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SGLang

APIyesFREEyesDOCS5/5
SG7.3#13of 33
outMacoutLinux

Ranked in LLM gateways·Free plan

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About

SGLang is an open-source inference framework for serving language, vision-language, and diffusion models. It targets agentic workloads, reinforcement-learning rollouts, and large-scale serving, from one GPU through distributed clusters. Its runtime includes RadixAttention, prefix caching, multi-GPU parallelism, disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler, and optimized GPU kernels. Listed model support covers DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM. SGLang works with Hugging Face and OpenAI APIs, providing OpenAI-compatible endpoints for launched model servers. It can run self-hosted through pip or Docker, with support listed for 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. Integrations include Ray Serve, NVIDIA Dynamo, llm-d, and several reinforcement-learning frameworks. The Apache-2.0 project is free.

Who it is for

SGLang suits teams serving supported models themselves across varied accelerators, from single GPUs to distributed clusters. It also fits agentic and reinforcement-learning workflows needing OpenAI-compatible access.

What is good

  • Open-source Apache-2.0 license
  • OpenAI-compatible and Hugging Face APIs
  • Supports many GPU, TPU, CPU, and NPU platforms
  • Includes caching and multi-GPU runtime features
  • Pip and Docker installation options

What to know first

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

Verdict

SGLang combines a broad hardware and model list with runtime features for high-throughput serving. Teams should account for the stated platform limitation and the absence of fallback routing or virtual API keys.

Compared on LLM gateways

Free plan
Yessglang.io
Deployment mode
dedicatedsglang.io
GPU accelerators
Yessglang.io
Supported model formats
safetensors, PyTorch .bin, GGUF, Mistral nativesglang.io
Batch inference
Yessglang.io

Facts

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

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