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A RoCE network for distributed AI training at scale
AI networks play an important role in interconnecting tens of thousands of GPUs together, forming the foundational infrastructure for training, enabling large models with hundreds of billions of pa…
AI networks play an important role in interconnecting tens of thousands of GPUs together, forming the foundational infrastructure for training, enabling large models with hundreds of billions of parameters such as LLAMA 3.1 405B. To accommodate this, we designed an aggregator training switch (ATSW) layer that connects the CTSWs in a data center building, expanding the RoCE domain beyond a single AI Zone. We would like to thank all contributors to the paper, including Rui Miao, Shengbao Zheng, Sai Jayesh Bondu, Guilherme Goes, Hany Morsy, Rohit Puri, Adi Mohammad Riftadi, Ashmitha Jeevaraj Shetty, Jingyi Yang, Shuqiang Zhang, Mikel Jimenez Fernandez, Shashi Gandham, Omar Baldonado.
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