Which of the following best describes the primary benefit of using GPUs over CPUs for AI workloads?

The primary benefit of using GPUs over CPUs for AI workloads lies in their design, which allows them to efficiently handle parallel processing tasks. In AI and machine learning, tasks such as matrix multiplications or tensor operations are common and can be performed simultaneously across multiple data points. GPUs are equipped with thousands of smaller cores, enabling them to execute many threads concurrently. This architecture is particularly advantageous for training large neural networks, where numerous calculations need to be processed simultaneously.

While accuracy in AI model predictions and specific power consumption ratios can vary based on the implementation and workload, they do not fundamentally define the core advantage of GPUs for the parallel nature of AI tasks. Memory capacity can also differ between individual GPU and CPU models, but again, it is the parallel processing capability that is most crucial for efficiently handling the complex computations associated with AI workloads.