Glossary

AI storage

AI storage is data storage chosen for the scale and access patterns of AI and machine learning workloads. It is judged by whether GPUs stay busy, not by peak capacity. It is a role a system plays in an AI environment, not a product category with its own technology.

What makes storage suited to AI?

AI workloads do three different things to storage, and no single setting serves all of them. Training reads huge datasets in parallel, mostly large sequential reads. Checkpointing writes large files in sudden bursts, as a long run saves its state. Inference and retrieval make many small, fast reads, often from many users at once. A system that handles the first well can still stall on the other two.

The first requirement is scale. Object storage fits because it grows to billions of objects in one namespace, which suits the mix of documents, images and model artifacts AI collects. The second is that many clients can read at once without queueing, which is a property of scale-out designs where each added node adds throughput.

Why does one tier rarely fit?

Putting every byte on flash is expensive. Putting checkpoints on slow disk stalls training, because the run waits while state is written. The workable approach is to match the tier to the access pattern: faster storage for checkpoints and hot data, high-capacity storage for source datasets and history. This is storage tiering applied to AI, and the flash and disk trade-off still decides the economics. The distinction between sequential and random I/O explains why training and retrieval stress different parts of the same system.

What is AI storage not?

It is not a guarantee. Calling storage AI-ready says nothing until its throughput, latency and concurrency are tested under the real mix of jobs. Capacity figures and peak-speed claims are weak evidence, because the test is whether GPUs reach high utilization during a full run. Marketing labels aside, any storage that serves these patterns can play the role. Concurrency deserves a specific test: dozens of training workers, a checkpoint burst and a retrieval service can all hit the same system in the same minute, and averages hide the collisions.

How does it relate to the neighbouring terms?

AI storage is one layer of AI data infrastructure. GPU storage narrows the view to the path that carries data into GPU memory. The wider platform adds metadata and governance on top of storage, which storage alone does not provide.

Frequently asked questions

Does AI storage have to be all flash?

No. Flash suits checkpoints and active data where latency matters. Source datasets, archives and older model versions usually sit on high-capacity disk.

Why do retrieval workloads need different storage from training?

Training streams large files at a steady rate. Retrieval fetches small pieces on demand, so response time per request matters more than total bandwidth.

Where do model checkpoints and versions end up?

Recent checkpoints stay on fast storage so training can resume quickly. Older checkpoints and model versions move to cheaper capacity once they are no longer active.