Storage that can actually feed a GPU cluster.
A starved cluster is an idle cluster. Storage is where expensive accelerators most often end up waiting, and where the requirement is least like ordinary enterprise storage.
What decides this requirement.
Throughput, in parallel, sustained.
Training reads enormous datasets continuously across every node at once, and writes large checkpoints periodically. That profile, sustained parallel throughput rather than transactional IOPS, is not what most enterprise storage was designed around.
Parallel filesystems exist for exactly this. Platforms from vendors such as VAST Data and WEKA are commonly deployed under AI clusters because they deliver aggregate throughput to many clients simultaneously, which is the specific thing a training pipeline demands.
The failure mode when this is undersized is quiet and expensive: the cluster runs, jobs complete, and effective utilisation is well below what the GPU count should deliver. Teams frequently blame the fabric or the code before checking the filesystem.
Checkpoints and cadence, not just capacity.
Capacity is the easy half. Throughput at your node count is the question that matters, and it has to be sustained rather than burst. We size against how many nodes will read simultaneously and how large and frequent your checkpoints are.
Checkpoint cadence deserves explicit design. Too frequent and you burn training throughput on writes; too rare and a failure costs days. It is a decision, not a default.
We source the storage with the cluster, from the vendors that suit the workload, and price it as part of the configuration rather than a later addition. Availability and options via the hardware sourcing desk.
Start from what is verified.
Current verified lines with quantities, lead times and indicative pricing are public on the live inventory. Anything not listed becomes a sourcing requirement with a first pass inside one business day.
Asked first, answered straight.
Because the profile is different. AI training wants sustained parallel throughput to many clients at once, where enterprise storage is typically designed around transactional IOPS. The mismatch shows up as idle GPUs.
Effective utilisation well below what the GPU count implies, with the fabric and code apparently fine, is the classic signature. It is worth checking the filesystem before blaming anything else.
We source storage platforms alongside the compute, including the parallel filesystem vendors commonly deployed under AI clusters. Which suits you depends on the workload, and we will say when a simpler option is sufficient.
Deliberately. Frequent checkpoints cost throughput; rare ones cost days when a run fails. It should be a designed trade-off rather than a default setting.
Working through this on a real requirement?
Twenty minutes with the desk, no pitch and no quote at the end of it. Tell us roughly what you need and we will come back within one business day.
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