SGLang 是专为大模型设计的高性能推理与服务框架，由 UC Berkeley 与英伟达等团队打造，以 RadixAttention 技术实现共享前缀加速，吞吐与延迟显著优于传统方案。支持 Llama、Qwen、DeepSeek 等主流模型与 OpenAI 兼容 API，是 2025-2026 年最热门的开源推理引擎之一。 🔗 文章: /post/ai-model-deployment-meaning-what-it-is-and-how-to-do-it-in-2026-20260723 | GitHub Stars: 31k+
SGLang is a high-performance inference and serving framework for LLMs built by UC Berkeley and NVIDIA collaborators. Its RadixAttention technology accelerates shared prefixes, beating traditional stacks on throughput and latency. Supports Llama, Qwen, DeepSeek and OpenAI-compatible APIs — one of the hottest open-source inference engines of 2025-2026. 🔗 Article: /post/ai-model-deployment-meaning-what-it-is-and-how-to-do-it-in-2026-20260723 | GitHub Stars: 31k+