How leading AI operators move from raw GPU spend to model-advancing utilization through next-generation network design

As AI infrastructure spend crosses $700 billion in 2026, operator success is no longer defined by GPU volume, but by the ability to move data efficiently across scale-up, scale-out, and scale-across fabrics with different physics, economics, and failure modes.

This 60-minute virtual panel examined how operators raise effective GPU utilization by treating network observability as a first-class concern, distinguishing fault tolerance from failure recovery, and designing fabrics around the divergent demands of pre-training, fine-tuning, and inference workloads.

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