Cisco Blue Fern Consulting Huawei Huawei Apple This document defines benchmarking terminology, methodologies, and Key Performance Indicators (KPIs) for evaluating Ethernet-based AI training network fabrics. As large-scale distributed Artificial Intelligence / Machine Learning (AI/ML) training clusters grow to tens of thousands of accelerators (GPUs or generic accelerator processing units (XPUs)), the backend network fabric determines 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. The tests cover Remote Direct Memory Access (RDMA) over Converged Ethernet version 2 (RoCEv2) transport, the Ultra Ethernet Transport (UET) protocol defined by the Ultra Ethernet Consortium (UEC) Specification 1.0 [UEC-1.0], congestion management (Priority Flow Control (PFC), Explicit Congestion Notification (ECN), Data Center Quantized Congestion Notification (DCQCN), Credit-Based Flow Control (CBFC)), load balancing strategies (Equal-Cost Multi- Path (ECMP), Dynamic Load Balancing (DLB), packet spraying), collective communication patterns (AllReduce, AllToAll, AllGather), and scale/soak testing. The methodology enables direct, reproducible comparison across switch ASICs, NIC transport stacks (RoCEv2 and UET), and fabric architectures (2-tier Clos, 3-tier Clos, and rail-optimized).