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SGLang

sglang.io

Free plan macOSLinuxAPISelf-hosted
Visit sglang.io
Spec score 8.4 #11 of 33 in LLM gateways

What it is

SGLang is a free, open-source inference framework for serving large language, vision-language and diffusion models. It targets agentic workloads, reinforcement-learning rollouts and large-scale serving, from one GPU to distributed clusters. Runtime features include RadixAttention, prefix caching and multi-GPU parallelism. Listed optimizations include 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 exposes OpenAI-compatible endpoints. It installs with pip or Docker and supports self-hosted deployment. Hardware support spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs and Moore Threads GPUs. SGLang Diffusion adds image and video generation. The Apache-2.0 license permits open-source use.

Spec sheet

Purpose
SGLang is an open-source inference framework for serving large language, vision-language, and diffusion models.Read 8 Oct 2026 · github.com
API
SGLang provides standard OpenAI-compatible endpoints for querying a launched model server.Read 8 Oct 2026 · sglang.io
Integrations
The README lists deployment and orchestration integrations including Ray Serve, NVIDIA Dynamo, and llm-d.Read 8 Oct 2026 · github.com
Security
The repository identifies its license as Apache-2.0.Read 8 Oct 2026 · github.com
Support
The project points users to GitHub issues, Slack, Discord, and community discussions for questions and help.Read 8 Oct 2026 · sglang.io
License
The GitHub repository identifies the project license as Apache-2.0.Read 8 Oct 2026 · github.com
API compatibility
SGLang is compatible with Hugging Face and OpenAI APIs, and its site describes OpenAI-compatible endpoints.Read 8 Oct 2026 · docs.sglang.io
Caching
The project describes hierarchical KV caching across GPU memory, host memory, and external storage through ecosystem projects including HiCache, Mooncake, and LMCache.Read 8 Oct 2026 · github.com
Community support
The documentation directs technical questions and development discussions to the SGLang Slack community.Read 8 Oct 2026 · docs.sglang.io
Diffusion
SGLang Diffusion is a built-in image and video generation engine included in the repository and Python package.Read 8 Oct 2026 · github.com
Ecosystem integrations
The project lists integrations with the RL frameworks Miles, slime, AReaL, Tunix, and verl for rollout generation.Read 8 Oct 2026 · github.com
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.Read 8 Oct 2026 · github.com
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.Read 8 Oct 2026 · github.com
Install
Users can install SGLang with pip or run it from a Docker image.Read 8 Oct 2026 · github.com
Installation
The project can be installed with Python tooling or run from a Docker image.Read 8 Oct 2026 · github.com
Model support
The product site lists support for DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM models.Read 8 Oct 2026 · sglang.io
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.Read 8 Oct 2026 · sglang.io
Optimizations
The product site lists disaggregated prefill and decode, speculative decoding, parallelism, a zero-overhead scheduler, and optimized GPU kernels.Read 8 Oct 2026 · sglang.io
Performance
The maker describes production inference from a single GPU to distributed clusters, targeting low latency and high throughput.Read 8 Oct 2026 · sglang.io
Runtime features
Its runtime includes RadixAttention, prefix caching, and multi-GPU parallelism.Read 8 Oct 2026 · docs.sglang.io
Use cases
The framework is optimized for agentic workloads, reinforcement-learning rollouts, and large-scale serving.Read 8 Oct 2026 · github.com

Plans and prices

SGLang Free Open-source inference framework · install with pip or Docker Read 8 Oct 2026 · github.com

Every SGLang plan and price →

Strong points and limits

Strong points

  • Free Apache-2.0 open-source framework
  • OpenAI-compatible model-server endpoints
  • Single-GPU through distributed-cluster serving
  • Broad accelerator support
  • Built-in image and video diffusion

Limits

  • Prefill context parallelism unavailable on HIP, NPU and MUSA
  • Self-hosted deployment requires infrastructure
  • No fallback routing
  • No virtual API keys

Who it suits

SGLang suits developers and infrastructure teams serving language, vision-language or diffusion models on varied accelerators. It is also aimed at agentic and reinforcement-learning workloads.

Verdict

SGLang offers a broad, self-hosted inference stack with caching, parallelism, API compatibility and many hardware targets. Platform-specific prefill support remains incomplete on HIP, NPU and MUSA.

Where Specifiction places it

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