GPU Storage and Networking Design for AI Clusters

GPU storage and networking must be designed together — storage that delivers throughput at the rate GPUs consume data, and a network that moves that data at the bandwidth and latency the GPUs demand, with no bottleneck at any hop between storage endpoint and GPU memory. For the storage requirements, see AI storage architecture requirements. For networking requirements, see networking requirements for AI infrastructure.

Co-Design Principles

Storage throughput → network bandwidth: the storage must deliver enough throughput to saturate the GPUs, and the network must carry that throughput without oversubscription. A storage layer that delivers 100GB/s is wasted if the network to the GPUs is 40GB/s. Network latency → storage response time: for inference, the network's baseline latency contributes to every data access; the storage and network together determine the total latency a GPU experiences when pulling data. Topology and locality: storage and GPUs co-located in the same rack or fabric zone reduces latency and avoids traversing oversubscribed links. Burst handling: checkpoint writes are bursty; the storage and network together must absorb the burst without throttling — storage write bandwidth and network capacity must both handle the peak.

FAQ

Should GPU storage and networking be designed together?

Yes — a fast storage layer on a slow network starves GPUs just as much as slow storage on a fast network. The data path from storage to GPU must have no bottleneck at any hop. Co-design means sizing storage throughput and network bandwidth together. See above.

Summary

GPU storage and networking co-design eliminates data path bottlenecks. For the full framework, see AI storage requirements.