Most of us are not aware that every time we prompt an AI for some text, an image or to create video footage that we are putting a greater demand on the grid. You might ask yourself: How much power is required to power AI? But that is not the right question. The real question is: how are we going to get get more energy to run them. On the bright side, if we cannot power all of the AI models now, then the odds of having Artificially Intelligent Overlords in the future is less of a threat than it was before you started reading this article.
Make no mistake, AI systems require huge amounts of electricity. And we are only at the beginning of this chapter in human/AI evolution. This article explains the energy needs of different AI models, how data centers manage these demands, and strategies to power them in less impactful ways as we transition to the future.
The rapid adoption of AI is significantly increasing global energy demand, particularly in data centers, which currently account for around 20% of total energy use in this sector.
Energy consumption varies greatly among different AI models, with larger and more complex systems requiring substantially more power, highlighting the importance of understanding individual power needs.
Integrating renewable energy sources and advancing energy-efficient AI technologies are critical strategies needed to mitigate AI’s growing carbon footprint and meet future energy demands.
The surge in artificial intelligence adoption is driving an unprecedented demand for energy—fast, scalable, and reliable. From powering data centers to supporting next-gen industrial systems, AI’s appetite for electricity is reshaping global energy needs. In this evolving landscape, transition fuels like LNG and Natural Hydrogen are stepping in as critical enablers:
LNG offers lower emissions than coal.
LNG can deliver consistent baseload power.
LNG provides a practical, near-term solution to bridge the gap between today’s infrastructure and tomorrow’s clean energy future.
Natural Hydrogen offers Zero Emissions, No CO₂ produced during extraction or use.
Natural Hydrogen offers Low Cost, Cheaper than green or blue hydrogen.
Natural Hydrogen offers Scalable, Abundant and compatible with existing infrastructure.
Exploring AI’s energy consumption reveals various facets of this critical issue, including the specific energy needs of different AI models and the role of data centers and cooling systems. This journey promises to be both enlightening and thought-provoking.
The rapid growth in AI’s energy demand is driven by significant investments from major tech companies like Google and Microsoft. AI models’ energy consumption is heavily influenced by their size and complexity, with larger models requiring more computational power. This concern is growing, especially with the increasing prevalence of AI technologies in daily life.
AI models are extremely energy intensive; the more sophisticated the AI model, the higher the energy required. For instance, generative artificial intelligence models like ChatGPT require substantial computational resources, translating into higher energy use. The complexity of these models often necessitates elaborate data center energy infrastructure, which, in turn, increases overall energy usage.
Think of it like your brain. Did you ever feel famished after a long bout of studying? It is the same here. Thinking burns energy and creating burns even more energy.
Understanding AI’s energy use involves examining the electricity consumption of data centers, the power demand of AI workloads, and the energy needed for computational resources. Machine learning and AI workloads significantly contribute to global electricity consumption.
AI models require significant energy to operate, with demand varying based on architecture and application. It will be no surprise to discover that larger, more complex models typically consume more energy. As AI models grow in capability, their performance often comes at the cost of increasing energy requirements. It would be the equivalent of the energy difference requirements between writing a film script and then shooting a movie.
In the AI industry, various models exhibit unique power demands. From large language models (LLMs) to image and video generation models, and real-time applications. Each type has its own energy usage profile. Understanding these differences is crucial for grasping the broader impact of AI energy consumption.
Large language models (LLMs) like GPT-4 consume varying amounts of energy based on their size and complexity. A single ChatGPT query uses about 0.3 watt-hours or 1,080 joules, which isn’t much. But when you consider that there are over 1 billion queries per day, you can see how it all stats to add up. And then if you think og Google, with over 8 billion queries per day and AI providing results for each one, now you’re getting the bigger picture.
Nonetheless, estimating the energy consumption of such models is complicated due to a lack of data on parameters, model architectures, and data center usage. The energy demand in AI models is significantly influenced by their size and how many parameters they contain. For example, Llama 3.1 (405B) consumes 3,353 joules per response, and 6,706 joules including cooling. About the same amout of power to microwave a poptart. These massive energy demands underscore the importance of understanding and managing the power needs of large language models.
The total estimated energy required for generating a standard-quality image using diffusion models is approximately 2,282 joules. Doubling diffusion steps increases the energy required for image generation to about 4,402 joules. How much energy diffusion models, commonly used for generating images and videos, demand significant energy.
