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2.10

Who makes AI storage systems for AI data centers

AI storage systems (parallel file systems, object, GPU-direct)

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.

Appears at:Enterprise / small coloLarge colo / hyperscale buildingAI campusGW-class campus· cooling:any — Presence and scale depend on cluster size and whether training data volume justifies a dedicated parallel file system versus general-purpose storage.

Companies on this part (14)

CompanyRoleOwnershipTickerPrice1dConfidenceNote
DellmanufacturerpublicDELL456 USD+1.7%disclosed · source
EverpuremanufacturerpublicP123 USD+2.4%disclosed · source
Hitachi Vantara · unit of Hitachimanufacturersubsidiary6501.T*disclosed · source
HPEmanufacturerpublicHPE56.69 USD+3.4%inferred
IBMmanufacturerpublicIBM254 USD+1.2%disclosed · source
NetAppmanufacturerpublicNTAP215 USD+2.7%inferred
SupermicromanufacturerpublicSMCI32.94 USD-1.7%inferred
DDNmanufacturerprivatedisclosed · source
Hammerspacemanufacturerprivatedisclosed · source
Huaweimanufacturerprivateinferred
MinIOmanufacturerprivatedisclosed · source
Qumulomanufacturerprivatedisclosed · source
VAST Datamanufacturerprivatedisclosed · source
Wekamanufacturerprivatedisclosed · source

Who competes here

The same table read the other way: every company above competes for AI storage systems sockets. Confidence tags matter — an inferred placement is a lead, not a confirmed rivalry.

Seen in real machines

AI storage systems is named in 15 of the rack teardowns and supercomputer breakdowns on this site. Each line below is a piece of that machine. The companies shown are the ones named on that line, not necessarily the maker of this part.

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Questions about AI storage systems

What are AI storage systems?
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.
Which companies make AI storage systems for AI data centers?
This catalog lists 14 companies on AI storage systems, 7 of them with listed equity: Dell, Everpure, Hitachi Vantara, HPE, IBM, NetApp, Supermicro, DDN, Hammerspace, Huawei, and 4 more.
How does AI change demand for AI storage systems?
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.
How reliable are these AI storage systems supplier placements?
The 14 placements on this part break down as 10 disclosed, 4 inferred. "disclosed" means the company or a filing says so; "reported" comes from trade press or a reference-design list; "inferred" is a taxonomy lead with no citation yet.

Sources: placements without a source URL come from the taxonomy seed and are tagged accordingly. How this data is built.· Markdown · JSON