Need smarter insights in your inbox? Join our weekly newsletters to get solely what issues to enterprise AI, knowledge, and safety leaders. Subscribe Now
As AI functions more and more permeate enterprise operations, from enhancing affected person care by means of superior medical imaging to powering complicated fraud detection fashions and even aiding wildlife conservation, a crucial bottleneck usually emerges: knowledge storage.
Throughout VentureBeat’s Remodel 2025, Greg Matson, head of merchandise and advertising, Solidigm and Roger Cummings, CEO of PEAK:AIO spoke with Michael Stewart, managing companion at M12 about how improvements in storage know-how permits enterprise AI use instances in healthcare.
The MONAI framework is a breakthrough in medical imaging, constructing it sooner, extra safely, and extra securely. Advances in storage know-how is what permits researchers to construct on prime of this framework, iterate and innovate shortly. PEAK:AIO partnered with Solidgm to combine power-efficient, performant, and high-capacity storage which enabled MONAI to retailer greater than two million full-body CT scans on a single node inside their IT setting.
“As enterprise AI infrastructure evolves quickly, storage {hardware} more and more must be tailor-made to particular use instances, relying on the place they’re within the AI knowledge pipeline,” Matson stated. “The kind of use case we talked about with MONAI, an edge-use case, in addition to the feeding of a coaching cluster, are properly served by very high-capacity solid-state storage options, however the precise inference and mannequin coaching want one thing totally different. That’s a really high-performance, very excessive I/O-per-second requirement from the SSD. For us, RAG is bifurcating the varieties of merchandise that we make and the varieties of integrations we now have to make with the software program.”
Enhancing AI inference on the edge
For peak efficiency on the edge, it’s crucial to scale storage right down to a single node, as a way to deliver inference nearer to the information. And what’s secret’s eradicating reminiscence bottlenecks. That may be completed by making reminiscence part of the AI infrastructure, as a way to scale it together with knowledge and metadata. The proximity of information to compute dramatically will increase the time to perception.
“You see all the large deployments, the massive inexperienced subject knowledge facilities for AI, utilizing very particular {hardware} designs to have the ability to deliver the information as shut as doable to the GPUs,” Matson stated. “They’ve been constructing out their knowledge facilities with very high-capacity solid-state storage, to deliver petabyte-level storage, very accessible at very excessive speeds, to the GPUs. Now, that very same know-how is going on in a microcosm on the edge and within the enterprise.”
It’s changing into crucial to purchasers of AI techniques to make sure you’re getting probably the most efficiency out of your system by working it on all strong state. That permits you to deliver enormous quantities of information, and permits unimaginable processing energy in a small system on the edge.
The way forward for AI {hardware}
“It’s crucial that we offer options which can be open, scalable, and at reminiscence pace, utilizing a few of the newest and best know-how on the market to try this,” Cummings stated. “That’s our aim as an organization, to supply that openness, that pace, and the dimensions that organizations want. I feel you’re going to see the economies match that as properly.”
For the general coaching and inference knowledge pipeline, and inside inference itself, {hardware} wants will maintain rising, whether or not it’s a really high-speed SSD or a really high-capacity resolution that’s energy environment friendly.
“I’d say it’s going to maneuver even additional towards very high-capacity, whether or not it’s a one-petabyte SSD out a few years from now that runs at very low energy and that may mainly change 4 instances as many laborious drives, or a really high-performance product that’s virtually close to reminiscence speeds,” Matson stated. “You’ll see that the massive GPU distributors are the right way to outline the following storage structure, in order that it may assist increase, very intently, the HBM within the system. What was a general-purpose SSD in cloud computing is now bifurcating into capability and efficiency. We’ll maintain doing that additional out in each instructions over the following 5 or 10 years.”
