SGLang Overview
SGLang is a high-performance runtime engine and structured generation language for Large Language Models (LLMs). It offers a flexible Python interface and integrates smoothly with models from popular ecosystems like Hugging Face. At its core is RadixAttention, a novel GPU kernel that efficiently manages the KV Cache for complex generation programs involving loops and conditionals. This innovation enables massive parallelization of different program branches, eliminates redundant computation, and delivers state-of-the-art performance for structured prompting tasks.
SGLang also features a simple yet powerful language front-end, allowing developers to mix text generation, control flow, and external tools within a single program. These programs are aggressively optimized by the SGLang compiler and executed by a runtime featuring continuous batching and an efficient, fragmentation-free memory pool. This integrated approach simplifies the development of complex LLM applications and unlocks significant speedups, especially for tasks like multi-turn conversation, in-context learning, and agentic workflows.
For more information about SGLang, including documentation and examples, see: