# AI use cases for SGLang (sgl-project/sglang)

Repo: [sgl-project/sglang](https://github.com/sgl-project/sglang) | Use cases: 3 | Page: https://dedupedai.com/use-cases/sgl-project-sglang/

SGLang is an open-source inference framework for large language, vision-language and diffusion models, optimized for agentic workloads, large-scale serving and RL rollouts. It runs on NVIDIA, AMD, Google TPU, Huawei Ascend and other hardware, ships SGLang Diffusion for image and video generation, and plugs into RL frameworks such as verl and slime for rollout generation. Launch commands per model and hardware live in its Cookbook.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Serve an open model on your own GPUs: https://dedupedai.com/use-cases/sgl-project-sglang/serve-an-open-model-on-your-own-gpus/
- Generate images and video with SGLang Diffusion: https://dedupedai.com/use-cases/sgl-project-sglang/generate-images-and-video-with-sglang-diffusion/
- Use SGLang as the rollout engine for RL post-training: https://dedupedai.com/use-cases/sgl-project-sglang/use-sglang-for-rl-rollouts/

## How to serve an open model on your own GPUs with SGLang

Repo: [sgl-project/sglang](https://github.com/sgl-project/sglang) | Difficulty: Advanced | Time: 1 hour | Basis: From the README

Install SGLang, pick a ready-to-run launch command for your model and hardware, and send a first request.

**Who it is for:** A team moving from a paid API to self-hosted open models.

**The problem:** API bills grow with usage, and some data cannot leave your servers.

**The outcome:** An SGLang server running your chosen model, answering requests on your hardware.

### Steps

1. Pull the image: `docker pull lmsysorg/sglang:latest`, or install with `uv pip install --prerelease=allow sglang`
2. Open the Cookbook and choose your model and hardware to get a launch command
3. Launch the model and follow the Quickstart to send your first request
4. Check the hardware platform guide for your GPU before tuning

### Prompt to paste into your agent

```text
Set up sgl-project/sglang with Docker on this <GPU> machine, use the Cookbook launch command for <model>, send one test request, and report tokens per second.
```

### Caveat

Large models need large GPUs; check the Cookbook's hardware notes for your model first.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sgl-project-sglang/serve-an-open-model-on-your-own-gpus/

## How to generate images and video with SGLang Diffusion with SGLang

Repo: [sgl-project/sglang](https://github.com/sgl-project/sglang) | Difficulty: Advanced | Time: 1 hour | Basis: From the README

Use the diffusion engine built into the same package to serve image and video models.

**Who it is for:** A team that already serves LLMs with SGLang and wants image or video generation on the same stack.

**The problem:** Running a separate stack for diffusion models doubles the ops work.

**The outcome:** Image or video generation served from the SGLang package you already run.

### Steps

1. Install SGLang (the diffusion engine is included in the Python package)
2. Follow the SGLang Diffusion installation guide
3. Launch a diffusion model and send a generation request

### Prompt to paste into your agent

```text
Using sgl-project/sglang's built-in SGLang Diffusion, serve <diffusion model> on this GPU and generate one test image from <prompt>. Report memory use and time per image.
```

### Caveat



Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sgl-project-sglang/generate-images-and-video-with-sglang-diffusion/

## How to use SGLang as the rollout engine for RL post-training with SGLang

Repo: [sgl-project/sglang](https://github.com/sgl-project/sglang) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Plug SGLang into an RL framework such as verl, slime or AReaL to generate rollouts fast.

**Who it is for:** Researchers running reinforcement learning on open models.

**The problem:** Rollout generation is often the bottleneck in RL training loops.

**The outcome:** An RL training setup that uses SGLang for rollout generation.

### Steps

1. Pick an RL framework that integrates SGLang: Miles, slime, AReaL, Tunix or verl
2. Install SGLang alongside it
3. Configure the framework to use SGLang for rollouts
4. Measure rollout throughput against your previous setup

### Prompt to paste into your agent

```text
Configure <verl or slime> to use sgl-project/sglang for rollout generation on <model>, run a short training job, and compare rollout throughput with the default engine.
```

### Caveat



Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/sgl-project-sglang/use-sglang-for-rl-rollouts/