llama.cpp vs SGLang
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
SGLang
Fast serving framework for LLMs and vision-language models
| Feature | llama.cpp | SGLang |
|---|---|---|
| Category | Local LLM Runners | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | C++ | Python |
| Setup difficulty | Hard | Hard |
| Min. RAM | 8,192 MB | 16,384 MB |
| Deployment | binary, bare-metal, docker | docker, kubernetes, bare-metal |
| GitHub stars | ★ 123,039 | ★ 31,514 |
| 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 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 llama.cpp or SGLang better?
Neither is universally better. llama.cpp has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.
Are llama.cpp and SGLang free and open-source?
Yes. llama.cpp is licensed under MIT and SGLang under Apache-2.0. Both can be self-hosted at no software cost.
Can I run llama.cpp and SGLang with Docker?
llama.cpp: yes. SGLang: yes.