A public-health GPU hall needs microsecond-class latency between GPU servers for RoCE-based training. Which network evaluation criterion is most critical?

A public-health GPU hall that needs microsecond-class latency between GPU servers for RoCE-based training must treat ultra-low latency and predictable fabric delay suited to RDMA/RoCE training paths as the critical network evaluation criterion. RoCE/RDMA training iterations are sensitive to hop delay and jitter; fabrics that add unnecessary latency stretch sync barriers and waste expensive GPU time on epidemiology workloads.

Higher latency tolerance with deep buffers tuned for RDMA/RoCE training paths accepts the wrong delay budget and adds queuing delay that RoCE paths cannot afford. Standard latency with TCP retransmission handling for RDMA/RoCE training paths misjudges the fabric; retransmission-based recovery does not preserve GPU-to-GPU delay and can erode the microsecond-class budget RoCE collectives need. Variable latency with best-effort ECMP routing for RDMA/RoCE training paths undermines the predictable east-west GPU-to-GPU fabric that actually determines training latency.

Exam caveat: For RoCE/RDMA AI fabrics, latency and predictability are first-class evaluation criteria alongside bandwidth—not optional after bandwidth looks “fast enough” on average. Operational check: measure GPU-to-GPU latency and jitter on the proposed public-health fabric under load, do not accept standard latency with TCP retransmission handling for RDMA/RoCE training paths, and reject topologies that force multi-hop trombones for every collective.