SGLang
SGLang is a free, open-source inference framework for serving language, vision-language, and diffusion models.
What SGLang is
SGLang is a free, open-source inference framework for serving language, vision-language, and diffusion models. It targets agentic workloads, reinforcement-learning rollouts, and large-scale serving, with a design for low-latency, high-throughput inference from one GPU to distributed clusters. Runtime components include RadixAttention, prefix caching, and multi-GPU parallelism. The project also lists disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler, and optimized GPU kernels. Supported models include DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM. SGLang works with Hugging Face and OpenAI APIs and provides OpenAI-compatible endpoints for querying a launched model server. It can be installed with Python tooling or run from Docker, and supports self-hosting. Listed hardware spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs, and Moore Threads GPUs. SGLang Diffusion, for image and video generation, is included in the repository and Python package. The project lists integrations for deployment, orchestration, and reinforcement-learning rollout workflows. Its repository identifies the license as Apache-2.0.
Good
- Open-source under the Apache-2.0 license.
- Supports OpenAI-compatible endpoints.
- Runs across a broad listed hardware range.
- Includes image and video generation engine.
- Supports self-hosted deployment.
Watch out
- Prefill context parallelism is unavailable on HIP, NPU, and MUSA.
- Fallback routing is not supported.
- Virtual API keys are not supported.
Who it suits
SGLang suits developers and teams serving supported models who need an inference framework for agentic workloads, rollouts, or larger deployments. It is intended for users comfortable with installation and operation through Python tooling or Docker.
Verdict
SGLang offers a broad inference toolkit with multiple hardware targets, API compatibility, and self-hosting. Its release notes identify a prefill context-parallelism gap on HIP, NPU, and MUSA.
Plans and prices
From SGLang's own pricing page, checked 8 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| SGLang | Free | Open-source inference framework · install with pip or Docker |
The facts
Each one read from SGLang's own pages, with the date it was checked.
What it does
- API
- SGLang provides standard OpenAI-compatible endpoints for querying a launched model server.checked 8 Oct 2026
- API compatibility
- SGLang is compatible with Hugging Face and OpenAI APIs, and its site describes OpenAI-compatible endpoints.checked 8 Oct 2026
- Caching
- The project describes hierarchical KV caching across GPU memory, host memory, and external storage through ecosystem projects including HiCache, Mooncake, and LMCache.checked 8 Oct 2026
- Community support
- The documentation directs technical questions and development discussions to the SGLang Slack community.checked 8 Oct 2026
- Diffusion
- SGLang Diffusion is a built-in image and video generation engine included in the repository and Python package.checked 8 Oct 2026
- Ecosystem integrations
- The project lists integrations with the RL frameworks Miles, slime, AReaL, Tunix, and verl for rollout generation.checked 8 Oct 2026
- Hardware
- The project lists support for NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs, and Moore Threads GPUs.checked 8 Oct 2026
- Hardware support
- The project README lists NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs, and Moore Threads GPUs.checked 8 Oct 2026
- Install
- Users can install SGLang with pip or run it from a Docker image.checked 8 Oct 2026
- Installation
- The project can be installed with Python tooling or run from a Docker image.checked 8 Oct 2026
- Integrations
- The README lists deployment and orchestration integrations including Ray Serve, NVIDIA Dynamo, and llm-d.checked 8 Oct 2026
- License
- The GitHub repository identifies the project license as Apache-2.0.checked 8 Oct 2026
- Model support
- The product site lists support for DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM models.checked 8 Oct 2026
- Notable limitation
- The October 2, 2026 release notes state that prefill context parallelism is unavailable on HIP, NPU, and MUSA until those platforms are ported.checked 8 Oct 2026
- Optimizations
- The product site lists disaggregated prefill and decode, speculative decoding, parallelism, a zero-overhead scheduler, and optimized GPU kernels.checked 8 Oct 2026
- Performance
- SGLang is designed for low-latency, high-throughput inference from a single GPU to distributed clusters.checked 8 Oct 2026
- Purpose
- SGLang is an open-source inference framework for serving large language, vision-language, and diffusion models.checked 8 Oct 2026
- Runtime features
- Its runtime includes RadixAttention, prefix caching, and multi-GPU parallelism.checked 8 Oct 2026
- Security
- The repository identifies its license as Apache-2.0.checked 8 Oct 2026
- Support
- The project points users to GitHub issues, Slack, Discord, and community discussions for questions and help.checked 8 Oct 2026
- Use cases
- The framework is optimized for agentic workloads, reinforcement-learning rollouts, and large-scale serving.checked 8 Oct 2026
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