As AI functions more and more permeate enterprise operations, from enhancing affected person care by way of superior medical imaging to powering advanced fraud detection fashions and even aiding wildlife conservation, a vital bottleneck typically emerges: information storage.
Throughout VentureBeat’s Remodel 2025, Greg Matson, head of merchandise and advertising and marketing, Solidigm and Roger Cummings, CEO of PEAK:AIO spoke with Michael Stewart, managing associate at M12 about how improvements in storage know-how allows 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 allows 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 atmosphere.
“As enterprise AI infrastructure evolves rapidly, storage hardware increasingly needs to be tailored to specific use cases, depending on where they are in the AI data pipeline,” Matson stated. “The type of use case we talked about with MONAI, an edge-use case, as well as the feeding of a training cluster, are well served by very high-capacity solid-state storage solutions, but the actual inference and model training need something different. That’s a very high-performance, very high I/O-per-second requirement from the SSD. For us, RAG is bifurcating the types of products that we make and the types of integrations we have to make with the software.”
Bettering AI inference on the edge
For peak efficiency on the edge, it’s vital to scale storage all the way down to a single node, so as to carry inference nearer to the info. And what’s key’s eradicating reminiscence bottlenecks. That may be accomplished by making reminiscence part of the AI infrastructure, so as 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 huge deployments, the big green field data centers for AI, using very specific hardware designs to be able to bring the data as close as possible to the GPUs,” Matson stated. “They’ve been building out their data centers with very high-capacity solid-state storage, to bring petabyte-level storage, very accessible at very high speeds, to the GPUs. Now, that same technology is happening in a microcosm at the edge and in the enterprise.”
It’s turning into vital 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 permits you to 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 imperative that we provide solutions that are open, scalable, and at memory speed, using some of the latest and greatest technology out there to do that,” Cummings stated. “That’s our goal as a company, to provide that openness, that speed, and the scale that organizations need. I think you’re going to see the economies match that as well.”
For the general coaching and inference information pipeline, and inside inference itself, {hardware} wants will hold growing, whether or not it’s a really high-speed SSD or a really high-capacity resolution that’s energy environment friendly.
“I would say it’s going to move even further toward very high-capacity, whether it’s a one-petabyte SSD out a couple of years from now that runs at very low power and that can basically replace four times as many hard drives, or a very high-performance product that’s almost near memory speeds,” Matson stated. “You’ll see that the big GPU vendors are looking at how to define the next storage architecture, so that it can help augment, very closely, the HBM in the system. What was a general-purpose SSD in cloud computing is now bifurcating into capacity and performance. We’ll keep doing that further out in both directions over the next five or 10 years.”
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