SGLang
High-performance open-source serving framework for LLMs and multimodal models
SGLang is an open-source LLM serving framework focused on throughput and structured generation. It pairs a fast runtime with a frontend language for expressing complex prompting patterns (multi-call workflows, structured outputs, tool use) so they execute efficiently on the backend.
The runtime uses RadixAttention to share KV cache across requests with overlapping prefixes, which speeds up multi-turn chat and few-shot prompting. It supports continuous batching, speculative decoding, structured output (constrained JSON), and tensor/pipeline/expert parallelism for large models. Apache 2.0 licensed, Python-based, with an OpenAI-compatible HTTP server.
SGLang is widely used as the rollout backend in RL post-training stacks (AReaL, slime, verl, Tunix) and runs production inference at companies including xAI, LinkedIn, and ByteDance. Their docs report adoption across over 400,000 GPUs worldwide.
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