llama.cpp vs MLX LM
A side-by-side comparison of two self-hosted apps from related categories — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
llama.cpp
High-performance LLM inference in plain C/C++
VS
MLX LM
Run and fine-tune LLMs locally on Apple Silicon with MLX
| Feature | llama.cpp | MLX LM |
|---|---|---|
| Category | Local LLM Runners | Self-Hosted AI |
| License | MIT | MIT |
| Language | C++ | Python |
| Setup difficulty | Hard | Medium |
| Min. RAM | 8,192 MB | 16,384 MB |
| Deployment | binary, bare-metal, docker | source, binary |
| GitHub stars | ★ 123,039 | ★ 6,543 |
| First released | 2023 | 2024 |
| Replaces | OpenAI API | OpenAI API |
Why pick each one
Choose llama.cpp if…
- Released under the MIT license
- First-class Docker support for quick deployment
- Mature project with 123k GitHub stars
- Written in C++
Choose MLX LM if…
- Released under the MIT license
- Mature project with 6.5k GitHub stars
- Written in Python
Frequently asked questions
Is llama.cpp or MLX LM better?
Neither is universally better. llama.cpp has the larger community, while MLX LM is simpler to set up (medium difficulty). Choose based on the comparison table above and your own setup.
Are llama.cpp and MLX LM free and open-source?
Yes. llama.cpp is licensed under MIT and MLX LM under MIT. Both can be self-hosted at no software cost.
Can I run llama.cpp and MLX LM with Docker?
llama.cpp: yes. MLX LM: check the project docs for container support.