Storage for AI Training Clusters

Storage can become a bottleneck when many accelerators request data faster than the system supplies it. Estimate the workload's aggregate demand from GPU count, batch size, sample representation, and training step rate. Then measure actual bytes per second, IOPS, metadata operations, and read latency under concurrency. A single-node benchmark may not represent a multi-node training job. Object storage provides durable, scalable access to objects through APIs, but it does not behave exactly like a POSIX filesystem. Many small get or list requests can add overhead, and access latency can be higher than local storage. Large sequential reads and sharded datasets often use it efficiently. A cache or parallel filesystem can present data through a filesystem interface and stage hot objects closer to compute. Parallel filesystems such as Lustre distribute data and metadata across storage components to serve high-throughput workloads. They can support many clients concurrently, but capacity, metadata servers, network fabric, striping, and file layout affect performance. Thousands of tiny files may stress metadata even when total bytes are modest. Dataset sharding, sequential reads, and prefetch can reduce pressure, though they change data-loader behavior. Local NVMe offers low-latency, high-throughput access near a node, but it may be ephemeral and limited in capacity. It can cache datasets or hold temporary checkpoints. Durable outputs should be copied to shared or object storage, and recovery plans should account for node loss. Checkpoint frequency trades recovery time against storage traffic and job pauses. Storage design should include consistency, permissions, encryption, backups, retention, and data versioning. Monitor throughput and wait time alongside GPU activity. If devices are idle while the loader blocks on reads, investigate format and storage before buying more GPUs. Validate recovery by reading checkpoints and dataset versions from the actual training environment.