As generative AI asks for more power, data centers seek more reliable, cleaner energy solutions – Deloitte
AI-driven data center power consumption will continue to surge, but data centers are not—in fact—that big a part of global energy demand. Deloitte predicts data centers will only make up about 2% of global electricity consumption, or 536 terawatt-hours (TWh), in 2025. But as power-intensive generative AI (gen AI) training and inference continues to grow faster than other uses and applications, global data center electricity consumption could roughly double to 1,065 TWh by 2030 (figure 1).1 To power those data centers and reduce the environmental impact, many companies are looking to use a combination of innovative and energy-efficient data center technologies and more carbon-free energy sources.
Nonetheless, it’s an uphill task for power generation and grid infrastructure to keep pace with a surge in electricity demand from AI data centers. Electricity demand was already growing fast due to electrification—the switch from fossil-fueled to electric-powered equipment and systems in the transport, building, and industrial segments—and other factors. But gen AI is an additional, and perhaps, an unanticipated source of demand. Moreover, data centers often have special requirements as they need 24/7 power supply with high levels of redundancy and reliability, and they’re working to have it be carbon-free.
Estimating global data centers’ electricity consumption in 2030 and beyond is challenging, as there are many variables to consider. Our assessment suggests that continuous improvements in AI and data center processing efficiency could yield an energy consumption level of approximately 1,000 TWh by 2030. However, if those anticipated improvements do not materialize in the coming years, the energy consumption associated with data centers could likely rise above 1,300 TWh, directly impacting electricity providers and challenging climate-neutrality ambitions.2 Consequently, driving forward innovations in AI and optimizing data center efficiency over the next decade will be pivotal in shaping a sustainable energy landscape.
Some parts of the world are already facing issues in generating power and managing grid capacity in the face of growing electricity demand from AI data centers.3 Critical power to support data centers’ most important components—including graphics processing unit (GPU) and central processing unit (CPU) servers, storage systems, cooling, and networking switches—is expected to nearly double between 2023 and 2026 to reach 96 gigawatts (GW) globally by 2026; and AI operations alone could potentially consume over 40% of that power.4 Worldwide, AI data centers’ annual power consumption is expected to reach 90 terawatt-hours by 2026 (or roughly one-seventh of the predicted 681 TWh of all data centers globally), roughly a tenfold increase from 2022 levels.5 As such, gen AI investments are fueling demand for so much electricity that in the first quarter of 2024, global net additional power demand from AI data centers was roughly 2 GW, an increase of 25% from the fourth quarter of 2023 and more than three times the level from the first quarter of 2023.6 Meeting data center power demand can be challenging because data center facilities are often geographically concentrated (especially in the United States) and their need for 24/7 power can burden existing power infrastructure.7
Deloitte predicts that both the technology and electric power industries can and will jointly address these challenges and contain the energy impact of AI—more specifically, gen AI. Already, many big tech and cloud providers are investing in carbon-free energy sources and pushing for net-zero targets,8 demonstrating their commitment to sustainability.
Hyperscalers plan massive expansion of gen AI data centers to help support growing customer demand
The surge in electricity demand is largely due to hyperscalers’ plans to build out data center capacity, globally.9 As AI demand—specifically gen AI—is expected to grow, companies and countries are racing to build more data centers to meet that demand. Governments are also establishing sovereign AI capabilities to maintain tech leadership.10 The data center real estate build-out has reached record levels based on select major hyperscalers’ capital expenditure, which is trending at roughly US$200 billion as of 2024, and estimated to exceed US$220B by 2025.11
Moreover, Deloitte’s State of Generative AI in the Enterprisesurvey noted that enterprises have been mostly piloting and experimenting until now.12 But as they experiment with getting value from gen AI, respondents are seeing tangible results and so intend to quickly scale up beyond pilots and proofs of concept. If usage grows as the technology matures, hyperscalers’ and cloud providers’ capital expenditure will most likely remain high through 2025 and 2026.
