ExLlama 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.
| Feature | ExLlama | SGLang |
|---|---|---|
| Deploy effort | Read-the-docs project | Under-an-hour setup |
| Health score | 23 · At risk | 99 · Excellent |
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Hard | Hard |
| Min. RAM | 8,192 MB | 16,384 MB |
| Deployment | source | docker, kubernetes, bare-metal |
| GitHub stars | ★ 2,946 | ★ 36,383 |
| First released | 2023 | 2024 |
| Replaces | OpenAI API | OpenAI API |
What are ExLlama and SGLang?
ExLlama
ExLlama is a standalone Python/C++/CUDA implementation for running quantized GPTQ Llama models with low VRAM use on modern GPUs. It is the predecessor to ExLlamaV2 and focuses on fast, memory-efficient local inference.
- Low VRAM GPTQ inference
- CUDA-accelerated
- Standalone library
- Fast token generation
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
ExLlama vs SGLang: key differences
Both projects are written in Python. Licensing differs — MIT for ExLlama versus Apache-2.0 for SGLang. ExLlama 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 2,946. SGLang lists first-class Docker deployment; ExLlama does not.
Why pick each one
Choose ExLlama if…
- Released under the MIT license
- Active community (2.9k GitHub stars)
- Written in Python
Choose SGLang if…
- Very high throughput
- RadixAttention prefix caching
- Vision model support
Watch out for
- Serious GPU required
- Complex tuning options
Frequently asked questions
Is ExLlama or SGLang better?
Neither is universally better. SGLang has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.
Are ExLlama and SGLang free and open-source?
Yes. ExLlama is licensed under MIT and SGLang under Apache-2.0. Both can be self-hosted at no software cost.
Can I run ExLlama and SGLang with Docker?
ExLlama: check the project docs for container support. SGLang: yes.
Which is lighter on resources, ExLlama or SGLang?
ExLlama 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.