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SGLang
What it is
SGLang is a free, open-source inference framework for serving large language, vision-language, and diffusion models. It is aimed at agentic workloads, reinforcement-learning rollouts, and large-scale serving, with a design for low-latency, high-throughput inference from one GPU through distributed clusters. Runtime features include RadixAttention, prefix caching, and multi-GPU parallelism; listed optimizations also include disaggregated prefill and decode, speculative decoding, and optimized GPU kernels. The framework supports models from DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM, and works with Hugging Face and OpenAI APIs. Users can query launched model servers through OpenAI-compatible endpoints. SGLang can be installed with Python tooling or run from Docker, and supports self-hosted deployment. Its hardware list includes NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs, and Moore Threads GPUs. The project also includes SGLang Diffusion for image and video generation, plus integrations for rollout, deployment, and orchestration. It uses the Apache-2.0 license. The October 2, 2026 release notes state that prefill context parallelism is not available on HIP, NPU, or MUSA until ported.
For and against
For
- Free, open-source framework under Apache-2.0
- Supports inference from one GPU to distributed clusters
- Offers OpenAI-compatible endpoints
- Includes a built-in image and video generation engine
Against
- Prefill context parallelism is not yet available on HIP, NPU, or MUSA
The verdict
SGLang offers a free framework with model serving, caching, parallelism, and self-hosted deployment options. Teams using HIP, NPU, or MUSA should account for the stated prefill context parallelism limitation.
Who it suits
SGLang suits technical teams serving language, vision-language, or diffusion models, including teams working on agentic workloads or reinforcement-learning rollouts. Its self-hosted deployment and broad hardware list may also suit organizations operating their own inference environments.
Plans and prices
SGLang
Open-source inference framework · install with pip or Docker
Read 8 Oct 2026 · github.com
On the record
- API
- SGLang provides standard OpenAI-compatible endpoints for querying a launched model server.sglang.io · read 8 Oct 2026
- API compatibility
- SGLang is compatible with Hugging Face and OpenAI APIs, and its site describes OpenAI-compatible endpoints.docs.sglang.io · read 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.github.com · read 8 Oct 2026
- Community support
- The documentation directs technical questions and development discussions to the SGLang Slack community.docs.sglang.io · read 8 Oct 2026
- Diffusion
- SGLang Diffusion is a built-in image and video generation engine included in the repository and Python package.github.com · read 8 Oct 2026
- Ecosystem integrations
- The project lists integrations with the RL frameworks Miles, slime, AReaL, Tunix, and verl for rollout generation.github.com · read 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.github.com · read 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.github.com · read 8 Oct 2026
- Install
- Users can install SGLang with pip or run it from a Docker image.github.com · read 8 Oct 2026
- Installation
- The project can be installed with Python tooling or run from a Docker image.github.com · read 8 Oct 2026
- Integrations
- The README lists deployment and orchestration integrations including Ray Serve, NVIDIA Dynamo, and llm-d.github.com · read 8 Oct 2026
- License
- The GitHub repository identifies the project license as Apache-2.0.github.com · read 8 Oct 2026
- Model support
- The product site lists support for DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM models.sglang.io · read 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.sglang.io · read 8 Oct 2026
- Optimizations
- The product site lists disaggregated prefill and decode, speculative decoding, parallelism, a zero-overhead scheduler, and optimized GPU kernels.sglang.io · read 8 Oct 2026
- Performance
- The maker describes production inference from a single GPU to distributed clusters, targeting low latency and high throughput.sglang.io · read 8 Oct 2026
- Purpose
- SGLang is an open-source inference framework for serving large language, vision-language, and diffusion models.github.com · read 8 Oct 2026
- Runtime features
- Its runtime includes RadixAttention, prefix caching, and multi-GPU parallelism.docs.sglang.io · read 8 Oct 2026
