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• [2025/01] 🔥 SGLang provides day one support for DeepSeek V3/R1 models on NVIDIA and AMD GPUs with DeepSeek-specific optimizations. (instructions,AMD blog,10+ other companies)
• [2024/12] 🔥 v0.4 Release: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs (blog).
• [2024/09] v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
• [2024/07] v0.2 Release: Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).

SGLang is a fast serving framework for large language models and vision language models.It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.The core features include:

• Fast Backend Runtime: Provides efficient serving with RadixAttention for prefix caching, zero-overhead CPU scheduler, continuous batching, token attention (paged attention), speculative decoding, tensor parallelism, chunked prefill, structured outputs, and quantization (FP8/INT4/AWQ/GPTQ).
• Flexible Frontend Language: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
• Extensive Model Support: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte, mcdse) and reward models (Skywork), with easy extensibility for integrating new models.
• Active Community: SGLang is open-source and backed by an active community with industry adoption.

• Install SGLang
• Quick Start
• Backend Tutorial
• Frontend Tutorial
• Contribution Guide

Learn more in the release blogs:v0.2 blog,v0.3 blog,v0.4 blog

Development Roadmap (2025 H1)

The project has been deployed to large-scale production, generating trillions of tokens every day.It is supported by the following institutions: AMD, Atlas Cloud, Baseten, Cursor, DataCrunch, Etched, Hyperbolic, Iflytek, Jam & Tea Studios, LinkedIn, LMSYS, Meituan, Nebius, Novita AI, NVIDIA, RunPod, Stanford, UC Berkeley, UCLA, xAI, and 01.AI.

For enterprises interested in adopting or deploying SGLang at scale, including technical consulting, sponsorship opportunities, or partnership inquiries, please contact us atcontact@sglang.ai.

We learned the design and reused code from the following projects:Guidance,vLLM,LightLLM,FlashInfer,Outlines, andLMQL. Please cite the paper,SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful.