Which networking feature is most important for supporting distributed training of large AI models across multiple data centers?

High throughput with low latency WAN links between data centers is crucial for supporting distributed training of large AI models because these models often require the synchronization of vast amounts of data across multiple nodes located in different geographical areas.

In distributed training, different parts of the model or different datasets are processed simultaneously at different locations. High throughput ensures that a large volume of data can be transferred quickly between data centers, while low latency ensures that the time delay in data transmission is minimized. This is particularly important for maintaining the efficiency of the training process, as any delays can hinder the training iterations and thus prolong the overall time required to train the model effectively.

If the network links between data centers are slow or have high latency, it can lead to bottlenecks that significantly disrupt the training workflow, causing inefficiencies and potentially impacting the accuracy and performance of the trained models. Consequently, having robust WAN links that can handle the demands of large-scale AI model training is essential for achieving optimal results.