Part of the day: AI storage systems

Assembled by the nightly build from this catalog’s own entry for AI storage systems (2.10, Layer 2 — Memory & storage). The tables below are rebuilt every night; first published 2026-09-03, refreshed 2026-09-24.

Purpose-built storage software and appliances — parallel file systems, object stores, and GPU-direct pipelines — that aggregate many drives into one system capable of feeding a large number of accelerators at once.

This is the system and software layer sitting above the drive tiers below it (2.07 through 2.09), the layer that a training cluster’s compute (Layer 4) and networking (Layer 3) actually talk to when reading or writing data. Accelerators sit idle waiting on data if storage cannot sustain the read throughput a large training job demands, so clusters increasingly get built around parallel file systems and GPU-direct storage paths designed specifically for that access pattern instead of general enterprise file and block storage. For an investor, a wave of storage-specific vendors has built products around this workload and now competes with established enterprise storage incumbents extending existing product lines into the same use case, alongside hyperscalers running their own in-house systems; competitive position depends on certified GPU-direct integration with each new accelerator and networking generation rather than on general enterprise storage credentials.

AI delta: Large training jobs need read throughput that general enterprise storage was not designed to sustain, which is what has created a distinct category of storage system built around GPU-direct access.

Who makes it

The full entry — sub-categories, news, videos and sources — lives on the AI storage systems page.