GPU Cluster Networking in Production: InfiniBand vs. RoCEv2 vs. Ultra Ethernet Architecture, Congestion Control, and NCCL Collective Latency
Distributed training and high-throughput inference workloads are fundamentally bound by the network fabric. While traditional cloud applications rely on asynchronous request-response cycles that absorb latency jitter, distributed deep learning relies on synchronous collective communication. Operations such as All-Reduce, All-Gather, and All-to-All require hundreds or thousands of GPUs to exchange tensors and synchronize at strict barrier points before execution can proceed. In this execution mo
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