AI's volatile power demand is damaging its own data centres

Naureen S Malik / Bloomberg

Aug 06

Rapid swings in AI data centres' power demands are straining vital equipment, causing batteries, generators and cooling systems to malfunction or wear out far sooner than expected.(Aug 6): Rapid ...

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They come at a time when investors and lenders are already jittery about hyperscalers' hundreds of billions of dollars of spending, amid growing concerns that these facilities could be depreciating much faster than estimated. \nThe problems are also a potential source of wider instability in power grids that are already straining to keep the lights on.\n\"AI does create very unusual power demand,\" said Amber Villegas-Williamson, principal consultant at the Uptime Institute in the UK, which advises electricity suppliers and data centres on standards and reliability. \"It's like over-revving your car wears out the engine faster than keeping a constant speed.\"\nData centres have been around for decades, gulping down electricity while they ensure that everything from your favourite streaming show to your online grocery order functions smoothly. But facilities designed for AI computing are different because their demand is so large and swings much more dramatically. \nPower increments equivalent to the consumption of factories, towns or even cities can appear and disappear within seconds, creating repeated shocks that connected equipment struggles to absorb. \nA gigawatt data centre is equivalent to a city the size of Boston, half of which can flicker on and off every few seconds, said Shannon Miller, founder and president of Mainspring Energy Inc, which works on micro-grid projects for industrial and data-centre customers. Some AI campuses planned in Texas, the Midwest and other states are more than five times bigger, consuming nearly as much power on average as New York City.\nAI data centres put particular strain on their power supply when they are training new models - a process that mobilises all of the graphics processing units (GPUs) in unison. Like the digital equivalent of bees swarming or a school of fish changing direction, hundreds of thousands of GPUs can power up and down on a millisecond basis.\nAI at times sees power usage spike as much as 50% above its design capacity, \"so a one gigawatt [GW] facility may use 1.5GW for a split second,\" said Drew Baglino, a former Tesla Inc executive who started Heron Power Electronics Co. The company is developing equipment to manage power fluctuations for Nvidia Corp's even more energy intensive next-generation of servers, due in 2027. \nMost equipment isn't designed for such big swings in power consumption. Jon Parrella, chief executive officer of energy-storage developer Terraflow Energy, likens it to driving a Ferrari and shifting straight from sixth gear to first. \"You can't swing that fast,\" he said. \nThis story is based on interviews with more than three dozen power experts in the US and Europe, including generators and other power suppliers, data-centre developers, grid operators, utilities, investors, standards developers, insurers and regulators, almost all of whom said the physical stresses on the facilities were evident. \nCranks on small natural gas combustion engines used to generate power at data centres have broken off, several of those people said. At xAI's Colossus computing facility in Memphis, Tennessee, gas-fired turbines had developed cracks, one of the people said. Batteries were installed within the system to help smooth out power swings and reduce the strain on spinning turbines, the person said.\nSpaceX, the parent company of xAI, didn't respond to requests for comment.\n\nTurbines have also cracked at much smaller data centres in the UK, said Andrew Cunningham, CEO of GeoPura Ltd, which is providing hydrogen for use in fuel cells that smooth out power flows at some sites.\nCracks or wear on devices can cause electrical arc flashes - when a current jumps between conductors - potentially damaging AI chips, said Jennifer Scanlon, CEO of UL Solutions Inc, which tests and certifies new technology.\nThere is a suite of equipment such as batteries, capacitors, transformers and flywheels that can help stabilise power flows. However, in the bid to build AI computing capacity quickly not enough of these technologies are being used at new data centres, several of the people said. Batteries that have been installed for this purpose have sometimes needed to replaced within months or even weeks due to the high strain, according to the Uptime Institute and other people working with operators.\nThis problem is seen at data centres around the world, from the Middle East and Africa, to Europe and the US, said Villegas-Williamson of the Uptime Institute. \nReliability problems\nThese issues are already causing delays or curtailing operations - and therefore revenue - at some AI computing facilities. \nTo ensure that a planned 2.67GW AI campus in West Texas can achieve the 99.999% reliability required by Microsoft Corp, extra time was baked into the schedule for engineering, said Chris James, CEO of Joulent Inc, which is developing the facility with energy giant Chevron Corp. This means power delivery will begin in 2028 instead of 2027, he said. \n\nJensen Huang displays the new Blackwell GPU chip during the Nvidia GPU Technology Conference in San Jose, California in 2024.