Ollama vs llama.cpp vs vLLM
Short answer: Use Ollama for the easiest local setup, llama.cpp for CPU, edge devices and fine control, and vLLM for high-throughput GPU serving to many users.
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| Ollama | llama.cpp | vLLM | |
|---|---|---|---|
| Best for | Easiest local setup | CPU, edge devices, full control | High-throughput GPU serving |
| Hardware | CPU or GPU | CPU or GPU | GPU |
| Licence | MIT | MIT | Apache-2.0 |
| Repo | ollama/ollama | ggml-org/llama.cpp | vllm-project/vllm |
Rule of thumb: start with Ollama, drop to llama.cpp when you need fine control or very low resources, and move to vLLM when many users hit one GPU server. LocalAI is another OpenAI-compatible option. Add a UI from the ChatGPT alternatives list.
Frequently asked questions
Should I use Ollama or llama.cpp?
Ollama is easier: one command to pull and run models, with an API. llama.cpp is the lower-level C/C++ engine, which gives more control and runs well on CPU.
When should I use vLLM?
When you serve many concurrent requests on a GPU. vLLM is built for high-throughput serving rather than single-user desktop use.
Do they offer an OpenAI-compatible API?
All three can expose an HTTP API that many OpenAI-compatible clients can use. Check each project's docs for the exact endpoints.