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English(EN) FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving

FluidPD 系统通过原地弹性增强 LLM 服务

研究人员开发了 FluidPD,一个旨在提高大型语言模型 (LLM) 服务效率和可靠性的新系统。FluidPD 通过引入原地弹性来应对 LLM 推理的预填充和解码阶段之间不断变化的需求的挑战。该系统使用 FluidToken 和 FluidRole 等机制,无需额外硬件即可动态调整资源和工作角色,从而在满足延迟服务水平目标 (SLO) 方面取得显著改进。 AI

影响 通过动态管理资源以满足延迟 SLO 来提高 LLM 服务效率和可靠性。

排序理由 该项目是一篇研究论文,详细介绍了一个用于 LLM 服务的新系统。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

FluidPD 系统通过原地弹性增强 LLM 服务

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该项目是一篇研究论文,详细介绍了一个用于 LLM 服务的新系统。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kartik Ramesh, Kaidi Fu, Zihan Zheng, Jiahuan Yu, Fabio Oliveira, Carlos Costa, Minjia Zhang ·

    FluidPD:用于 SLO 感知的预填充-解码分离式 LLM 服务中的就地弹性

    arXiv:2610.06917v1 Announce Type: new Abstract: Prefill-decode disaggregation is becoming a common architecture for LLM serving because it separates two phases with distinct execution patterns and SLO objectives. Existing systems typically combine a fixed prefill/decode worker ra…