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ByteDance deploys RDMA-based ByteExpress for faster AI training and inference

By PulseAugur Editorial · [1 sources] ·

ByteDance has developed ByteExpress, a software communication layer built on top of hardware RDMA, to improve the efficiency of large-scale inference and training workloads. This system addresses challenges like variable-size messages and service failures that standard RDMA implementations struggle with. Deploying ByteExpress in ByteDance's parameter-server systems has led to significant reductions in latency and CPU usage compared to traditional TCP, with notable improvements in model parameter fetch and push operations. AI

IMPACT Optimizes infrastructure for large-scale AI model training and inference, potentially reducing costs and improving performance.

RANK_REASON Paper detailing a new software communication layer for RDMA in ML workloads. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ByteDance deploys RDMA-based ByteExpress for faster AI training and inference

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Paper detailing a new software communication layer for RDMA in ML workloads. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yibo Huang, Yi Cui, Yukai Huang, Huaping Zhou, Jiancong Tong, Zhaoyu Wu, Gen Miao, Haoran Wei, Boyan Ding, Zhuo Jiang, Jianxi Ye, Yibo Zhu, Chuanxiong Guo, Yang Xu, Jie Wu ·

    Deploying RDMA for Large-Scale Inference and Training Workloads

    arXiv:2610.12815v1 Announce Type: new Abstract: RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing TCP. Variable-size messages, concurrent connections, bursty incasts, and service…

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