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Collective Communications: The Operations Behind Every AI Network Flow

This post, part of a series on AI data center networking, clarifies the traffic patterns arising from collective communications during GPU operations. It highlights key operations like broadcast, scatter, gather, and reduce, explaining their significance for both training and inference, especially in Mixture-of-Experts models. Understanding these patterns aids in optimizing network performance.

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How a GPU Actually Connects to the Network

This article discusses essential aspects of data center networking for AI, emphasizing the unique communication dynamics between GPUs. It highlights the importance of PCIe bus architecture, NUMA locality, and NVLink technology in optimizing performance. The design strategy of one NIC per GPU and the use of SmartNICs in multi-tenant environments are also explored.

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Why networking is different for AI Data Centers

This article discusses the necessity for rethinking data center networking in the context of AI. Traditional designs based on traffic averages and oversubscription do not apply, as AI workloads produce massive, synchronized “elephant” flows, necessitating non-blocking architectures and emphasizing metrics like job completion time and GPU utilization over conventional measures.

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