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GPU utilization of a deep-learning model running solely on a GPU is most of the time much less than 100%. The only way for multiple applications to run simultaneously is to cooperate with one another. More advanced GPUs require much more effort to utilize properly. Sharing a GPU by running multiple applications on the same GPU can minimize these idle times, utilize this unused GPU memory and increase the GPU utilization drastically. This can help teams and organizations significantly reduce rental costs, get more out of their already purchased GPUs, and be able to develop, train, and deploy more models using the same hardware.