Artificial intelligence (AI) has the potential to solve some of the toughest problems facing humanity, including the challenges of climate change. But at the same time, the technology – especially generative AI – uses an enormous amount of computing power, and therefore also an enormous amount of energy. This is a problem, and one that will only get worse.
The amount of computing power required for advanced AI models is doubling every five to six months, and it is reasonable to assume that this will continue to increase as demand for the technology explodes. Data centers already consume 1.5% of the world’s electricity supply, and energy consumption is responsible for around 75% of the EU’s man-made greenhouse gas emissions.
Recent research from Gartner predicts that “AI could help reduce global greenhouse gas emissions by 5% to 10% by 2030.” However, Gartner predicts that “AI could consume up to 3.5% of the world’s electricity by the same year.”
The technology industry faces a clear challenge: finding solutions to curb AI’s energy demands, thereby unlocking the technology’s full potential to benefit humanity.
How AI consumes energy
The power required by AI is due to two factors: energy is consumed when training models, and during inference, where live data is passed through a trained AI model to solve tasks. Research published in the journal Joule suggests that inference could account for at least 60% of generative AI’s energy consumption, and that adding AI capabilities to web searches could increase energy demands tenfold. There is also often a higher volume of queries when using a generative model compared to a search engine, due to the back-and-forth dialogue as users try to achieve the desired result.
EMEA Director, HPC & AI at Lenovo Infrastructure Solutions Group (ISG).
As new use cases for generative AI emerge around text, images and video, there will also be an increase in the number of large models being trained, retrained and refined on a daily basis. The recent class of generative AI models requires a more than 200-fold increase in computing power to train compared to previous generations. Each new generation of models requires more computing power for inference and more energy to train. It is a constant cycle that continually adds demand to the required infrastructure.
On the hardware front, the graphics processing units (GPUs) used for AI can consume many times the energy of a traditional CPU system. Today’s GPUs can consume up to 700 watts, and an average installation requires eight GPUs per server. This means that a server can consume almost six kilowatts, compared to one kilowatt for the traditional two-socket server units that companies use for virtualization. So the big question is: how can we make this more sustainable?
Finding answers
The first step is to understand that sustainability is a journey: there is no single action that can ‘fix’ it when it comes to AI. But small steps can make a big difference. The computer industry is getting a loud, clear message to make better products that use fewer resources. This call comes from consumers and investors, but also increasingly from governments. Being energy efficient will be a regulatory requirement for organizations in the AI space in the future. Recent changes to the EU AI law will require operators to adopt state-of-the-art methods to reduce energy consumption and improve the efficiency of their AI platforms.
This can be achieved in three specific technical ways: first, in the chips used to generate the computing power, second, in the computers built for those chips, and third, in the data center. Sustainability is increasingly becoming a competitive differentiator for both chip makers and PC makers, and will become even more so as companies work to achieve ESG goals. According to research in the journal Nature, new developments such as analog chips could offer an energy-efficient alternative in the coming decades, perfect for neural networks.
In the data center, older air cooling technologies are already struggling to meet the high energy demands of AI, and customers are turning to liquid cooling to minimize energy consumption. By efficiently converting the heat generated by generative AI into water, customers can save up to 30-40% on electricity. Data centers powered by renewable energy sources will be critical to reducing AI’s carbon footprint. An ‘as a service’ approach to AI technology can also help minimize waste and ensure organizations use the latest, most durable hardware, without upfront capital expenditure.
AI for good
There is a trade-off around AI and its energy needs that needs to be discussed. Some use AI for the benefit of humanity, for example by improving medicine or tackling climate change, while others use it to generate entertainment. This raises questions about whether we should look at these different energy needs differently.
What is certain is that AI has enormous potential to do good, and is already having an impact in many areas. There are dozens of examples of how AI has the potential to mitigate the effects of climate change, with the UN pointing out that it not only helps to better predict and understand extreme weather, but also provides direct assistance to communities affected.
Furthermore, AI can provide new insight into the world around us, which could in turn help reduce greenhouse gas emissions. Smart cities have the potential to minimize emissions by saving minutes or hours of heating and air conditioning on a city scale, by learning people’s habits and gradually turning down the heating or air conditioning in the hour before they leave their homes. The technology can also regulate traffic through a city, so that vehicles move efficiently and traffic jams are prevented. Norwegian start-up Oceanbox.io is using predictive AI in its mission to understand the depths of the ocean, predicting the movement of currents that could help combat the spread of pollution and help ships reduce their gasoline consumption.
AI’s contribution to a net-zero world
There’s no doubt that AI consumes a lot of energy, but we can tackle this step by step – by using hot water cooling instead of air cooling, by using renewable energy sources to power data centers, and by innovations in chip and computer design.
In so many ways, AI can also bring positive impacts to humanity and become a powerful force to drive the world towards the UN Sustainable Development Goals. It has the potential to help us better understand and tackle the causes of climate change, reduce inequality and preserve our oceans and forests. When used responsibly, AI can go hand in hand with sustainability goals. As the world comes together to move towards climate neutrality, AI will play an increasingly important role.
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