\n \nJensen Huang displays the new Blackwell GPU chip during the Nvidia GPU Technology Conference in San Jose, California in 2024.\n\nIf essential equipment breaks down prematurely, \"the financial consequence is not primarily replacing a pump or a breaker or some power component - it's the the value of that expensive compute capacity not generating revenue because it's offline,\" said Jason Hoffman, chief strategy officer at data-centre builder and operator Switch.\nThe cost of downtime in terms of lost revenue varies widely, with estimates ranging from thousands to hundreds of thousands of dollars per minute, depending on the type of facility and its workload.\nData centres are built on the assumption that, once they are online, they will operate around the clock 365 days a year, said a person involved in the financing of such facilities. In reality, some are seeing uptime closer to 80%, and unless resolved this could hit investors in certain projects in the next 12 to 24 months, the person said. \nAny issues with reliability add to wider concerns about the returns generated from hundreds of billions of dollars of planned AI investments. The rate of depreciation of another crucial piece of equipment at data centres, the GPU racks themselves, has raised questions about whether the industry can be as profitable as it promises. \nThese problems with reliability also have the potential to destabilise the wider power grid. This sprawling web of high voltage lines, transformers and power plants requires constant calibration, something that has become more challenging with each passing year due to aging equipment, rising demand and extreme weather.\nThe expansion of intermittent wind and solar generation, which often result in big swings in supply from one hour to the next, are already a destabilizing force. AI data centres can put this volatility on steroids. \n\"These loads are extremely dynamic or fluctuating, which causes grid instability and can lead to, if not corrected, potential blackouts or power outages,\" said Sreemant Roy, a power-quality expert and global offer manager at Schneider Electric in Nashville, Tennessee. Of particular concern is a data centre's ability to cause sub-synchronous oscillations in the power flow, which can damage equipment connected to other parts of the network, he said. \n\"That has made utility companies globally very worried,\" Roy said. \nWithin the last two years, the North American Electric Reliability Corp (NERC) - the top US regulatory body establishing standards aimed to keep the lights on - has repeatedly warned and issued alerts that data centres are one of the greatest risks to grid stability.\nNERC evaluated more than 33GW of operational data centres in the US an found about three quarters of their load models \"are insufficient to represent data-centre dynamic behaviour,\" according to a September report. Earlier this year, the agency issued a rare level-three alert requiring big data centres to address these immediate risks and submit their responses by Aug 3.\nFrom top to bottom, the AI industry is aware of these issues and actively working on solutions. \nNvidia started working more closely with power experts when it developed Blackwell GPUs, which were first released in 2024 and have become pervasive in data centres, said Dion Harris, senior director of hyperscale infrastructure solutions at the chipmaker. \n\"We're building the chips and processors\" but also using them in the company's own data centres, Harris said. Nvidia is working to make its deployments a lot smoother \"both on the data-centre build out, design and engineering phase, as well as on the power delivery.\" \nData-centre users have deployed techniques to smooth out the power fluctuations of AI workloads by running side computations - essentially dummy math that isn't part of the training process - to keep GPUs operating steadily. However, this approach has been criticised for wasting electricity at a time when power demand is surging. \nLast year, the National Laboratory of the Rockies near Denver, Colorado set up a test-bed on behalf of the US Department of Energy to figure out how to integrate AI safely onto the grid, said Martha Symko-Davies, the NLR's programme manager for the DOE's office of electricity. \nThe site has GPUs and power generation on site that developers can use to figure out if their setup can handle the variability of AI. One power supplier said they will use the facility to test batteries, software and other equipment intended to smooth out oscillations between the data centre and grid that can cause damage on either side.\n\"We have the opportunity right now to get it right,\" said Symko-Davies.\nUploaded by Felyx Teoh","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"-1","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}