Nvidia says only up to 70% of data center power goes to AI work
Speaking at Vertiv's annual EMEA event, Nvidia vice president Rod Evans said that of the electricity an AI data center draws from the grid, at most 70% is used for AI processing, with 30% lost to inefficiency: 20% to cooling, power distribution and conversion, and a further 10% to incorrectly configured racks, failures and restarts. TechRadar noted that its headline cites a figure as low as 60% while the body gives a maximum of 70%. Nvidia and Vertiv, which have been co-designing next-generation data centers since early 2026, say some of that loss can be recovered through better design, and argue that grid connection rather than chip supply is now the main bottleneck on AI data center capacity.
Nvidia and Vertiv executives quoted in the report say grid connection, not chip shortages, is now the main constraint on AI data center capacity, with some facilities waiting several years for power to match their capacity.
- Nvidia claims that for each gigawatt an AI data center uses, only 70% is used for AI work
- Inefficiencies include cooling and power distribution, failures, and restarts
- While a chip shortage was considered to be the main bottleneck to AI data center capacity, it is now a facility’s connection to the grid
AI data centers are inefficient, says Nvidia, with various physical and systems issues contributing to power use beyond AI processing. This opinion, shared by data center builder Vertiv at its recent annual EMEA event, underpins future development steps, in which it plans to assemble sustainable facilities.
Vertiv and Nvidia have been collaborating since early 2026 on co-designing the next generation of data centers, with Vertiv providing power and infrastructure management for Nvidia’s GPUs.
With a maximum of 70% of electricity drawn from the grid used for AI processing, the remaining 30% represents inefficiencies that optimized data center designs for the future should reduce.
That's not efficient
Although the various inefficiencies affecting AI data centers may not come as a surprise, the scale of them might. According to Nvidia – via its Vice President of Supercomputing, AI Cloud Infrastructure, and AI/HPC for EMEA Rod Evans – that 30% consists of infrastructure and system issues.