How do GPUs and DPUs complement CPU functionality in a data center designed for AI workloads?

In a data center optimized for AI workloads, GPUs and DPUs play distinct yet complementary roles that enhance the overall functionality and performance of the computing environment. GPUs (Graphics Processing Units) are well-suited for the parallel processing required in training and running AI models. They excel in handling numerous tasks simultaneously, making them particularly effective for operations involving large datasets and complex neural networks. This parallelism is crucial for tasks like matrix multiplications and deep learning algorithm implementations, which are fundamental to AI computations.

On the other hand, DPUs (Data Processing Units) are designed to offload and optimize various data-centric tasks typically managed by the CPU (Central Processing Unit). This includes managing I/O operations, network processing, and storage data management within the data center. By handling these tasks, DPUs free up CPU resources, allowing them to focus more on computational tasks instead of getting bogged down with data management chores. This synergy between GPUs and DPUs enables a more efficient architecture tailored for AI workloads, as each type of processor can dedicate its strength to its specific domain, leading to enhanced overall system performance and responsiveness.

The other options do not accurately describe the distinct roles of GPUs and DPUs in the context of AI workloads, making them less representative of the actual