Which statement correctly highlights a key difference between GPU and CPU architectures?

The statement that highlights a key difference between GPU and CPU architectures is that GPUs are optimized for parallel processing, while CPUs are optimized for sequential processing.

GPUs (Graphics Processing Units) consist of a large number of smaller cores designed to handle thousands of threads simultaneously, making them particularly adept at tasks that can be parallelized, such as graphics rendering and certain machine learning workloads. This architecture allows GPUs to perform many operations concurrently, significantly accelerating processing tasks that benefit from parallel execution.

On the other hand, CPUs (Central Processing Units) are designed with fewer cores that are highly optimized for sequential processing. They excel at tasks that require complex decision-making and can process a single thread much faster than a GPU, but struggle with the same degree of parallel workload because they are not built to handle multiple operations at once in the same manner as GPUs.

This distinction is crucial in AI and deep learning applications, where workloads often involve large datasets and model training that benefit immensely from parallelization, thereby making GPUs the preferred choice for these tasks.