As AI functions more and more permeate enterprise operations, from enhancing affected person care by superior medical imaging to powering advanced fraud detection fashions and even aiding wildlife conservation, a essential bottleneck typically emerges: information 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 associate at M12 about how improvements in storage expertise allows enterprise AI use circumstances in healthcare.
The MONAI framework is a breakthrough in medical imaging, constructing it sooner, extra safely, and extra securely. Advances in storage expertise is what allows researchers to construct on high 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 circumstances, relying on the place they’re within the AI information pipeline,” Matson mentioned. “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 sorts of merchandise that we make and the sorts of integrations we’ve to make with the software program.”
Bettering AI inference on the edge
For peak efficiency on the edge, it’s essential to scale storage all the way down to a single node, with a purpose to carry inference nearer to the info. And what’s secret is eradicating reminiscence bottlenecks. That may be achieved by making reminiscence part of the AI infrastructure, with a purpose to scale it together with information and metadata. The proximity of knowledge to compute dramatically will increase the time to perception.
“You see all the large deployments, the large inexperienced discipline information facilities for AI, utilizing very particular {hardware} designs to have the ability to carry the info as shut as attainable to the GPUs,” Matson mentioned. “They’ve been constructing out their information facilities with very high-capacity solid-state storage, to carry petabyte-level storage, very accessible at very excessive speeds, to the GPUs. Now, that very same expertise is occurring in a microcosm on the edge and within the enterprise.”
It’s changing into essential to purchasers of AI programs to make sure you’re getting essentially the most efficiency out of your system by operating it on all strong state. That lets you carry big quantities of knowledge, and allows unbelievable processing energy in a small system on the edge.
The way forward for AI {hardware}
“It’s crucial that we offer options which are open, scalable, and at reminiscence velocity, utilizing a number of the newest and best expertise on the market to do this,” Cummings mentioned. “That’s our objective as an organization, to offer that openness, that velocity, and the size that organizations want. I feel you’re going to see the economies match that as properly.”
For the general coaching and inference information pipeline, and inside inference itself, {hardware} wants will hold rising, whether or not it’s a really high-speed SSD or a really high-capacity resolution that’s energy environment friendly.
“I might 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 principally substitute 4 instances as many arduous drives, or a really high-performance product that’s virtually close to reminiscence speeds,” Matson mentioned. “You’ll see that the large GPU distributors are taking a look at how you can outline the subsequent storage structure, in order that it could possibly assist increase, very carefully, the HBM within the system. What was a general-purpose SSD in cloud computing is now bifurcating into capability and efficiency. We’ll hold doing that additional out in each instructions over the subsequent 5 or 10 years.”
