GitHub - sgl-project/sglang: SGLang is a fast serving framework for large language models and vision language models. (original) (raw)

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News

• [2025/05] 🔥 Deploying DeepSeek with PD Disaggregation and Large-scale Expert Parallelism on 96 H100 GPUs (blog).
• [2025/03] Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X (AMD blog)
• [2025/03] SGLang Joins PyTorch Ecosystem: Efficient LLM Serving Engine (PyTorch blog)
• [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).
• [2024/07] v0.2 Release: Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog). More
• [2025/02] Unlock DeepSeek-R1 Inference Performance on AMD Instinct™ MI300X GPU (AMD blog)
• [2024/10] The First SGLang Online Meetup (slides).
• [2024/09] v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
• [2024/02] SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
• [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
• [2024/01] SGLang powers the serving of the official LLaVA v1.6 release demo (usage).

About

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, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor parallelism, pipeline parallelism, expert parallelism, structured outputs, chunked prefill, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
• 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.

Getting Started

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

Benchmark and Performance

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

Roadmap

Development Roadmap (2025 H1)

Adoption and Sponsorship

SGLang has been deployed at large scale, generating trillions of tokens in production every day. It is trusted and adopted by a broad range of leading enterprises and institutions, including xAI, NVIDIA, AMD, Google Cloud, Oracle Cloud, LinkedIn, Cursor, Voltage Park, Atlas Cloud, DataCrunch, Baseten, Nebius, Novita, InnoMatrix, RunPod, Stanford, UC Berkeley, UCLA, ETCHED, Jam & Tea Studios, Hyperbolic, as well as major technology organizations across North America and Asia. As an open-source LLM inference engine, SGLang has become the de facto standard in the industry, with production deployments running on over 100,000 GPUs worldwide.

Contact Us

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

Acknowledgment

We learned the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL.