What practice is most effective for reducing energy consumption in an AI data center using NVIDIA GPUs?

Enabling NVIDIA's Adaptive Power Management features is the most effective practice for reducing energy consumption in an AI data center utilizing NVIDIA GPUs. This feature allows the system to dynamically adjust the power consumption of the GPUs based on the workload demands in real-time. By optimizing power usage, the system can significantly decrease energy waste during periods of lower activity, thereby maximizing efficiency.

Adaptive Power Management utilizes techniques such as clock speed adjustments and voltage scaling, which help to ensure that the GPUs use only the necessary amount of power required for the tasks at hand. This not only reduces energy consumption but can also lead to lower operational costs in data center management while minimizing the environmental impact of high power use.

In contrast, disabling power capping or running all GPUs at maximum clock speeds could lead to unnecessary energy consumption and increased heat generation, which can adversely affect the cooling systems and overall efficiency. Utilizing older GPUs might seem like a way to save on power costs, but older technology may not have the energy-efficient capabilities found in newer hardware, including advanced power management features.