Using GPUs can significantly speed up training because they can handle what?

Using GPUs can significantly speed up training because they excel at parallel processing. In the context of artificial intelligence and machine learning, training models often involves performing a large number of computations simultaneously. This is especially true for tasks like deep learning, where operations like matrix multiplications and convolutions can be computed simultaneously for many data points.

GPUs are specifically designed to handle thousands of threads in parallel, optimizing the training process considerably compared to traditional CPUs, which are optimized for sequential processing. This capability allows GPUs to complete complex operations much faster by utilizing their architecture to process multiple calculations at once, thus greatly reducing the time it takes to train models.

In contrast, sequential operations are handled more efficiently by CPUs, as they are designed for tasks that require step-by-step processing. Structured queries pertain more to databases and data retrieval rather than the training of AI models, while unsupervised data sets are a type of data used in certain machine learning contexts, not a feature that enhances processing speed. Therefore, parallel processing is what truly enables GPUs to accelerate training in AI applications.