What strategy effectively reduces energy consumption in an AI data center without significantly impacting performance?

Implementing dynamic voltage and frequency scaling (DVFS) is an effective strategy for reducing energy consumption in an AI data center without significantly impacting performance because it allows for the adjustment of power and performance levels according to workload demands. With DVFS, the system can decrease the voltage and frequency during periods of lower workload, which conserves energy, while scaling up when needed for more intensive tasks. This dynamic adjustment ensures that the performance remains optimal while also enhancing energy efficiency, making it ideal for environments with fluctuating demands.

In contrast, scheduling all AI workloads for nighttime might lower operational costs due to cheaper energy rates, but it does not inherently reduce energy consumption since the workloads still require full power during execution, regardless of the time of day. Reducing clock speed of all GPUs can save power but may negatively influence performance, potentially leading to slower processing and longer execution times for tasks. Consolidating workloads onto a single GPU can also reduce energy usage by minimizing the number of active components, yet it risks creating potential bottlenecks and inefficiencies if multiple tasks demand resources simultaneously, which may hurt overall performance. Thus, DVFS stands out as the most balanced approach to achieving energy efficiency while maintaining performance in an AI data center environment.