AI Cluster Network Telemetry: What to Monitor Before RoCEv2 Jobs Fail

AI cluster network telemetry should answer one operational question quickly: is the training or inference job slow because of the application, storage, GPU scheduling, or the network fabric? In RoCEv2 and high-throughput Ethernet fabrics, waiting for a hard failure is too late. By the time users report idle GPUs, the useful evidence may already be spread across switch counters, NIC statistics, job logs and monitoring gaps.

This practical checklist focuses on signals that catch congestion, packet loss and unstable paths before they become a full incident. It is vendor-neutral on purpose: the exact commands change, but the telemetry model is reusable for leaf/spine data centers, AI infrastructure pods and lab environments.

X Follow @ipexptobe

RoCEv2 Troubleshooting Checklist: PFC, ECN and GPU Storage Latency

RoCEv2 troubleshooting is hard because the failure often looks like an application problem: GPU jobs stall, checkpoint writes take longer, a storage node looks slow, or one rack suddenly drags down the rest of the pod. The network may show no classic packet loss on a normal interface graph, while the real issue is hiding in priority queues, ECN marking, PFC pause frames, NIC classification, or a backup flow accidentally sharing the lossless class.

This practical checklist is for small and mid-size AI clusters, data-center labs and private GPU pods where RoCEv2 is used for storage, distributed training or low-latency east-west traffic. It complements the AI Infrastructure hub, the Data Center Networking hub, and the broader Start Here networking topics page.

X Follow @ipexptobe