Ollama vs vLLM: which to use and when
The simplest way to run open models on your own machine. High-throughput inference server built around PagedAttention.
At a glance
| Capability | Ollama | vLLM |
|---|---|---|
| Licence | MIT | Apache 2.0 |
| Implementation | Go (wrapping llama.cpp) | Python + CUDA |
| Runs on | CPU or GPU | GPU only |
| Continuous batching | No | Yes |
| Paged attention | No | Yes |
| Tensor parallelism | No | Yes |
| Quantisation | GGUF (2–8 bit) | AWQ, GPTQ, FP8, INT8 |
| OpenAI-compatible API | Yes | Yes |
How to choose
Ollama — Developers who want a model running locally in one command, and teams prototyping against open weights before committing to a serving stack.
vLLM — Production serving of open models where throughput per GPU is the metric that matters, and for multi-GPU deployment of models too large for one card.
Both expose an OpenAI-compatible HTTP API, so this is not a one-way door: switching is a base-URL change, and running one locally while serving on the other is a common and sensible split.
Frequently asked
- Should I use Ollama or vLLM?
- Developers who want a model running locally in one command, and teams prototyping against open weights before committing to a serving stack. By contrast, vllm is the better answer when: production serving of open models where throughput per GPU is the metric that matters, and for multi-GPU deployment of models too large for one card.
- Can I use both?
- Yes, and most teams do. Because both expose an OpenAI-compatible HTTP API, moving a workload between them is a base-URL change. A common pattern is developing against the lighter runtime locally and serving production traffic on the higher-throughput one.
Project home: https://ollama.com