English(EN) Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation

Dooly工具通过降低分析成本简化大模型推理模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了Dooly，一种旨在提高大模型（LLM）推理模拟效率的新型分析工具。传统的模拟器需要为每种配置进行广泛的重新分析，这非常耗时。Dooly通过理解模型配置和操作之间的结构关系来解决这个问题，使其能够进行配置无关和冗余感知的分析。这种方法在保持高模拟精度的同时，显著减少了分析所需的计算资源。 AI

影响该工具可以通过提高不同配置评估的效率，加速大模型的开发和部署。

排序理由该集群包含一篇详细介绍大模型推理模拟新工具的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍大模型推理模拟新工具的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Joon Ha Kim, Geon-Woo Kim, Anoop Rachakonda, Daehyeok Kim · 2026-10-23 04:00

Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation

arXiv:2605.07985v3 Announce Type: replace-cross Abstract: Selecting the optimal LLM inference configuration requires evaluation across hardware, serving engines, attention backends, and model architectures, since no single choice performs best across all workloads. Profile-based …

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Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation

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