English(EN) Pacing Branch Parallelism in LLM Serving

新的LLM服务方法PACE将吞吐量提升1.77倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-04 05:00

研究人员开发了一种名为PACE（Pacing Admission Controller for Execution）的新LLM服务方法，该方法优化了请求内并行性的使用。与急切执行或固定容量方法不同，PACE根据批次的当前剩余容量动态地接纳额外分支，从而降低延迟并提高吞吐量。该方法在Qwen3-32B模型上展示了1.77倍的有效吞吐量提升，并在其他大型模型上取得了相当的收益，同时保持了99%的服务水平目标（SLO）达成率。 AI

影响优化LLM服务效率，可能为AI应用带来更快的响应时间和更高的吞吐量。

排序理由详细介绍LLM服务新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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本文如何被排名

Signal score: 39 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍LLM服务新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: infra, paper; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Swapnil Gandhi, Siva Kumar Sastry Hari, William J. Dally, Christos Kozyrakis · 2026-11-04 05:00

LLM服务中的并行分支优化

arXiv:2605.06914v2 Announce Type: replace-cross Abstract: Recent methods expose intra-request parallelism in LLM outputs, allowing independent branches to decode concurrently. Existing serving systems execute these branches eagerly or under fixed caps. We show that both are britt…

本文如何被排名

报道来源 [1]

LLM服务中的并行分支优化

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