You are leading a project to enhance the energy efficiency of a data center that...

The best strategy is to use workload-specific benchmarks such as MLPerf-style AI benchmarks to understand energy efficiency in real-world scenarios. NVIDIA has argued that traditional PUE is not enough for modern AI data centers because PUE measures facility overhead relative to IT power, but it does not measure useful computational output. For AI infrastructure, the important question is not only how much power the facility consumes, but how much useful AI work is completed per unit of energy. NVIDIA’s discussion of next-generation efficiency metrics emphasizes useful work per energy and the need to account for real applications. Kilowatt-hours are useful for measuring energy consumed, but they do not by themselves capture productive AI output. Watts-used is only instantaneous power and does not reflect completed work. PUE remains useful for facilities management, but relying on it as the primary metric misses the performance and efficiency characteristics of accelerated computing. Workload-specific benchmarks allow teams to compare training, inference, and system performance against energy consumed in practical AI operations.