新型代理ATTUNE将LLM训练吞吐量提升高达2.6倍

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

一篇新研究论文介绍了一种名为ATTUNE的代理系统，旨在优化大型语言模型（LLM）的训练吞吐量。ATTUNE作为一个编码代理，能够智能地调整训练配方，优化GPU拆分和内存-计算权衡等参数。在亚马逊的生产配方上，使用NVIDIA H100、H200和B200集群，ATTUNE的吞吐量平均提高了1.7倍，并且在性能上超越了NVIDIA自身的基准配方，最高提升17%。该系统在未用于其开发的模型上也取得了显著的提升，优于现有的自动化调优器。 AI

影响优化LLM训练效率，可能降低计算成本并加速模型开发周期。

排序理由研究论文，详细介绍了一种用于LLM训练优化的新型代理系统。[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) · Leo Fan, Hongyang Wang, Wenbo Chen, Jingguo Liu, Xian Li, Xuan Wang, Ruochen Wen, Yiming Yan, Hao Wu, Yichuan Li, Qian Qu, Zheshen Wang, Benoit Dumoulin · 2026-10-23 04:00

ATTUNE: Agentic Throughput Tuning for LLM Training

arXiv:2610.17528v2 Announce Type: replace-cross Abstract: The throughput of an LLM training job depends on its recipe: how the model is split across GPUs, how memory is traded for compute, and which kernels and communication overlaps it uses. The best recipe changes with every mo…

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报道来源 [1]

ATTUNE: Agentic Throughput Tuning for LLM Training

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