Ollama vs LM Studio: which to use and when
The simplest way to run open models on your own machine. A desktop GUI for discovering, running and chatting with local models.
At a glance
| Capability | Ollama | LM Studio |
|---|---|---|
| Licence | MIT | Proprietary (free for personal use) |
| Implementation | Go (wrapping llama.cpp) | Electron desktop app |
| Runs on | CPU or GPU | CPU or GPU |
| Continuous batching | No | No |
| Paged attention | No | No |
| Tensor parallelism | No | No |
| Quantisation | GGUF (2–8 bit) | GGUF, MLX (Apple Silicon) |
| 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.
LM Studio — Non-terminal users, model evaluation, and anyone who wants to compare quantisations of the same model interactively before committing to one.
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 LM Studio?
- Developers who want a model running locally in one command, and teams prototyping against open weights before committing to a serving stack. By contrast, lm studio is the better answer when: non-terminal users, model evaluation, and anyone who wants to compare quantisations of the same model interactively before committing to one.
- 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