Which of the following features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?

The ability to execute parallel operations across thousands of cores (B) is the most crucial feature of

GPUs for accelerating AI workloads, particularly deep learning. Deep learning involves massive

matrix operations (e.g., convolutions, matrix multiplications) that are inherently parallelizable.

NVIDIA GPUs, such as the A100 Tensor Core GPU, feature thousands of CUDA cores and Tensor Cores

designed to handle these operations simultaneously, providing orders-of-magnitude speedups over

CPUs. This parallelism is the cornerstone of GPU acceleration in frameworks like TensorFlow and

PyTorch.

Large onboard cache memory(A) aids performance but is secondary to parallelism, as deep learning

relies more on compute than cache size.

Lower power consumption(C) is not a GPU advantage over CPUs (GPUs often consume more power)

and isn’t the key to acceleration.

High clock speed(D) benefits CPUs more than GPUs, where core count and parallelism dominate.

NVIDIA’s documentation highlights parallelism as the defining feature for AI acceleration (B).

Reference:NVIDIA GPU Architecture Whitepapers; Deep Learning Acceleration Guide on nvidia.com.