Which solution would likely resolve storage bottlenecks in a medical image analysis AI model deployed on an NVIDIA DGX system?

Implementing NVMe over Fabric (NVMe-oF) would likely resolve storage bottlenecks in a medical image analysis AI model deployed on an NVIDIA DGX system due to its ability to significantly improve data transfer speeds between storage devices and compute resources. NVMe-oF enables the transmission of NVMe commands over a network, thereby allowing for lower latency and higher input/output operations per second (IOPS) compared to traditional storage protocols. This rapid data access is essential for applications like medical image analysis, where large amounts of data must be processed quickly to ensure timely results.

In contrast, using a cloud-based storage solution, while potentially scalable, may introduce latency issues and bandwidth limitations depending on network conditions. Reducing the resolution of medical images might yield smaller file sizes but at the cost of losing critical information necessary for accurate analysis. Switching to traditional spinning disk storage could lower costs but would likely exacerbate storage bottlenecks due to its inherently slower data access speeds compared to NVMe solutions. Therefore, employing NVMe-oF optimally addresses the need for high-performance data handling in AI medical image analysis.