Which action should be prioritized to optimize AI data center management and energy efficiency while ensuring high performance?

Implementing GPU workload scheduling based on real-time performance metrics is crucial for optimizing AI data center management and energy efficiency while ensuring high performance. This practice allows for dynamic allocation of resources based on current workloads and system demands, leading to more efficient usage of available hardware. By focusing on performance metrics, data centers can adjust allocations to prevent bottlenecks, optimize resource utilization, and ultimately enhance throughput without unnecessary energy expenditure.

This approach also increases energy efficiency by ensuring that only the necessary resources are actively engaged, which mitigates increased power consumption that may occur if all GPUs operate at full capacity regardless of demand. Moreover, by adapting to fluctuations in workloads, GPU scheduling helps reduce thermal buildup, thus minimizing the need for excessive cooling measures.

The other choices, while potentially beneficial in certain contexts, do not address the core challenge of balancing performance with energy efficiency effectively. Increasing the number of active cooling systems, for example, can be costly and might not tackle the underlying inefficiencies in workload distribution. Disabling power management features on GPUs could lead to higher energy use and thermal issues, while simply installing additional GPUs may not solve performance issues if the workloads are not managed effectively.