AI/ML Data Center Network Validation

Modern AI and machine learning workloads are pushing data center networks to their limits. As organizations scale AI infrastructure with thousands of GPUs and xPUs, ensuring high-speed, synchronized, and lossless communication across network fabrics becomes critical to performance. The AI/ML Data Center Network Validation technical brief offers essential insights and methodologies to help engineers test and validate their networks for the demands of AI-scale computing.

This comprehensive brief explores the impact of AI workloads on network design, with deep dives into collective communication patterns such as RingAllReduce, AlltoAll, and more. It outlines the unique traffic behaviors introduced by large-scale training jobs and the role of RDMA over Converged Ethernet version 2 (RoCEv2) in supporting low-latency, high-throughput performance.