February 2026
Intermediate to advanced
384 pages
12h 48m
English
book
AI Data Center Network Design and Technologies
by Mahesh Subramaniam, Michal Styszynski, Himanshu Tambakuwala
Content preview from AI Data Center Network Design and Technologies
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6
Efficient Load Balancing
As we discussed in Chapter 1, “Wonders in the Workload,” AI/ML clusters use different kinds of models, such as large language models (LLM) with both natural language understanding (NLU) and natural language generation (NLG) components. The models are trained at the same time, across multiple GPUs. As a part of the training, GPUs need to synchronize massive amounts of data across nodes, which leads to enormous east–west traffic in the data center. Typically, the number of applications generating traffic in a cluster is low, most of the traffic is RDMA over Converged Ethernet (RoCEv2) traffic, and there is low entropy at the transport layer. Also, in many cases, the traffic in an AI/ML fabric is between source/destination ...
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