SGLang: Fast Inference for LLMs and Multimodal Models

What SGLang is

SGLang is an open-source inference framework for large language models, vision-language models and diffusion models. The README positions it for three workloads in particular: agentic applications, reinforcement-learning rollouts, and large-scale serving. Its documentation describes it as a framework meant for production-level serving, designed for low latency and high throughput on anything from a single GPU to large distributed clusters.

It is aimed at teams that host their own models: ML platform engineers standing up an OpenAI-compatible endpoint for an open-weight model, researchers who need fast rollout generation inside an RL training loop, and infrastructure teams serving many models across mixed hardware. The project is hosted by LMSYS, a non-profit open-source organization, and the main package is published on PyPI as sglang.

SGLang is not only a text engine. The repository also contains SGLang Diffusion, a built-in image and video generation engine shipped in the same Python package, and the wider ecosystem includes SGLang Omni for text-to-speech and speech recognition models.

How it works

At its core SGLang is a model server. The quickstart starts a server for a Hugging Face model ID, waits for it to report ready, then verifies it with a standard /v1/chat/completions request, so clients talk to it through an OpenAI-compatible API. The docs homepage also lists Hugging Face compatibility and broad model coverage, naming Llama, Qwen and DeepSeek.

Performance comes from the runtime. The docs name RadixAttention, prefix caching and multi-GPU parallelism as the main techniques. Prefix caching matters for agentic and multi-turn traffic, where many requests share long system prompts, tool definitions or conversation history, because shared prefixes do not need to be recomputed on every call. For KV cache management beyond GPU memory, the ecosystem table lists HiCache for hierarchical caching across GPU memory, host memory and external storage, with integrations for Mooncake and LMCache to transfer and reuse cache in distributed inference.

The README's acknowledgment section notes that the project learned from and reused code from Guidance, vLLM, LightLLM, FlashInfer, Outlines and LMQL.

Key features

• OpenAI-compatible server: launch a model and call it with the same chat completions request format existing clients already use.
• RadixAttention and prefix caching: reuse computation across requests that share a prompt prefix.
• Multi-GPU parallelism: scale a single model across several accelerators.
• Broad hardware support: NVIDIA (A100, H100/H200, B200/B300, GB200/GB300, select RTX cards, DGX Spark, Jetson Orin), AMD Instinct MI300X through MI355X, Google TPU v6e and v7, Intel Arc GPUs and Xeon CPUs, Apple Silicon via Metal and MLX, Huawei Ascend NPUs, and Moore Threads GPUs.
• Diffusion built in: SGLang Diffusion handles image and video generation from the same package.
• Speculative decoding tooling: the companion SpecForge project trains draft models for speculative decoding and deploys them with SGLang.
• RL rollout engine: training frameworks including Miles, slime, AReaL, Tunix and verl integrate SGLang for rollout generation.
• Cookbook of launch recipes: cookbook.sglang.io gives ready-to-run launch commands by model and hardware.

On scale, the docs state that SGLang generates trillions of tokens each day across more than 400,000 GPUs worldwide. That is the project's own figure, but it signals that the engine is used well beyond research settings.

Getting started

The README offers two install paths. The Docker image bundles SGLang and its dependencies:

docker pull lmsysorg/sglang:latest

Or install into an activated Python environment with uv:

uv pip install --prerelease=allow sglang

From there, the quickstart walks through launching a model by its Hugging Face ID and sending a first request. Its commands target Linux with an NVIDIA GPU and a CUDA 13-compatible driver, and the Docker route expects the NVIDIA Container Toolkit. For anything beyond a small test model, start from the Cookbook, which picks launch arguments for a specific model and hardware combination rather than leaving you to tune flags by hand.

For contributors, the README recommends the lmsysorg/sglang:dev image and an editable install from the repository root:

pip install -e "python"

Use cases

• Self-hosted model APIs: serve an open-weight model behind an OpenAI-compatible endpoint for internal applications.
• Agent backends: long, repeated system prompts and tool schemas are exactly the traffic prefix caching is meant for.
• RL post-training: use SGLang as the rollout engine inside frameworks such as verl, slime or AReaL.
• Non-NVIDIA deployments: run on AMD Instinct, Google TPU, Ascend NPU, Intel or Apple Silicon hardware with one serving stack.
• Image and video generation: serve diffusion models with SGLang Diffusion alongside text models.
• Distributed serving: pair SGLang with orchestration projects named in the README, including llm-d, Ray Serve, NVIDIA Dynamo, RBG and SMG, for routing, load balancing and cluster orchestration.
• Learning inference internals: Mini-SGLang, zero-to-sglang and a DeepLearning.AI short course teach engine design through code.

How it compares

The obvious comparison is vLLM, the other widely used open-source LLM serving engine; the SGLang README credits vLLM among the projects it learned from. Both serve open-weight models behind OpenAI-compatible APIs and support a wide range of hardware. Differences tend to show up in specific runtime techniques, model support timing and performance on particular workloads, and they shift from release to release, so the reliable way to choose is to benchmark both on your own models, hardware and traffic shape. Several orchestration layers, including llm-d and Ray Serve, support SGLang as a backend, so switching engines does not necessarily mean switching the rest of the stack.

SGLang is a different layer from AI gateways and model routers. Those sit in front of one or more inference servers; SGLang is the server that actually runs the weights.

Things to know before adopting

• Pre-release installs: the documented uv command passes --prerelease=allow, so dependency resolution may pull pre-release packages. Pin versions for production images.
• Hardware support varies by platform: NVIDIA is the default target in the quickstart, while other platforms have dedicated guides. Integrations for AWS Trainium, Cambricon MLU, Qualcomm and others are listed as in progress, not finished.
• Model compatibility is per model: check the Cookbook and platform guides for your specific model and hardware before planning capacity.
• Operational surface: production serving at scale typically involves an orchestration layer for routing and autoscaling, which SGLang leaves to companion projects.
• Contributing: contributions are expected to include tests, a pre-commit run --all-files pass, and benchmarks or accuracy evaluations where relevant. Larger changes should be discussed first in a GitHub issue or on Slack. The README also says long-term active contributors can apply for sponsored access to coding agents such as Cursor, Claude Code or OpenAI Codex.
• Governance: the project is hosted by LMSYS, publishes a public roadmap at roadmap.sglang.io, and lists an email contact for enterprise deployment and consulting inquiries.

Project activity

As of October 2026 the repository has roughly 36,700 stars. It was created on 8 January 2024, is written primarily in Python, and is licensed under Apache-2.0. The source is at github.com/sgl-project/sglang, the homepage is sglang.io, and documentation lives at docs.sglang.io. Release announcements and technical write-ups appear on the LMSYS blog, and the project runs a Slack workspace plus regular meetups, developer meetings and office hours.