Poorly designed storage systems can lead to significant inefficiencies in AI applications, causing expensive GPUs to remain idle. Storage can consume up to one-third of the power required for training deep learning models, and optimizing storage architecture is essential for maximizing GPU utilization. Intelligent storage management can actively enhance performance by adjusting data placement and caching based on compute demand.
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