Storage farm for AI: Ceph, Lustre and NVMe, how to choose

AI storage fails when it is sized on capacity instead of behavior. Training epochs read millions of small files or large sequential shards depending on your data loader. Checkpointing a large model bursts tens to hundreds of gigabytes of writes, from every node at once, on a strict interval. Inference wants low-latency reads of model weights at startup and near-zero I/O afterwards. Three profiles, three different winning architectures.

Measure before buying: capture read patterns from a real training run, note checkpoint sizes and frequency, and set a target for GPU utilization during data loading. A farm where accelerators wait on storage 20 percent of the time has silently lost a fifth of its budget.

Lustre is the parallel filesystem of record for large sequential throughput: hundreds of gigabytes per second across many clients, ideal for big training shards and burst checkpoints. Its cost is operational expertise and less comfort with small-file metadata storms. Ceph is the versatile general store: object (S3-compatible), block and file from one cluster, self-healing, well understood by platform teams. It powers data lakes and dataset versioning brilliantly, but needs careful design (NVMe OSDs, correct erasure coding) to feed hungry training nodes.

NVMe-oF is not a filesystem but a transport: it exposes remote flash at near-local latency. Use it as the hot tier, local scratch for the active epoch, checkpoint landing zone before async drain to Ceph or Lustre. The mature pattern is tiered: NVMe hot, parallel FS warm, object store cold, with lifecycle automation moving data instead of engineers.

Three rules from the field. First, size bandwidth for checkpoint bursts, not average load: if 64 nodes each flush 4 GB within your checkpoint window, the write path must absorb that or training stalls cluster-wide. Second, never let capacity pass 70 percent on the hot tier; rebalancing and recovery need headroom exactly when you have none. Third, plan the growth story on day one: datasets triple faster than procurement cycles, so choose systems that scale by adding nodes, not by forklift replacement.

A storage farm designed this way is not a cost center bolted onto the GPU budget. It is what keeps the GPU budget honest. YAPIO sizes the storage tier from your real I/O traces in the same audit that sizes compute and fabric.