacks/open-webui-litellm). Pick vLLM if you want a high-throughput OpenAI-compatible serving engine on a GPU you already run.
Facts checked 20 Oct 2026. License, language, and GitHub activity come from the catalog or the project's own repo. "Not in the catalog row" means the public catalog dump does not store that number.
Ollama in the catalog vLLM in the catalog
How they compare
| Ollama | vLLM | |
|---|---|---|
| License | MIT License (catalog) source | Apache License 2.0 (catalog) source |
| Language | Go (catalog) | Python (catalog) |
| GitHub activity | 183,730 stars in the catalog source | 94,158 stars in the catalog source |
| Self-host | One daemon. ollama pull, then an API on localhost. | Serving engine. GPU recommended. OpenAI-compatible HTTP. |
| Best for | A laptop or one box next to Open WebUI. | Throughput on a GPU you already pay for. |
Self-hosting and resource needs
Ollama is already on /best/local-llm. vLLM is vllm-project/vllm — an inference server, not a chat UI. Point Open WebUI or LiteLLM at either. LM Studio stays the desktop GUI (not OSS) — /compare/ollama-vs-lm-studio. Neither has a one-click c17 Railway template; a GPU host is the honest path for vLLM.
FAQ
- Where is the chat UI?
- /stacks/open-webui-litellm and /best/llm-chat-ui. Point Open WebUI at either runner.
- Ollama or LM Studio?
- LM Studio is the desktop GUI and is not open source — /compare/ollama-vs-lm-studio.
- Are these free?
- Ollama is MIT. vLLM is Apache License 2.0. You pay hardware. Weights have their own licenses.
- How do I deploy Ollama?
- Install the project's documented binary. Pull one model. Point Open WebUI at it.
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