Summary:
1. Innovations in storage technology enable enterprise AI use cases in healthcare.
2. The MONAI framework in medical imaging benefits from advances in storage technology.
3. The future of AI hardware will focus on high-capacity and high-performance solutions.
Article:
In the realm of enterprise AI, the integration of AI applications has become increasingly prevalent. From revolutionizing patient care through advanced medical imaging to bolstering fraud detection models and supporting wildlife conservation efforts, the use of artificial intelligence is expanding across various industries. However, a crucial bottleneck that often arises in this technological landscape is data storage.
At VentureBeat’s Transform 2025 event, industry experts delved into how innovations in storage technology are pivotal in enabling enterprise AI applications, particularly in the healthcare sector. Greg Matson, head of products and marketing at Solidigm, and Roger Cummings, CEO of PEAK:AIO, engaged in a discussion with Michael Stewart, managing partner at M12, shedding light on the significance of storage technology advancements in driving AI use cases.
One notable advancement highlighted during the conversation was the MONAI framework, a groundbreaking development in the realm of medical imaging. This framework facilitates faster, safer, and more secure medical imaging processes, allowing researchers to build upon it, iterate, and innovate with ease. PEAK:AIO collaborated with Solidigm to integrate power-efficient, high-performance, and high-capacity storage solutions, enabling MONAI to store over two million full-body CT scans on a single node within their IT environment.
As enterprise AI infrastructure continues to evolve at a rapid pace, the necessity for tailored storage hardware to cater to specific use cases becomes increasingly evident. Matson emphasized the importance of deploying high-capacity solid-state storage solutions for edge-use cases and training clusters, while high-performance storage solutions are essential for inference and model training processes.
To achieve peak performance at the edge, scaling storage down to a single node is crucial, allowing for closer proximity of inference to data. By addressing memory bottlenecks and integrating memory as a fundamental component of AI infrastructure, organizations can scale data and metadata seamlessly, significantly reducing the time required to derive insights from data.
Looking towards the future of AI hardware, industry experts foresee a continued focus on high-capacity and high-performance solutions. Cummings emphasized the importance of providing open, scalable solutions that operate at memory speed, utilizing cutting-edge technology to meet the evolving demands of organizations. As hardware requirements for training and inference data pipelines continue to escalate, the emphasis will shift towards high-speed SSDs and power-efficient, high-capacity solutions to drive AI advancements in the coming years.
In conclusion, the intersection of AI and storage technology is poised to shape the future of enterprise AI applications, with a clear trajectory towards high-capacity, high-performance solutions that cater to the evolving needs of organizations across industries.