Two broad areas drive most of the electricity consumption in a data center: computing power and server resources like server systems (roughly 40% data center power consumption) and cooling systems (consume 38% to 40% power). These two are the most energy-intensive components even in AI data centers, and they will continue to fuel data centers’ power consumption. Internal power conditioning systems consume another 8% to 10%, while network and communications equipment and storage systems use about 5% each, and lighting facilities usually use 1% to 2% of power (figure 2).13 With gen AI requiring massive amounts of power, data center providers—including the hyperscalers and data center operators—need to look at alternate energy sources, new forms of cooling, and more energy-efficient solutions when designing data centers. Several efforts are already underway.
Gen AI is contributing to increased electricity demand
Data centers’ energy consumption has been surging since 2023, thanks to exploding demand for AI.14 Deploying advanced AI systems requires vast numbers of chips and processing capacity, and training complex gen AI models can require thousands of GPUs.
Hyperscalers and large-scale data center operators that are supporting gen AI and high-performance computing environments require high-density infrastructure to support computing power. Historically, data centers relied mainly on CPUs, which ran at roughly 150 watts to 200 watts per chip.15 GPUs for AI ran at 400 watts until 2022, while 2023 state-of-the-art GPUs for gen AI run at 700 watts, and 2024 next-generation chips are expected to run at 1,200 watts.16 These chips (about eight of them) sit on blades placed inside of racks (10 blades per rack) in data centers, and are using more power and producing more heat per square meter of footprint than traditional data center designs from only a few years ago.17 As of early 2024, data centers typically supported rack power requirements of 20 kW or higher. But the average power density is anticipated to increase from 36 kW per server rack in 2023 to 50 kW per rack by 2027.18
Total AI computing capacity, measured in floating-point operations per second (FLOPS), has also been increasing exponentially since the advent of gen AI. It’s grown 50% to 60% quarter over quarter globally since the first quarter of 2023 and will likely grow at that pace through the first quarter of 2025.19 But data centers don’t only measure capacity in FLOPS, they also measure megawatt hours (MWh) and TWh.
Gen AI’s multibillion parameter LLMs and the multibillion watts they consume
Gen AI large language models (LLMs) are becoming more sophisticated, incorporating more parameters (variables that enable AI learning and prediction) over time. From the 100 to 200 billion parameter models that were released initially during 2021 to 2022, current advanced LLMs (as of mid-2024) have scaled up to nearly two trillion parameters, which can interpret and decode complex images.20 And there’s competition to release LLMs with 10 trillion parameters. More parameters add to data processing and computing power needs, as the AI model must be trained and deployed. This can further accelerate demand for gen AI processors and accelerators, and in turn, electricity consumption.
Moreover, training LLMs is energy intensive. Independent research of select LLMs that were trained on more than 175 billion parameters of data sets demonstrated that they consumed anywhere between 324 MWh and 1,287 MWh of electricity for each training run … and models are often retrained.21
On average, a gen AI–based prompt request consumes 10 to 100 times more electricity than a typical internet search query.22 Deloitte predicts that if only 5% of daily internet searches, globally, use gen AI– based prompt requests, it would require approximately 20,000 servers (with eight specialized GPU cores in each of the servers) that consume 6.5 kW on an average per server to fulfill the prompt requests, amounting to an average daily electricity consumption of 3.12 GWh and annual consumption of 1.14 TWh23—which is equivalent to annual electricity consumed by approximately 108,450 US households.24
Data center demand could present challenges and opportunities for power sector transition
The electric power sector was already planning for rising demand: Many in the industry predicted as much as a tripling of electricity consumption by 2050 in some countries.25 But that trajectory has recently accelerated in some areas due to burgeoning data center demand. Previous forecasts in many countries have projected rising power demand due to electrification as well as increasing data center consumption and overall economic growth. But recent sharp spikes in data center demand, which could be just the tip of the iceberg, reveal the growing magnitude of the challenge.26
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As generative AI asks for more power, data centers seek more reliable, cleaner energy solutions – Deloitte, source
