Networking Requirements for AI Infrastructure Design

AI infrastructure networking must deliver bandwidth for collective operations at scale, low latency and jitter for inference, RDMA fabrics with non-oversubscribed topologies, and the ability to serve both training and inference without one degrading the other. For the cluster networking evaluation, see evaluating AI cluster networking. For the GPU-level requirements, see GPU cluster networking requirements.

Design Requirements by Workload

Training: high bandwidth for all-reduce and other collectives, non-oversubscribed topology so communication does not bottleneck at any link, and RDMA to minimize CPU overhead. The fabric must sustain peak collective operation bandwidth at the cluster's full scale. Inference: low baseline latency and low jitter for multi-node serving, because network latency is paid on every forward pass. RDMA is the standard; TCP adds unacceptable latency. Mixed workloads: the fabric must serve both profiles — sustained bandwidth alongside low-latency paths — without contention between training traffic and inference traffic. This typically requires QoS or traffic isolation. Scale: as GPU count grows, the communication pattern becomes the bottleneck before the compute does. The fabric must scale with the cluster — a topology that works at 8 nodes may oversubscribe at 64. For the testing methodology, see evaluating AI cluster networking.

FAQ

What networking does AI infrastructure need?

RDMA fabrics with non-oversubscribed topologies, bandwidth sized for collective operations, low latency and jitter for inference, and the ability to serve mixed workloads. Design for the peak collective operation demand at the cluster's full scale. See the requirements above.

Summary

AI networking requires RDMA, bandwidth, low latency, and non-oversubscription — designed for the workload. For the full evaluation, see evaluating AI cluster networking.