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Using medical imaging as case-study, we demonstrate how Intel-optimizedTensorFlow on an x86-based server equipped with 2nd Generation Intel XeonScalable Processors with large system memory allows for the training ofmemory-intensive AI/deep-learning models in a scale-up server configuration. Webelieve our work represents the first training of a deep neural network havinglarge memory footprint (~ 1 TB) on a single-node server. We recommend thisconfiguration to scientists and researchers who wish to develop large,state-of-the-art AI models but are currently limited by memory.

Addressing the Memory Bottleneck in AI Model Training