Unverified50% confidenceFactExact time
SGLang is a high-performance large model inference and serving framework developed by the UC Berkeley LMSYS team, which optimizes KV Cache reuse efficiency through technologies such as RadixAttention.
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9/2/2026
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UnverifiedSGLang由斯坦福团队开发，专注于结构化输出和复杂推理链的高效执行78% similarUnverifiedSGLang is a high-performance large model inference framework jointly developed by academia and industry, with RadixAttention as its core mechanism.73% similarUnverifiedSGLang 引入了 RadixAttention 技术，在某些基准测试中推理速度可比 vLLM 等主流推理框架提升 2-5 倍66% similarUnverified加州大学伯克利分校的研究团队开源了一套名为FreeToken的推理系统，可在单张工作站显卡上运行753B参数的大模型62% similarUnverifiedPagedAttention已被TensorRT-LLM、LMDeploy、SGLang等主流推理框架广泛借鉴和实现62% similar
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