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As AI transforms enterprise operations throughout various industries, crucial challenges proceed to floor round knowledge storage—irrespective of how superior the mannequin, its efficiency hinges on the power to entry huge quantities of information rapidly, securely, and reliably. With out the correct knowledge storage infrastructure, even probably the most highly effective AI techniques will be delivered to a crawl by sluggish, fragmented, or inefficient knowledge pipelines.
This subject took middle stage on Day Considered one of VB Remodel, in a session centered on medical imaging AI improvements spearheaded by PEAK:AIO and Solidigm. Collectively, alongside the Medical Open Community for AI (MONAI) mission—an open-source framework for growing and deploying medical imaging AI—they’re redefining how knowledge infrastructure helps real-time inference and coaching in hospitals, from enhancing diagnostics to powering superior analysis and operational use circumstances.
>>See all our Remodel 2025 protection right here<<Innovating storage on the fringe of medical AI
Moderated by Michael Stewart, managing accomplice at M12 (Microsoft’s enterprise fund), the session featured insights from Roger Cummings, CEO of PEAK:AIO, and Greg Matson, head of merchandise and advertising at Solidigm. The dialog explored how next-generation, high-capacity storage architectures are opening new doorways for medical AI by delivering the pace, safety and scalability wanted to deal with large datasets in medical environments.
Crucially, each corporations have been deeply concerned with MONAI since its early days. Developed in collaboration with King’s School London and others, MONAI is purpose-built to develop and deploy AI fashions in medical imaging. The open-source framework’s toolset—tailor-made to the distinctive calls for of healthcare—contains libraries and instruments for DICOM help, 3D picture processing, and mannequin pre-training, enabling researchers and clinicians to construct high-performance fashions for duties like tumor segmentation and organ classification.
A vital design aim of MONAI was to help on-premises deployment, permitting hospitals to keep up full management over delicate affected person knowledge whereas leveraging normal GPU servers for coaching and inference. This ties the framework’s efficiency carefully to the info infrastructure beneath it, requiring quick, scalable storage techniques to totally help the calls for of real-time medical AI. That is the place Solidigm and PEAK:AIO come into play: Solidigm brings high-density flash storage to the desk, whereas PEAK:AIO makes a speciality of storage techniques purpose-built for AI workloads.
“We have been very lucky to be working early on with King’s School in London and Professor Sebastien Orslund to develop MONAI,” Cummings defined. “Working with Orslund, we developed the underlying infrastructure that enables researchers, docs, and biologists within the life sciences to construct on high of this framework in a short time.”
Assembly twin storage calls for in healthcare AI
Matson identified that he’s seeing a transparent bifurcation in storage {hardware}, with totally different options optimized for particular phases of the AI knowledge pipeline. To be used circumstances like MONAI, comparable edge AI deployments—in addition to situations involving the feeding of coaching clusters—ultra-high-capacity solid-state storage performs a crucial position, as these environments are sometimes house and power-constrained, but require native entry to large datasets.
As an illustration, MONAI was in a position to retailer greater than two million full-body CT scans on a single node inside a hospital’s present IT infrastructure. “Very space-constrained, power-constrained, and really high-capacity storage enabled some pretty exceptional outcomes,” Matson stated. This sort of effectivity is a game-changer for edge AI in healthcare, permitting establishments to run superior AI fashions on-premises with out compromising efficiency, scalability, or knowledge safety.
In distinction, workloads involving real-time inference and lively mannequin coaching place very totally different calls for on the system. These duties require storage options that may ship exceptionally excessive enter/output operations per second (IOPS) to maintain up with the info throughput wanted by high-bandwidth reminiscence (HBM) and guarantee GPUs stay absolutely utilized. PEAK:AIO’s software-defined storage layer, mixed with Solidigm’s high-performance solid-state drives (SSDs), addresses each ends of this spectrum—delivering the capability, effectivity, and pace required throughout your entire AI pipeline.
A software-defined layer for medical AI workloads on the edge
Cummings defined that PEAK:AIO’s software-defined AI storage know-how, when paired with Solidigm’s high-performance SSDs, permits MONAI to learn, write, and archive large datasets on the pace medical AI calls for. This mixture accelerates mannequin coaching and enhances accuracy in medical imaging whereas working inside an open-source framework tailor-made to healthcare environments.
“We offer a software-defined layer that may be deployed on any commodity server, remodeling it right into a high-performance system for AI or HPC workloads,” Cummings stated. “In edge environments, we take that very same functionality and scale it all the way down to a single node, bringing inference nearer to the place the info lives.”
A key functionality is how PEAK:AIO helps eradicate conventional reminiscence bottlenecks by integrating reminiscence extra straight into the AI infrastructure. “We deal with reminiscence as a part of the infrastructure itself—one thing that’s usually neglected. Our resolution scales not simply storage, but additionally the reminiscence workspace and the metadata related to it,” Cummings stated. This makes a major distinction for patrons who can’t afford—both by way of house or value—to re-run massive fashions repeatedly. By protecting memory-resident tokens alive and accessible, PEAK:AIO permits environment friendly, localized inference with no need fixed recomputation.
Bringing intelligence nearer to the info
Cummings emphasised that enterprises might want to take a extra strategic method to managing AI workloads. “You possibly can’t be only a vacation spot. It’s important to perceive the workloads. We do some unbelievable know-how with Solidign and their infrastructure to be smarter on how that knowledge is processed, beginning with how you can get efficiency out of a single node,” Cummings defined. “So with inference being such a big push, we’re seeing generalists turning into extra specialised. And we’re now taking work that we’ve finished from a single node and pushing it nearer to the info to be extra environment friendly. We would like extra clever knowledge, proper? The one method to try this is to get nearer to that knowledge.”
Some clear tendencies are rising from large-scale AI deployments, notably in newly constructed greenfield knowledge facilities. These services are designed with extremely specialised {hardware} architectures that carry knowledge as shut as attainable to the GPUs. To realize this, they rely closely on all solid-state storage—particularly ultra-high-capacity SSDs—designed to ship petabyte-scale storage with the pace and accessibility wanted to maintain GPUs constantly fed with knowledge at excessive throughput.
“Now that very same know-how is principally taking place at a microcosm, on the edge, within the enterprise,” Cumming defined. “So it’s turning into crucial to purchasers of AI techniques to find out how you choose your {hardware} and system vendor, even to ensure that if you wish to get probably the most efficiency out of your system, that you simply’re operating on all solid-state. This lets you carry large quantities of information, just like the MONAI instance—it was 15,000,000 plus pictures, in a single system. This permits unbelievable processing energy, proper there in a small system on the finish.”
