Storage Capacity Planning for AI Workloads

Storage capacity planning for AI workloads balances dataset size, checkpoint volume, inference log growth, and vector database storage — sizing for capacity and throughput together so GPUs are fed without overspending on unused storage. For the storage architecture requirements, see AI storage architecture requirements. For capacity sizing broadly, see how to size AI infrastructure capacity.

The Capacity Drivers

Datasets: the raw training data, the preprocessing and augmented versions, and the version history that accumulates as datasets evolve. Plan for dataset growth — training on larger datasets over time raises storage capacity needs. Checkpoints: each training run produces checkpoints; the capacity consumed depends on checkpoint frequency, model size, and retention policy. For the checkpoint volume, see checkpoint storage architecture. Inference logs: prompts and responses accumulate with every inference request; the volume scales with traffic and retention period. Vector databases: for RAG workloads, the indexed document store grows with the document corpus. Throughput: capacity is about how much fits; throughput is about whether the storage feeds GPUs fast enough. Both must be planned. For throughput vs capacity tradeoffs, see storage requirements by workload.

FAQ

How do I plan AI storage capacity?

Estimate dataset size and growth, checkpoint volume and retention, inference log accumulation, and vector database growth. Size for capacity and throughput — GPUs need both. Add headroom for dataset growth. See the drivers above.

Summary

AI storage capacity planning sizes for datasets, checkpoints, logs, vectors, and throughput. For the full storage framework, see AI storage architecture requirements.