English(EN) FleetSieve: Decision-Critical Profiling for SLO-Aware LLM Fleet Configuration

FleetSieve 通过智能剖析优化 LLM 舰队配置

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-21 04:00

研究人员开发了 FleetSieve，一种用于优化大型语言模型 (LLM) 舰队配置的新颖方法。该系统智能地选择要剖析的配置，旨在减少确定最佳张量并行度和下副本数量所需的计算资源。FleetSieve 联合建模容量和尾部延迟，当决策差距足够小时停止剖析，并在 NVIDIA H100 GPU 上展示了相对于随机剖析的节省效果。 AI

影响优化 LLM 服务资源分配，可能降低运营成本并提高效率。

排序理由该项目是一篇研究论文，详细介绍了一种新的 LLM 舰队配置方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Huang Cheng, Scott Zhang, Aubert Li · 2026-08-21 04:00

FleetSieve：面向 SLO 感知的 LLM 集群配置的决策关键分析

arXiv:2608.19659v1 Announce Type: new Abstract: Choosing tensor-parallel (TP) degrees and replica counts for an LLM serving fleet is difficult because performance is not monotonic in TP and the feasible choice can change with load. Exhaustive profiling resolves this uncertainty, …

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报道来源 [1]

FleetSieve：面向 SLO 感知的 LLM 集群配置的决策关键分析

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