Abstract：With the rapid development of AI large models, the demand for computing power has sharply increased. Focusing on the
pain points of low utilization of computing power resources and security concerns caused by sensitive data leaving the park, this ar‐
ticle proposes a solution based on storage and lossless network technology. By constructing an intelligent computing experimental
network and adopting an innovative mode of simultaneous transmission and training, real-time data processing and rapid model up‐
dates have been achieved. The experimental results show that this scheme significantly improves the efficiency of computing power
usage, reduces single task training time by 50%, increases data transmission rate to 7.3 Gbps, and can still maintain efficient opera‐
tion under 200 KM distance conditions. However, challenges such as data security and privacy protection still need to be addressed
in the future. This study provides new ideas and methods for addressing the computing power demand in the era of large models.
祁 钰 黄 嘉 王立秋 涂艳丽 陈子瑜. 基于存储拉远与无损网络的AI大模型高效训练方案研究[J]. 电脑与电信, 2025, 1(1-2): 9-.
QI Yu HUANG Jia WANG Li-qiu TU Yan-li CHEN Zi-yu. Research on Efficient Training Scheme for AI Large Model Based on Storage Remote
and Lossless Network. Computer & Telecommunication, 2025, 1(1-2): 9-.
2025, Vol. 1 
