NVIDIA: Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency

NVIDIA has published a detailed technical argument positioning performance-per-watt as the primary lens developers and infrastructure teams should use when evaluating AI hardware and system design choices. The piece argues that raw FLOP counts and even cost-per-token metrics are insufficient without factoring in energy consumption, which increasingly determines total cost of ownership at scale. As AI inference workloads grow, power constraints at the data center level are becoming a real bottleneck affecting how many models can run simultaneously and at what cost. For developers architecting inference infrastructure or advising on hardware procurement, this reframes the evaluation criteria away from peak throughput alone. The post also implicitly positions NVIDIA's own GPU lineup favorably in this metric, but the underlying argument about energy efficiency holds independent of vendor.