yongwww/sglang

SGLang is a tool that helps you run large language models and vision language models faster and more efficiently. If you've ever tried serving an AI model, making it available to answer questions or generate text for users, you know it can be slow and expensive. This project tackles that problem by speeding up how models process requests and giving you more control over how they generate output. At a high level, it works as a layer between your model and the people or applications sending it requests. It uses several optimization techniques to speed things up: it caches parts of conversations so repeated questions get answered faster, it batches multiple requests together intelligently, and it can split work across multiple GPUs. It also includes a frontend interface that lets you write applications with chained prompts, branching logic, and multi-modal inputs (like images alongside text). The result is that more users can query your model simultaneously with lower latency. The main users are teams deploying AI models in production. For example, a company building a chatbot powered by DeepSeek-R1 or Llama could use this to handle thousands of concurrent users without each person waiting seconds for a response. The project notes that it's already deployed at large scale, generating trillions of tokens daily, with adoption from companies like Cursor, LinkedIn, xAI, NVIDIA, and AMD. It supports a wide range of popular models including the Llama, Gemma, Mistral, Qwen, DeepSeek, and LLaVA families. What's notable is how it pairs backend speed with frontend flexibility. Many serving tools focus only on making inference faster. This one also gives you a way to program complex model interactions, chaining multiple generation calls together, controlling flow, and producing structured outputs like JSON. It's also been quick to support new models, offering day-one support for DeepSeek V3 and R1 on both NVIDIA and AMD hardware. The project is open-source and part of the PyTorch ecosystem, with an active community contributing to its development.