Benchmarking Methodology for AI Training Network Fabrics

This document defines benchmarking terminology, methodologies, and Key Performance Indicators (KPIs) for evaluating Ethernet-based AI training network fabrics. As large-scale distributed AI/ML training clusters grow to tens of thousands of accelerators (GPUs/XPUs), the backend network fabric becomes the critical bottleneck determining job completion time (JCT), training throughput, and accelerator utilization. This document establishes vendor-independent, reproducible test procedures for benchmarking fabric-level performance under realistic AI training workloads, covering RDMA/RoCEv2 transport, the Ultra Ethernet Transport (UET) protocol defined by the UEC Specification 1.0 [UEC-1.0], congestion management (PFC, ECN, DCQCN, CBFC), load balancing strategies (ECMP, DLB, packet spraying), collective communication patterns (AllReduce, AlltoAll, AllGather), and scale/ soak testing. The methodology enables direct, reproducible comparison across different switch ASICs, vendor implementations, NIC transport stacks (RoCEv2 vs. UET), and fabric architectures (2-tier Clos, 3-tier Clos, rail-optimized).