# SGLang review

> SGLang is an open-source inference and serving framework for large language, vision-language, and diffusion models, with OpenAI-compatible endpoints.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/sglang/

## TL;DR

SGLang is an open-source framework for serving large language, vision-language, and diffusion models. It targets agentic workloads, large-scale serving, and RL rollouts. It runs on NVIDIA, AMD, TPU, Intel, Apple Silicon, and Ascend hardware, and includes optimizations such as disaggregated prefill/decode and speculative decoding.

## SGLang at a glance

- **Category:** [Inference and training](https://index.devtoolwatch.com/categories/inference-and-training/)
- **Maintained by:** sgl-project (organization, [@sgl-project](https://github.com/sgl-project) on GitHub)
- **License:** Apache 2.0
- **Language:** Python, Rust, Cuda
- **Repository:** [github.com/sgl-project/sglang](https://github.com/sgl-project/sglang)
- **Website:** [sglang.io](https://sglang.io)
- **PyPI package:** [sglang](https://pypi.org/project/sglang/) 0.5.22
- **Latest release:** [v0.5.22](https://github.com/sgl-project/sglang/releases/tag/v0.5.22) on GitHub, Oct 19, 2026
- **First published:** Jan 8, 2024

## What SGLang does

- SGLang serves large language, vision-language, and diffusion models for text, multimodal understanding, and image and video generation.
- SGLang exposes standard OpenAI-compatible API endpoints for querying a launched model.
- SGLang includes SGLang Diffusion, a built-in image and video generation engine shipped in the sglang Python package.
- SGLang includes disaggregated prefill/decode, speculative decoding, parallelisms, a zero-overhead scheduler, and optimized GPU kernels.
- SGLang scales from a single accelerator to distributed clusters for large-scale deployments.
- SGLang integrates with RL and post-training frameworks such as verl, slime, AReaL, Miles, and Tunix for rollout generation.
- SGLang supports reasoning and tool-call serving, and LoRA on quantized weights for some models.

### How it works

SGLang is installed with pip or uv, or pulled as a Docker image. You launch a server with one command that points to your model, then query it through OpenAI-compatible endpoints. The Cookbook gives ready-to-run launch commands per model and hardware. SGLang can be deployed and scaled with tools like Ray Serve, NVIDIA Dynamo, llm-d, SMG, and RBG.

## How to install SGLang

**With Docker**

```
docker pull lmsysorg/sglang:latest
```

**With uv**

```
uv pip install --prerelease=allow sglang
```

### Requirements

- Python 3.11 or newer for the PyPI package.
- A supported accelerator or CPU, such as NVIDIA, AMD, Google TPU, Intel, Apple Silicon, Huawei Ascend, or Moore Threads hardware.

### First steps

1. Install SGLang with uv in an activated Python environment, or pull the Docker image.
2. Use the Cookbook to choose your model and hardware and get a ready-to-run launch command.
3. Launch the server with a single command pointing to your model.
4. Send requests to the model using standard OpenAI-compatible endpoints.

## Who SGLang is for

- Teams running large-scale production LLM or multimodal serving across GPUs or clusters.
- RL and post-training pipelines that need fast rollout generation.
- Developers building agentic workloads that need OpenAI-compatible model endpoints.
- Teams serving image and video diffusion models alongside language models.

### Limitations

- SGLang support for AWS Trainium, Cambricon MLU, Qualcomm QAIC, and several other accelerators is still in progress.
- SGLang on Google TPU uses separate projects, SGLang-TorchTPU or SGL-JAX, rather than the main package alone.
- SGLang audio serving for TTS and ASR lives in a separate project, SGLang Omni.
- SGLang model compatibility varies by hardware, so you should check the Cookbook and platform guides first.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #223 overall, #23 in Inference and training, momentum score 41
- Downloads that week: 336.7k (−18% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 371; Aug 16: 410; Aug 23: 531; Aug 30: 2.7k; Sep 6: 397; Sep 13: 350; Sep 20: 298; Sep 27: 322; Oct 4: 237; Oct 11: 308; Oct 18: 856; Oct 25: 372

