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As AI applications increasingly permeate enterprise operations, from enhancing patient care through advanced medical imaging to powering complex fraud detection models and even aiding wildlife conservation, a critical bottleneck often emerges: data storage.
During VentureBeat’s Transform 2025, Greg Matson, head of products and marketing, Solidigm and Roger Cummings, CEO of PEAK:AIO spoke with Michael Stewart, managing partner at M12 about how innovations in storage technology enables enterprise AI use cases in healthcare.
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The MONAI framework is a breakthrough in medical imaging, building it faster, more safely, and more securely. Advances in storage technology is what enables researchers to build on top of this framework, iterate and innovate quickly. PEAK:AIO partnered with Solidgm to integrate power-efficient, performant, and high-capacity storage which enabled MONAI to store more than two million full-body CT scans on a single node within their IT environment.
“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 said. “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.”
Improving AI inference at the edge
For peak performance at the edge, it’s critical to scale storage down to a single node, in order to bring inference closer to the data. And what’s key is removing memory bottlenecks. That can be done by making memory a part of the AI infrastructure, in order to scale it along with data and metadata. The proximit …