Deploying RDMA for Large-Scale Inference and Training Workloads
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Computer Science > Machine Learning
arXiv:2610.12815 (cs)
[Submitted on 8 Oct 2026]
Title:Deploying RDMA for Large-Scale Inference and Training Workloads
Authors: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
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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 failures introduce challenges that hardware offload alone cannot address. This paper reports our experience deploying ByteExpress, a software communication layer over hardware RDMA, in ByteDance's parameter-server-based inference and training systems. We present three lessons from the deployment: efficient RDMA requires buffer management across application and transport layers; scalable communication requires coordinated management of connections, receive capacity, and outstanding data; and service availability requires recovery above the transport layer. ByteExpress combines an RDMA-aware allocator, zero-copy transfers, QP multiplexing, receive-credit flow control, a software send window, and RPC-assisted recovery. In production inference, ByteExpress reduces mean and P99 model parameter fetch latency by 35.7% and 43.7%, respectively, and total CPU usage by 10.69% compared with kernel TCP. In training, ByteExpress reduces P99 model parameter push latency by up to 74.0% compared with TCP, with no PFC pauses observed at runtime.
| Comments: | 14 pages, 16 figures |
| Subjects: | Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.12815 [cs.LG] |
| (or arXiv:2610.12815v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12815
arXiv-issued DOI via DataCite (pending registration)
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