NVIDIA advocates for a holistic view of data center efficiency that moves beyond infrastructure metrics like PUE. The key strategy is to focus on computational efficiency, measured as performance-per-watt. Upgrading to newer GPU architectures provides a massive leap in performance for a given power envelope. While this might increase power density at the rack level, it drastically reduces the time and total energy required to complete AI workloads (energy-to-solution). This approach yields greater overall efficiency and lower Total Cost of Ownership (TCO) than focusing solely on minimizing infrastructure overhead.

A: Focusing only on PUE ignores the efficiency of the actual compute work, which is the primary driver of energy consumption in an AI data center.

B: Aggressive power-capping can hurt performance so much that jobs run longer, potentially consuming more total energy to reach a solution.

D: While liquid cooling improves PUE, it is an infrastructure-only optimization and does not address the core computational efficiency of the servers.

1. NVIDIA Corporate Blog. (2023, May 29). "Accelerated Computing Is Sustainable Computing." This article explicitly states, "The better way to look at data center energy use is to measure the amount of work done per watt of power..." It champions performance-per-watt over PUE.

2. NVIDIA GTC Keynotes (e.g., GTC 2023, 2024). Jensen Huang frequently discusses how accelerated computing saves costs and energy by dramatically speeding up workloads, emphasizing performance-per-watt and TCO as the critical metrics for modern data centers.