The effect comes with innovation. The social media revolution changed how we share materials, how we buy, sell and learn, but also raise questions about the misuse of technology, censorship and protection. Every time we take one step forward, we also need to deal with the challenges, and AI is no different.
One of the major challenges for AI is its energy consumption. Together, dataments and AI currently use between 1-2% of the world’s electricity, but this figure is growing rapidly.
To complicate cases, these estimates change as our AI technologies and use patterns. In 2022, Datastenters, including AI and Cryptocurrency platforms, used about 460 Twhh electricity. In early 2024, it was estimated that they could use an additional 900 Twhh by 2030. In early 2025, this figure was fundamentally revised up to about 500 Twhh, due to large -scale more efficient AI models and datasantra technologies. In addition, to keep it in reference, the demand for the electric vehicle industry will reach 854twh by 2030, sitting at about 486twh with domestic and industrial heating.
However, this growth is still important, and everyone – provider and user equally – is a duty to ensure that their use of AI devices is as efficient as possible.
Global Environment Director, Ovhcloud.
How is AI Infrastructure becoming more powerful?
Whether it is Moore’s law that we will tell us that we will see more transistors on the same chip, or by the law of Komi, we are telling us that we will see more computation towards joules towards the energy used, computing has always become more efficient over time and GPU, AI “engine”, will definitely follow that trend.
When we look back between 2010 and 2018, the amount of datasentor calculations increased by 550%, but the use of energy only increased by 6%. We are already looking at such improvements in AI workload, and we have many reasons to be a bit more optimistic about the future.
We are also seeing an increase in adopting liquid cooling technologies. According to markets and markets, the market will increase by about ten times in the next seven years for liquid cooling in datasters. Water has a higher thermal conductivity than air, causing liquid cooling technique to become more powerful (and therefore cheaper) than the cooling of the air. This is ideal for AI workload, which consumes more power than non-AI workloads and run hotters. The cooling of water increases the power use effectiveness of the datasaver dramatically.
In addition, we also see significant innovation in liquid cooling area. Historically, the datastery has used the straight liquid to chip cooling (DLTC) where cooling plates sit on the CPU or GPU. As power (and resulting heat) load increases, we are seeing more immersion cooling, where the entire server is immersed in a non-conductive liquid and all components can be cooled simultaneously.
This format can also be combined with DLTC cooling, ensuring that the server component that usually gains more cooling power to ‘hot’ (eg CPU and GPU), while the rest of the server is cooled by the surrounding fluid.
How can we make AI more resourceful?
With power, we usually consider water to be a resource in ourselves. Consider a standard internet search. An AI-powered discovery uses approximately 25 ml of water, where a non-AI-operated search will use 50 times less: half a ml. On an industrial scale, in a recent trial case operated by the National Renewable Energy Laboratory, it was found that cooling of smart water reduced water consumption by about 56%; In their case, more than one million liters of water a year.
It is also important to think about the minerals using our infrastructure, as they are not present in separation. Reusion the re -use components, or when it is not, recycled them, can be a very efficient way to avoid both unnecessary purchases and reduce the environmental impact of AI.
As an example, consider a major component lithium in electric cars. Lithium may require half a million liters of water and can produce fifteen tons of CO2 for a ton metal. At the same time, there is a geopolitical element for our resource use: around a third of our nickel, used in the heatssink, used to come from Russia.
In many cases, it is also possible to recover some metals. For example, using pyrolysis, you can get “black” copper from complex components. Then, via electrolysis, separate the elements to recover pure copper, nickel, iron, palladium, titanium, silver and gold, converting e-waste into valuable assets. Although it will not be quite revenue stream, it is a strong example of the stability of being a revenue generator rather than a cost center!
How can users make their AI processes more power-efficient?
It is not enough for users to rely on datastery operators and equipment manufacturers to reduce energy consumption and carbon footprints. All organizations need to take care of energy consumption and ensure that their business is durable by design wherever possible.
To give an example on the hands, AI model training is rarely sensitive to delay, as it is not usually a user-supporting process. This means that it can be done anywhere and as a result, should be done in places that have more access to renewable energy. A company that training models in Canada instead of Poland, for example, the carbon footprint will be about 85% less.
At the same time, it is important to be practical about AI infrastructure. According to Intel PCF / OvhCloud LCA, a Nvidia H100 has a cradle-to-go-gate (manufacturing) carbon footprint, which is about three times more than an NVidia L4, stating how important it is for organizations to understand which GPU is needed.
In many cases, the latest GPU will be important-especially, when organizations are trying to apply in the market quickly-but in some, a low-kalpana and more durable GPU will do the same thing at the same time.
AI stability: an exercise in detailed attention in detail
Overall, there is no doubt that our power and resource consumption is going to increase in future; This is the price of progress. What we can do can ensure that we set an example to make every part of our AI supply chains and procedures as efficient as possible from gate-go, so that future development also includes it in its standard operation processes.
If we can earn partial profit wherever possible, they will add and ensure that today’s needs are not compromised with tomorrow’s world.
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