How Much is Enough? Fueling AI’s Energy Appetite

 

James Flanagan

HST401-A

How Much is Enough? Fueling AI’s Energy Appetite

Generative AI demands significant electricity. Driving a rapid increase in global data center power consumption. The International Energy agency reports that data center electricity consumption will rise from 460 terawatt hours in 2022 to anywhere between 620 and 1,050 terawatt hours by 2026. To put this in perspective, Russia’s total electricity consumption in 2021 was 996 terawatt hours. Generative AI is to blame for this rapid increase in data center power consumption.

               Google has been experimenting with AI and features generative AI responses on about 15% of its searches. This AI integration has substantially increased Googles power consumption. With 9 billion daily Google searches, Google will process over a billion AI requests. This is just the start of it, OpenAI, Microsoft and Meta have their own generative AI’s which likely have a similar volume of requests. This becomes problematic when looking at the power consumption of AI requests. An article published by Goldman Sachs Research found that one ChatGPT request uses 2.9 watt-hours of electricity, compared to the 0.3 watt-hours used for a google search. That’s almost ten times more power for an AI search. AI requests consume more power than standard searches due to the complex calculations needed, which are handled by GPUs, computer chips that are designed for high-speed processing of large datasets.

              Over the past decade, datacenters have needed to expand to keep up with the increasing usage of the internet. Despite this massive growth, the electricity consumption of data centers has remained consistent. As the increased internet and data center usage increased, the efficiency of hardware also increased which has been able to offset the power demand until recently. The increased efficiency of hardware encourages higher power consumption for AI. In How much electricity does AI consume? Alex de Vries, a PhD candidate at VU Amsterdam who has extensive experience studying the energy usage of GPUs, states “[Increased hardware efficiency] creates a natural incentive for people to just keep adding more computational resources, and as soon as models or hardware becomes more efficient, people will make those models even bigger than before.” The increased hardware efficiency ironically encourages AI developers to use more resources, because each efficiency gain enables them to create larger data centers to support bigger models. No amount of efficiency gains would be able to keep up with the increasing usage rate to offset the growing energy demand.

              Nvidia, the leading designer and manufacturer of GPUs for AI, recently released the the H100, which can do three times the work of their previous processor, the A100, while using the same amount of power. Though the H100 consumes 75% more power than the A100, it is preferred because of its higher efficiency. It just makes sense for data centers to use the power-hungry card because they get better value out of it. In the Forbes article AI Power Consumption: Rapidly Becoming Mission-Critical, Beth Kindig, the CEO and Lead Tech Analyst for the I/O Fund, says “Nvidia and other industry executive have laid out a path for GPU clusters in data centers to scale from tens of thousands of GPUs per cluster to the hundred-thousand plus range, even up to the millions of GPUs by 2027 and beyond.” The goal is not to reduce power consumption, but to maximize performance to run bigger AI models. More efficient GPUs simply encourage building bigger datacenters because it gives them justification to consume even more power. It is no longer becoming a big deal to increase power consumption by 75% because performance increases are so large.

              As much as I am personally skeptical of AI as it stands right now, it has value and real applications that justify its high energy demand. The current growth rate is alarming. As data centers expand to support AI workloads, there are concerns on where their energy comes from. Data centers are required to operate all day everyday all year long, so they most often get their power from fossil fuels because they provide consistent and dependable power. Unlike current renewable sources such as solar and wind. It is unsustainable to make AI dependent on fossil fuels, emissions, global warming and we are likely to run out of fossil fuels within the next century. If energy demand for AI keeps growing at its current rate, then it will always be dependent on unsustainable fossil fuels unless something changes. Until sustainable energy can reliably can meet AI’s growing energy needs, prioritizing efficiency in AI is essential to its future.

 

 

 

 

 

Sources

How much electricity does AI consume?

https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption

AI already uses as much energy as a small country. It’s only the beginning.

https://www.vox.com/climate/2024/3/28/24111721/climate-ai-tech-energy-demand-rising

AI Power Consumption: Rapidly Becoming Mission-Critical

https://www.forbes.com/sites/bethkindig/2024/06/20/ai-power-consumption-rapidly-becoming-mission-critical/

 

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