What is the primary purpose of using GPUs in deep learning?

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The primary purpose of using GPUs (Graphics Processing Units) in deep learning is to improve processing speed and performance. GPUs are designed to handle parallel processing, which is particularly beneficial for the computations involved in training deep learning models. These models often require large amounts of matrix and vector operations, which can be executed much more efficiently on a GPU than on a traditional CPU.

This capability allows deep learning practitioners to train models faster, iterate more quickly on model designs, and handle larger datasets. The thousands of cores present in a GPU enable it to perform multiple calculations simultaneously, which is critical for tasks such as training neural networks where many operations can be parallelized.

While other factors like power consumption can be a consideration in computing setups, the primary reason for leveraging GPUs in deep learning is their ability to significantly enhance speed and performance, making them indispensable tools in the field.

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