What scenario best illustrates the advantage of using DPUs in the transformation to an AI-optimized data center?

Using DPUs to handle network traffic encryption and decryption exemplifies a significant advantage in transforming to an AI-optimized data center because it allows CPUs to focus solely on compute-intensive AI tasks without the overhead of managing network operations. In traditional data center architectures, CPUs often bear the full burden of both computation and network processing, which can lead to bottlenecks, especially when dealing with high volumes of data or complex encryption algorithms.

By delegating network tasks to DPUs, which are specifically designed to handle such workloads efficiently, the data center can achieve greater overall performance. This not only accelerates data transmission and enhances security but also optimizes the overall resource allocation across the infrastructure. As a result, more CPU cycles are available for actual AI computations, thus improving the efficiency and speed of AI workflows.

In contrast, the other scenarios focus on different aspects of workload management, such as offloading tasks related to model training or memory management. While these are certainly important for overall system efficiency, the specific example of offloading encryption/decryption tasks directly impacts the optimization of CPU resources for AI applications in a way that is particularly crucial for high-performance environments.