Speakers

As AI and ML workloads grow in complexity, the networks supporting them must evolve to handle new traffic patterns, extreme data volumes, and unique performance demands.  Unlike traditional data centers, AI clusters require highly efficient network fabrics to optimize GPU utilization and minimize job completion time. Even minor delays or packet loss affecting a single GPU can lead to significant inefficiencies across the entire system.This presentation will examine the critical parameters necessary to validate and optimize AI data center networks for enhancing fabric efficiency, ensuring optimal GPU utilization, and minimizing job completion times. By exploring the fundamental differences between AI and traditional data center networks, attendees will gain valuable insights into designing scalable, high-performance AI infrastructures that maximize hardware investments and accelerate model training. Read More...