chones - stock.adobe.com
Gartner warns GenAI demand will lead to datacentre energy shortages
IT market watcher Gartner is warning enterprises that the growing demand for generative AI workloads will have a knock-on impact on energy supply and pricing in future years
The surging demand for generative artificial intelligence (GenAI) workloads is likely to lead to operational constraints in AI datacentres due to energy shortages, according to Gartner.
The analyst is predicting that 40% of existing AI datacentres will be affected by power supply issues by 2027, because of how rapidly energy consumption is expected to rise over the coming years due to more server farms hosting AI workloads.
According to Gartner’s forecast, the amount of power needed by datacentres to run incremental AI-optimised servers will hit 500 terawatt-hours (TWh) per year in 2027, which is 2.6 times higher than in 2023.
“The explosive growth of new hyperscale datacentres to implement GenAI is creating an insatiable demand for power that will exceed the ability of utility providers to expand their capacity fast enough,” said Bob Johnson, vice-president analyst at Gartner.
“In turn, this threatens to disrupt energy availability and lead to shortages, which will limit the growth of new datacentres for GenAI and other uses from 2026.”
As previously reported by Computer Weekly, the soaring demand for GenAI offerings has been cited by various hyperscale cloud suppliers as a source of revenue growth in recent quarters, with both Microsoft and Amazon committing to building new datacentres to accommodate this demand.
Read more about datacentres and energy
- Hyperscalers are looking at nuclear to power their energy-hungry datacentres and meet net-zero targets – but regulations may curb their plans.
- Operators have revealed troubling increases in their carbon emissions of late, but could these have been prevented?
However, Gartner said these facilities may take longer than expected to come online due to the power supply issues.
“New, larger datacentres are being planned to handle the huge amounts of data needed to train and implement the rapidly expanding large language models (LLMs) that underpin GenAI applications,” said Johnson. “However, short-term power shortages are likely to continue for years, as new power transmission, distribution and generation capacity could take years to come online and won’t alleviate current problems.”
And enterprises that are betting big on GenAI for their future business growth need to take this into account, because power supply issues will likely lead to rising energy costs, he said.
“Significant power users are working with major producers to secure long-term guaranteed sources of power independent of other grid demands,” said Johnson. “In the meantime, the cost of power to operate datacentres will increase significantly as operators use economic leverage to secure needed power. These costs will be passed on to AI and GenAI product and service providers as well.”
Another complicating factor in all this for enterprises is that, to meet the growing energy demands GenAI is likely to cause, it’s likely that non-renewable sources will need to be used, and this could spell bad news for enterprises and their pursuit of net-zero.
“The reality is that increased datacentre use will lead to increased [carbon] emissions to generate the needed power in the short term,” said Johnson. “This, in turn, will make it more difficult for datacentre operators and their customers to meet aggressive sustainability goals relating to [carbon] emissions.”
