Ollama vs LM Studio vs llama.cpp vs MLX on a Mac
Short answer
Use LM Studio if you want a point-and-click app, Ollama if you want the simplest command line and an API for other apps, MLX if you want the fastest speeds on Apple Silicon and do not mind Python, and llama.cpp if you want full control. All four are free and run models fully offline.
All four tools run large language models on your Mac without sending anything to the cloud. They differ in how you use them and how much control you get.
At a glance
| LM Studio | Ollama | MLX (mlx-lm) | llama.cpp | |
|---|---|---|---|---|
| How you use it | Desktop app | Terminal and local API | Python and terminal | Terminal and local server |
| Best for | Beginners | Everyday use, other apps | Top speed on Apple Silicon | Tinkerers, full control |
| Model formats | GGUF and MLX | GGUF, plus MLX for some models | MLX | GGUF |
| Finding models | Built-in search | ollama pull |
Hugging Face | Hugging Face |
| Open source | No (free to use) | Yes | Yes | Yes |
LM Studio
A polished desktop app. You search for a model, click download, and chat. On Apple Silicon it can run both GGUF models (through llama.cpp) and MLX models, and it shows whether a model is likely to fit in your memory before you download it. It can also run a local server that speaks the same API as OpenAI's, so other apps can use your local model.
Choose it if you would rather not use the terminal.
Ollama
A small background app with a one-line command to download and run models. On Apple Silicon it also offers MLX versions of popular models (tags ending in -mlx). Its strength is the API: many apps and editor plugins support Ollama out of the box. See our Ollama setup guide.
Choose it if you want the simplest setup and plan to connect other tools.
MLX
MLX is Apple's open-source machine learning framework, built for Apple Silicon's unified memory. The mlx-lm package runs and fine-tunes language models:
pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3-8B-4bit --prompt "Explain unified memory in one paragraph."
Choose it if speed matters most, or you want to fine-tune models on your Mac.
llama.cpp
The engine underneath much of the local AI world. It is the most configurable option and gets support for new models quickly.
brew install llama.cpp
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
Choose it if you want to control every setting, or run a model the other tools do not support yet.
Our suggestion
Start with LM Studio or Ollama. Move to MLX when you want more speed, and to llama.cpp when you need a setting the others hide. Whichever you pick, the size of model you can run is set by your Mac's memory: see how much memory you need.
Frequently asked questions
Which is fastest on a Mac?
MLX, Apple's own machine learning framework, is usually the fastest on Apple Silicon, often modestly ahead of llama.cpp for the same model and quantization. LM Studio can run MLX models, so you can get that speed without writing code.
Can I use the same model files across these apps?
Partly. Ollama, LM Studio and llama.cpp all use GGUF files, though Ollama stores them in its own folder. MLX uses its own format, usually downloaded from the mlx-community organization on Hugging Face; Ollama also offers MLX versions of some models under tags ending in -mlx.