What is AI Storage?
Short answer: AI storage is designing data access so accelerators train or serve, rather than wait: sustained throughput, predictable latency, parallel access, metadata and checkpoints, not a SKU and not capacity alone.
Definition
AI storage answers an operational question: whether the training, inference or retrieval pipeline can keep feeding accelerators. It is not a product category. The gap shows up when a saturated NAS, all-flash that is not parallel, or a corpus living on the same tier as the GPUs, leaves workers waiting. Capacity alone does not close that gap.
Why AI workloads stress storage differently
General-purpose enterprise storage is usually designed for mixed transactional workloads with moderate concurrency. AI pipelines invert those assumptions: a small number of very large jobs read the same dataset from many workers at once, then write large checkpoints in bursts.
The failure is rarely dramatic. It shows up as epochs that take longer than the model architecture would suggest, inference cold starts that miss latency targets, or a retrieval service that degrades as the corpus grows.
Training vs inference
Training is throughput-dominated and highly parallel: the goal is to keep every accelerator fed. Inference is latency-dominated and concurrency-heavy: many small reads, strict tail-latency expectations, and sensitivity to model load times on cold start. RAG pipelines sit in between and add a metadata-intensive retrieval layer.
- Throughput
- Sustained read bandwidth to keep accelerators fed during training epochs.
- Latency
- Predictable response for inference and retrieval paths, including tail latency.
- Parallel access
- Many workers reading the same dataset concurrently without contention.
- Metadata
- Directory and small-file operations that dominate some AI and RAG pipelines.
- Checkpointing
- Bursty large writes that must not stall the training loop.
File vs object
File access suits training pipelines that expect POSIX semantics and very high parallel read rates. Object access suits large and growing corpora, archives, and portable pipelines. Most production estates run both and connect them with an explicit lifecycle policy.
Growth
Plan capacity for dataset versions, checkpoints, embeddings and outputs, not only for the active dataset. Decide early what moves to a capacity tier and when. That object tier does not cover the same requirement as the parallel file system that feeds the GPUs.
In the Azuba model
In Data & AI Infrastructure, AI storage is the access problem. In the published architectures, WEKA covers parallel POSIX file; Scality covers the capacity object tier. Everpure, Silk and Tintri cover other requirements.
Questions to ask when evaluating AI storage
- Is the dominant pattern training, inference, RAG, or a mix?
- How many workers read the same dataset at once?
- How frequent and how large are checkpoints?
- Is the expected access file, object, or both?
- What headroom exists for versions, embeddings and outputs?
A future AI Storage Planner tool will help size these questions. It is not published yet.
Continue
Is AI storage a product category or an architecture?
In practice it is an architecture. Several platform types can serve AI workloads well; what makes storage AI storage is that it is designed around the access patterns of training, inference and retrieval.
Does AI storage have to be all-flash?
The performance tier that feeds accelerators is normally flash. Most estates also keep a capacity tier for corpora, dataset versions and outputs, with a lifecycle policy between the two.
What does WEKA cover?
Parallel POSIX file in the Data & AI Infrastructure model. AI storage is the access problem; Scality covers object.
Does object storage replace this design?
No. Object fits when the corpus grows or is archived. Parallel file fits when training expects POSIX and concurrent reads. In Azuba’s model, Scality covers object; it does not cover feeding GPUs.
Is there a storage planner or score on this site?
A sizing tool is not published. This note explains the access pattern; GB/s and fit are assessed in the environment.