Explainer: Why your legacy storage is choking your expensive GPU
THE REGISTER EXPLAINER: GPUs idle? Blame your outdated storage, not the silicon sprinters.
When your accelerators sit idle, the problem usually isn't the chips. It's everything between them and the data. Rather than thinking purely about GPU performance, it's time to think about storage as an active engine for throughput, rather than a passive archive. Legacy storage architectures aren't built that way.
What is GPU starvation?
A starved GPU is an accelerator waiting around with nothing to do because data isn't arriving quickly enough. Sometimes the network is choking; in other cases, the next batch of training or inference data can't get off storage fast enough.
Modern AI training and inference workloads demand sustained high-bandwidth, low-latency feeds that traditional storage was never designed to deliver.
How do companies solve the AI storage problem?
In many cases, badly. To compensate for slow, passive storage, teams copy and stage datasets into whichever...
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