SGLang vs Xinference
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
| Feature | SGLang | Xinference |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 99 · Excellent | 93 · Excellent |
| Category | Self-Hosted AI | Self-Hosted AI |
| License | Apache-2.0 | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Hard | Medium |
| Min. RAM | 16,384 MB | 8,192 MB |
| Deployment | docker, kubernetes, bare-metal | docker, kubernetes, source |
| GitHub stars | ★ 36,383 | ★ 9,592 |
| First released | 2024 | 2023 |
| Replaces | OpenAI API | OpenAI API, Hugging Face Inference Endpoints |
What are SGLang and Xinference?
SGLang
SGLang is a high-performance serving framework for large language and vision-language models. It features a fast runtime with RadixAttention and a flexible programming language for complex LLM applications.
- RadixAttention caching
- Structured generation
- OpenAI-compatible server
- Multi-GPU scaling
Xinference
Xorbits Inference (Xinference) is a framework for serving language, embedding, image, audio, and rerank models with a single command. It exposes OpenAI-compatible APIs and supports distributed deployment across multiple machines.
- Serve LLMs, embeddings and images
- OpenAI-compatible API
- Distributed cluster support
- Built-in model registry
SGLang vs Xinference: key differences
Both projects are written in Python. Xinference is the lighter option, starting around 8,192 MB of RAM against 16,384 MB for SGLang. SGLang has the considerably larger community, at 36,383 GitHub stars versus 9,592.
Why pick each one
Choose SGLang if…
- Very high throughput
- RadixAttention prefix caching
- Vision model support
Watch out for
- Serious GPU required
- Complex tuning options
Choose Xinference if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 9.6k GitHub stars
Frequently asked questions
Is SGLang or Xinference better?
Neither is universally better. SGLang has the larger community, while Xinference is simpler to set up (medium difficulty). Choose based on the comparison table above and your own setup.
Are SGLang and Xinference free and open-source?
Yes. SGLang is licensed under Apache-2.0 and Xinference under Apache-2.0. Both can be self-hosted at no software cost.
Can I run SGLang and Xinference with Docker?
SGLang: yes. Xinference: yes.
Which is lighter on resources, SGLang or Xinference?
Xinference has the smaller minimum footprint at 8,192 MB of RAM, compared to about 16,384 MB for SGLang. Real-world usage depends on library size, user count, and enabled features.