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Cracking AI’s storage bottleneck and supercharging inference on the edge
Tech

Cracking AI’s storage bottleneck and supercharging inference on the edge

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Last updated: July 7, 2025 1:41 pm
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Published: July 7, 2025
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As AI functions more and more permeate enterprise operations, from enhancing affected person care by superior medical imaging to powering complicated fraud detection fashions and even aiding wildlife conservation, a essential bottleneck typically emerges: information storage.

Throughout VentureBeat’s Rework 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 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 rapidly. 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 quickly, storage {hardware} more and more must be tailor-made to particular use instances, relying on the place they’re within the AI information 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 nicely 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 now we have 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, in an effort to deliver inference nearer to the information. And what’s secret is eradicating reminiscence bottlenecks. That may be performed by making reminiscence part of the AI infrastructure, in an effort to scale it together with information and metadata. The proximity of information to compute dramatically will increase the time to perception.

“You see all the massive deployments, the large inexperienced area information facilities for AI, utilizing very particular {hardware} designs to have the ability to deliver the information as shut as attainable to the GPUs,” Matson stated. “They’ve been constructing out their information 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 occurring in a microcosm on the edge and within the enterprise.”

It’s changing into essential to purchasers of AI methods to make sure you’re getting probably the most efficiency out of your system by operating it on all strong state. That means that you can deliver large quantities of information, and permits 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 pace, utilizing a number of the newest and best know-how on the market to do this,” Cummings stated. “That’s our objective as an organization, to supply that openness, that pace, and the size that organizations want. I believe you’re going to see the economies match that as nicely.”

For the general coaching and inference information pipeline, and inside inference itself, {hardware} wants will maintain growing, whether or not it’s a really high-speed SSD or a really high-capacity answer 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 principally change 4 occasions as many exhausting drives, or a really high-performance product that’s nearly close to reminiscence speeds,” Matson stated. “You’ll see that the large GPU distributors are taking a look at the best way to outline the following storage structure, in order that it could actually 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 maintain doing that additional out in each instructions over the following 5 or 10 years.”

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