Scaling the future: Why Ethernet is the backbone of AI Supercomputing

The rapid evolution of artificial intelligence is fundamentally changing how we architect data centers. As AI models grow more complex, the industry is shifting focus from individual server performance to the data center’s interconnected fabric. Two factors are driving this shift: expanding training clusters and inference workloads that now demand cluster-level performance.

For training, frontier models require large numbers of GPUs, and cluster sizes now exceed the capacity of a single data hall. Clusters span multiple data centers connected by wide-area networks, and the infrastructure must scale to support hundreds of thousands of GPUs across broad geographic regions.

Inference is also transforming the infrastructure. Frontier models, even at FP4 precision, now surpass the capacity of a single GPU. The push for faster token serving is increasing demand for larger inference clusters, matching the same coordinated, high-performance networking as training clusters.