Ethernet vs InfiniBand for AI Infrastructure

Training workloads are sensitive to latency and to congestion during collective operations. Inference and RAG are often more sensitive to isolation, East-West fairness, and the path to storage. One fabric rarely serves every plane well if it is treated as a single flat network.

InfiniBand remains a proven choice for tightly coupled training domains. It is not automatically the right choice for every AI factory. Ethernet at 400/800G, with a lossless or properly engineered congestion story, is now a serious training fabric, and often a better fit where the operator already runs Ethernet at scale. NVIDIA SuperPOD reference architectures name Quantum-X800 Q3400 or Quantum-2 QM9700 for compute, QM9700 or Spectrum SN5600 for storage, and SN5610 for user/NFS paths.

What actually decides the design: message size and parallelism strategy, number of endpoints, storage protocol, in-house operational skill, and whether the same fabric is expected to carry tenant traffic.

We separate planes. East-West compute, storage/data when dedicated, North-South front-end, and management/OOB are different networks even when they share a vendor. Collapsing them to save ports is how you create an outage that looks like a 'model problem'. Management/OOB is never application North-South.

There is no poster-child answer. There is a workload, a scale, and an operations team. The architecture should be able to say why InfiniBand, why Ethernet, or why both, in writing, with a diagram, before anyone orders optics.