To optimize power efficiency in an AI data center, which action is most effective?

Implementing dynamic power scaling on GPUs based on workload is the most effective action for optimizing power efficiency in an AI data center. Dynamic power scaling allows the data center to adjust the GPU's power consumption in real time, depending on the current workload requirements. When the workload is light, the GPUs can reduce their power consumption, while during heavier computations, they can ramp up to deliver the necessary performance. This flexibility leads to better energy usage overall, as it minimizes waste and reduces operating costs.

The other choices do not prioritize power efficiency effectively. Scheduling all deep learning tasks to run simultaneously may lead to increased power consumption as multiple tasks could overload the system, leading to waste. Consolidating all workloads onto high-power GPUs could also exacerbate power inefficiencies, as it does not consider the varying demands of different tasks and could lead to situations where resources are underutilized or overutilized. Lastly, replacing DPUs with additional GPUs might increase the computational capacity but does not necessarily translate to improved power efficiency, as it does not involve optimizing how existing resources are utilized. Dynamic power scaling offers a nuanced approach that directly addresses workload variability and power consumption, making it the most effective for optimization in this context.