## SGLang alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [dsh-our-free-model](https://index.devtoolwatch.com/tools/ebony-vinyl-dsh-our-free-model/) | 54.3k | – |
| #2 | [magpie](https://index.devtoolwatch.com/tools/yetone-magpie/) | 26.9k | – |
| #3 | [Strata](https://index.devtoolwatch.com/tools/niko1221-strata/) | 37.1k | – |
| #4 | [edge0](https://index.devtoolwatch.com/tools/edge0-ai-edge0/) | 8.9k | – |
| #5 | [OmniRoute](https://index.devtoolwatch.com/tools/diegosouzapw-omniroute/) | 83.2k | 106k |
| #6 | [CLIProxyAPI](https://index.devtoolwatch.com/tools/router-for-me-cliproxyapi/) | 57.1k | – |

## SGLang FAQ

### Is SGLang free and open source?

Yes. SGLang's source code is on GitHub (sgl-project/sglang) under the Apache 2.0 license, so you can use it for free.

### How do I install SGLang?

Run `docker pull lmsysorg/sglang:latest`. The sglang package needs Python 3.11 or newer. The install section above has the other options from the project's README.

### Is SGLang still maintained?

Yes. The sgl-project/sglang repository was last updated on Oct 25, 2026, and the latest release, v0.5.22 on GitHub, came out on Oct 19, 2026.

### How popular is SGLang?

SGLang has 38.3k GitHub stars as of Nov 1, 2026. It gained 372 new stars in the week of Oct 25–31, 2026, against a 12-week average of 592 a week. Its main package had 336.7k downloads that week, −18% against its own 4-week average. That made it #23 among LLM inference and training tools on the Devtoolwatch Board.

### SGLang vs vLLM: how do they compare?

SGLang and vLLM are both tracked by Devtoolwatch as LLM inference and training tools. SGLang has 38.3k GitHub stars and vLLM has 96k. In the week of Oct 25–31, 2026, SGLang's main package had 336.7k downloads and vLLM's had 677.2k. vLLM is an open-source Python library and engine for high-throughput, memory-efficient inference and serving of large language models. [vLLM review](https://index.devtoolwatch.com/tools/vllm/)

### SGLang vs Ollama: how do they compare?

SGLang and Ollama are both tracked by Devtoolwatch as LLM inference and training tools. SGLang has 38.3k GitHub stars and Ollama has 185.5k. In the week of Oct 25–31, 2026, SGLang's main package had 336.7k downloads and Ollama's had 4M. Ollama is a tool for running and building with open AI models locally or in the cloud on macOS, Windows, and Linux. [Ollama review](https://index.devtoolwatch.com/tools/ollama/)

### What are the best alternatives to SGLang?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

### Is SGLang open source?

Yes. SGLang is an open-source inference framework released under the Apache-2.0 license. It is hosted by LMSYS, a non-profit open-source organization.

### What hardware does SGLang run on?

SGLang runs on NVIDIA GPUs, AMD Instinct GPUs, Google TPUs, Intel GPUs and Xeon CPUs, Apple Silicon Macs via Metal/MLX, Huawei Ascend NPUs, and Moore Threads GPUs. NVIDIA support includes DGX Spark and Jetson Orin.

### Does SGLang provide an OpenAI-compatible API?

Yes. SGLang lets you launch a server pointing to your model and query it through standard OpenAI-compatible endpoints.

### Can SGLang generate images and video?

Yes. SGLang includes SGLang Diffusion, a built-in image and video generation engine in the same repository and sglang Python package.

### Can SGLang run on a Mac?

Yes. SGLang supports Apple Silicon Macs through Metal and MLX.

## Sources

1. [sgl-project/sglang on GitHub](https://github.com/sgl-project/sglang)
2. [SGLang README](https://github.com/sgl-project/sglang/blob/main/README.md)
3. [sglang on PyPI](https://pypi.org/project/sglang/)
4. [SGLang website](https://sglang.io)
5. [Release v0.5.22 (Oct 19, 2026)](https://github.com/sgl-project/sglang/releases/tag/v0.5.22)
6. [Release v0.5.21 (Oct 2, 2026)](https://github.com/sgl-project/sglang/releases/tag/v0.5.21)
7. [Release v0.5.20 (Sep 18, 2026)](https://github.com/sgl-project/sglang/releases/tag/v0.5.20)
8. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)