Keep every GPU fed through the entire training pipeline

More GPU-hours converted to training progress, fewer hours burned waiting on data.

VDURA delivers the sustained parallel reads, the checkpoint burst writes, and the availability that decide how much of your fleet actually trains.

Storage availability is a GPU-hour number.

A 10,000-GPU cluster running at 98% storage availability loses roughly 1,752,000 GPU-hours a year. Not to a catastrophic outage, just to the 2%. On a training fleet, every hour the storage tier is degraded is an hour the whole job is degraded with it.

This is why the resiliency story belongs in the same conversation as throughput. Multi-level erasure coding scopes a failure to a single VPOD, self-healing rebuilds run in the background, and upgrades happen in place, so the fleet keeps training.

Slow writes make you checkpoint less often. That is the trap.

At thousands of accelerators, component failure is continuous, not exceptional. Every failure costs you the progress since your last checkpoint, plus the time to reload and resume. Stretch the interval and both losses grow.

VDURA lands checkpoint and optimizer state as parallel burst writes across flash nodes; V12 snapshots version every checkpoint instantly. Cheap checkpoints mean frequent checkpoints, and parallel reads reload state fast enough that a failed run resumes instead of restarting.

A true parallel file system, not a gateway in front of an object store.

Every DirectFlow client stripes files wide across storage nodes simultaneously, over RDMA or TCP, with client-side erasure coding and full cache coherence. No NFS choke point, no controller pair, no scale-out NAS pretending to be parallel. Throughput scales with node count, linearly, all the way up.