What is sglang?

SGLang is a tool that helps companies run large AI models faster and more efficiently. If you've built an AI-powered feature like a chatbot or a document analyzer, you need to serve that model to your users. As your user base grows, generating text, images, or video can become slow and expensive. SGLang acts as a high-performance engine that powers these AI models, making them respond more quickly and handle many simultaneous users without breaking a sweat. At a high level, when an AI model generates text, it does so in small chunks called tokens. The software managing this process has to juggle requests from many users at once. This project uses clever memory tricks, like remembering parts of previous questions to speed up new answers, and smart scheduling to pack as many tasks as possible onto a single GPU. It also allows work to be split across massive clusters of hardware, which is how very large AI models are able to respond to users in real time. This tool is built for teams who are deploying AI to real users at scale. For example, a startup building an AI coding assistant, or a company like xAI running a chatbot for millions of people, needs their model to respond instantly. They would use this infrastructure to handle the heavy traffic. It supports major open models like Llama and DeepSeek, and works across different types of hardware from companies like NVIDIA, AMD, Intel, and Google. The project is notable for its focus on raw speed and hardware optimization. It has become a widely adopted industry standard, reportedly powering over 400,000 GPUs and generating trillions of tokens daily. While it is incredibly powerful, the tradeoff is that it addresses deep infrastructure challenges that only become relevant once you have a working model and need to serve it to a large audience.