As data science becomes central to discovery across disciplines, universities need IT infrastructure that supports innovation beyond traditional limits of compute speed and storage. Providing students and researchers with powerful systems enables them to pursue ambitious ideas without constraint. With AI now permeating every aspect of modern life and relying on increasingly large datasets, institutions must balance baseline computing capacity with scalable resources they can expand during peak demand. This “own the base, rent the spikes” approach ensures consistent performance while accommodating surges in deep learning and data analytics workloads.

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