---
id: 20260829-T0-05
title: "智能网络参与分布式训练：带宽利用率提升2倍"
title_en: "Intelligent Network Accelerates Distributed Training with 2x Bandwidth Utilization"
url: https://ai.daily.yangsir.net/daily/20260829-T0-05
issue_date: 2026-08-29
publish_date: 2026-08-28T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.26453
---

# 智能网络参与分布式训练：带宽利用率提升2倍

一篇arXiv论文提出让网络本身成为分布式训练的“主动参与者”，而不仅仅是被动的数据传输管道。该方法在交换机层面实现参数聚合与梯度压缩，从而减少跨广域网（WAN）传输的数据量。实验显示，在100Gbps链路上，训练吞吐量比传统方案提升了约2倍，带宽利用率也从约60%提升至95%。该技术将显著降低多数据中心联合训练的成本，为企业级大模型训练提供新路径。

## English Version

**Intelligent Network Accelerates Distributed Training with 2x Bandwidth Utilization**

A new arXiv paper proposes treating the network as an active participant in distributed training, performing in-switch aggregation and gradient compression. This reduces cross-WAN data volume. Experiments on 100Gbps links show a ~2x training throughput improvement and bandwidth utilization rising from ~60% to 95%. This offers a cost-effective path for multi-datacenter large-model training.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.26453)

**详情页**：https://ai.daily.yangsir.net/daily/20260829-T0-05

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