Ollama vs vLLM
Both are catalogued under Model Serving. The figures come from the GitHub API; the assessments are ours.
OllamaRun open models locally with one commandOfficialMIT
vLLMHigh-throughput inference server with an OpenAI-compatible APIOfficialApache-2.0At a glance
| At a glance | Ollama | vLLM |
|---|---|---|
| License | MIT | Apache-2.0 |
| Languages | Go | Python, CUDA |
| Deployment | Runs locally / Self-hosted | Self-hosted / Runs locally |
| Maturity | Established | Established |
| Stars | 179k | 89.1k |
| Star growth over the last 7 days | +21 ★ | +23 ★ |
| Forks | 17.4k | 20.7k |
| Open issues | 3.7k | 6.7k |
| Last commit | 15 Aug 2026 | 15 Aug 2026 |
| Activity | Active | Active |
What each one does
Ollama
Handles model download, quantisation and an OpenAI-compatible server so a local model is a single command away. The obvious starting point for development against local inference. For serving many concurrent users, a throughput-oriented server will do considerably better.
Full entry →vLLM
Paged attention and continuous batching let one GPU serve far more concurrent requests than a naive loop, which matters because agents are unusually chatty. The OpenAI-compatible endpoint means most agent frameworks point at it with a base-URL change. Expect real operational work around GPU memory sizing and model loading times.
Full entry →What you can do
Ollama
- Run a model in one command —
ollama run gemma4pulls the weights and opens a chat session, with the full catalogue of runnable models at ollama.com/library. - Back a coding agent locally —
ollama launch claudestarts the Claude Code integration against local models, and the same command covers Codex, Copilot CLI, OpenCode, Droid and DeepSeek Harness. - Call it from your code — A REST endpoint at
http://localhost:11434/api/chataccepts JSON withmodel,messagesandstream, and official clients install withpip install ollamaornpm i ollama. - Turn it into an assistant —
ollama launch openclawconnects the local model to WhatsApp, Telegram, Slack and Discord as a personal AI assistant.
vLLM
- Serve more concurrent agents per GPU — Paged attention and continuous batching keep many requests in flight on a single card instead of running them through a naive one-at-a-time loop.
- Point existing clients at it — Most agent frameworks switch over to the OpenAI-compatible API server with a base-URL change and no client rewrite, and the same server also speaks the Anthropic Messages API and gRPC.
- Run it on the hardware you have —
uv pip install vllminstalls the server, which runs on NVIDIA, AMD and Intel GPUs and on x86/ARM/PowerPC CPUs, with hardware plugins covering Google TPUs, Intel Gaudi, Huawei Ascend and Apple Silicon. - Shrink a model to fit the card — Quantized weights are served directly — FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF and compressed-tensors are all on the supported list — so the memory a model needs is something you choose rather than a fixed property of the checkpoint.
- Constrain output and parse tool calls — Structured output generation runs through
xgrammarorguidance, and the server ships tool-calling and reasoning parsers for the models that emit them.