What could be a reason for suboptimal network throughput after integrating DPUs into an AI data center?

The correct choice highlights that if the DPUs (Data Processing Units) are not properly configured to offload network tasks, this can lead to CPU bottlenecks that negatively impact overall network throughput. DPUs are designed to take on specific processing tasks that can free up CPU resources, thus leading to improved performance and efficiency within a data center. When they are not configured correctly, the intended benefits of having the DPUs—such as handling networking tasks and reducing the load on CPUs—are not realized, leading to congestion and slower throughput.

When configuration issues arise, it may result in the CPU having to handle additional processing tasks that the DPU should be managing, ultimately leading to inefficient resource utilization and lower network performance. This situation underscores the importance of proper deployment and management of DPUs to maximize their advantages in an AI data center environment.

In contrast, the other options identify issues that may be relevant but do not directly explain the suboptimal throughput in the same context. For example, managing storage I/O might not directly correlate with the primary task of network optimization. Similarly, using outdated Ethernet cables may create a physical bandwidth limit, but it is not specific to the DPU integration. Lastly, while handling AI model training tasks may not be optimal for DP