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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
High‑performance serving framework for LLMs and vision‑language models.
SGLang provides low‑latency, high‑throughput inference for large language and vision‑language models, scaling from a single GPU to distributed clusters with extensive hardware and model compatibility.
SGLang is a high‑performance serving engine designed for developers, enterprises, and research labs that need fast, scalable inference of large language models (LLMs) and vision‑language models (VLMs). It delivers low latency and high throughput across a spectrum of deployments, from a single GPU workstation to massive multi‑node clusters.
The framework features RadixAttention prefix caching, a zero‑overhead CPU scheduler, speculative decoding, continuous batching, and support for quantization (FP4/FP8/INT4) and multi‑LoRA batching. It runs on NVIDIA, AMD, Intel, Google TPU, and Ascend hardware, and integrates seamlessly with Hugging Face and OpenAI‑compatible APIs. SGLang’s flexible Python frontend enables chained generation calls, advanced prompting, and multimodal inputs, while its active community and extensive documentation accelerate adoption in production environments.
Deployed on over 300,000 GPUs worldwide, SGLang powers token generation for leading cloud providers, universities, and AI‑focused enterprises, establishing it as a de‑facto standard for LLM inference.
When teams consider SGLang, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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SGLang is released under the Apache‑2.0 license.
The primary interface is Python, with core runtime components in C/C++ and CUDA.
Yes, it supports AMD Instinct MI300X, MI355, and other AMD accelerators.
SGLang works with most Hugging Face model formats and OpenAI‑compatible APIs.
Join the project’s Slack, attend bi‑weekly development meetings, and consult the documentation and blog posts.
Project at a glance
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