MLX LM vs SGLang
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
MLX LM
Run and fine-tune LLMs locally on Apple Silicon with MLX
VS
SGLang
Fast serving framework for LLMs and vision-language models
| Feature | MLX LM | SGLang |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Medium | Hard |
| Min. RAM | 16,384 MB | 16,384 MB |
| Deployment | source, binary | docker, kubernetes, bare-metal |
| GitHub stars | ★ 6,543 | ★ 31,514 |
| First released | 2024 | 2024 |
| Replaces | OpenAI API | OpenAI API |
Why pick each one
Choose MLX LM if…
- Released under the MIT license
- Mature project with 6.5k GitHub stars
- Written in Python
Choose SGLang if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 31.5k GitHub stars
Frequently asked questions
Is MLX LM or SGLang better?
Neither is universally better. SGLang 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 MLX LM and SGLang free and open-source?
Yes. MLX LM is licensed under MIT and SGLang under Apache-2.0. Both can be self-hosted at no software cost.
Can I run MLX LM and SGLang with Docker?
MLX LM: check the project docs for container support. SGLang: yes.