---
id: 20260401-T0-13
title: "大规模AI系统吞吐量优化新策略"
title_en: "New Strategies for Throughput Optimization in Large-Scale AI"
url: https://ai.daily.yangsir.net/daily/20260401-T0-13
issue_date: 2026-04-01
publish_date: 2026-03-31T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.26823
---

# 大规模AI系统吞吐量优化新策略

arXiv研究提出，大规模AI系统开发中，吞吐量优化应作为战略杠杆。通过数据加载器和内存剖析的创新，研究团队在LLM训练中实现了显著性能提升，为降低大模型训练成本提供新思路。

## English Version

**New Strategies for Throughput Optimization in Large-Scale AI**

An arXiv study repositions throughput optimization as a strategic lever in large-scale AI systems. Innovations in dataloader and memory profiling significantly improved LLM training performance, offering cost reduction insights.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.26823)

**详情页**：https://ai.daily.yangsir.net/daily/20260401-T0-13

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