Interestingly, the energy footprint of AI-generated video is generally smaller than that of traditional video production methods. This efficiency in generating images and videos showcases the potential for AI to transform the digital world while still facing the challenge of high energy consumption. But keep in mind that in order to get consistent useable video footage from AI is about 100 to 1. In other words, you will need to prompt 100 videos before you get one version that is useable in your movie. With no guarantee that the AI will be able to render the next character exactly the same.
Real-time AI applications, including conversational agents, demand a continuous power supply to maintain operational effectiveness. These applications have unique energy requirements that vary widely based on the model’s design and deployment.
Data centres are the unsung heroes powering the AI revolution. Key facts about their energy use include:
AI currently accounts for approximately 20% of the total energy use in global data centers.
The electricity consumption of data centers has been increasing at a rate four times faster than overall energy consumption in recent years.
As of 2023, data centres accounted for 4.4% of total US electricity usage, a figure expected to triple by 2028.
The carbon footprint of AI is escalating, partly due to the significant energy required for data center operations. Think of all of the emails in the world being sent successfully delivered at this exact moment. That is operations.
High-performance GPUs like NVIDIA’s A100 can consume 400 watts each, and training large models across thousands of these GPUs significantly raises electricity consumption, resulting in massive amounts of energy usage. The equivalent of running 4 100 watt tungsten bulbs simultaneously forever. Now multiply it by 1000, and a thousand more for all of the other GPUs operating around the world.
But there are cases of using AI to curb AI. Infrastructure efficiency, such as Google’s data centers having a Power Usage Effectiveness (PUE) of 1.12, affects overall energy consumption during model training. Effective resource use and energy management strategies for data centers can lead to a reduction of roughly equivalent 10% to 20% in global electricity consumption and lower energy costs, improving efficiency.
For example, Google’s DeepMind technology has reduced energy consumption for cooling data centers by 40%, showcasing significant efficiencies. As AI grows, more data centers will be needed, increasing the demand for computational resources. We just need to keep our cool.
Cooling systems are essential for maintaining optimal temperatures in data centers, particularly for high-density computing tasks like AI and HPC. Key points about these systems include:
They require large amounts of water, which can exacerbate sustainability challenges, especially in areas with limited water resources.
Liquid cooling is particularly effective for high-density servers.
Liquid cooling utilizes coolants to quickly dissipate heat from equipment.
Cooling methods include:
Immersion cooling: submerges IT components in fluids that absorb heat, suitable for ultra-high-density applications.
Evaporative cooling: uses water evaporation to efficiently remove heat, offering an alternative to traditional air cooling.
Hybrid cooling strategy: combines multiple techniques to enhance efficiency and adapt to varying IT equipment needs.
Optimizing airflow management with techniques like hot/cold aisle containment can significantly reduce cooling costs. Future innovations include AI-driven cooling systems that adjust based on real-time data, minimizing energy waste. Data centers designed for liquid cooling from the outset achieve superior thermal performance and energy efficiency. But they also add to the upfront costs of building them.
Key points about AI energy consumption and environmental impact:
AI energy consumption is predicted to exceed that of bitcoin mining by the end of this year.
In 2024, AI-specific servers located in US data centers consumed between 53 and 76 terawatt-hours of electricity. (1 TWh can power 100 million homes for one hour or 4 million homes for a year.)
This range indicates the significant energy demand associated with AI operations.
AI data centers tend to use electricity that is 48% more carbon-intensive than the US average due to their reliance on fossil fuels. But that is changing with the growing market for transition fuels like LNG and Natural Hydrogen.
It is no surprise that AI’s energy consumption is closely tied to the proliferation of AI technologies, which has drastically increased electricity demand in data centers. The International Energy Agency (IEA) emphasizes the need for energy efficiency improvements to manage this growing demand. AI’s energy use is a significant part of global electricity consumption, highlighting the importance of sustainable practices in the ai market. Furthermore, ai’s impact on energy consumption underscores the necessity for innovative solutions.
As AI technologies continue to evolve, their impact on global electricity consumption will only grow. The power demand from AI workloads is expected to put significant pressure on power grids, emphasizing the need for more energy-efficient solutions and renewable energy integration. In the United States this equates to a 1.5 trillion dollar update of the existing grid.
AI’s rapid growth is expected to lead to:
A tenfold increase in global energy consumption by 2026.
AI-specific servers’ energy consumption rising to between 165 and 326 terawatt-hours per year by 2028.
AI accounting for over half of the electricity consumed by US data centers by 2028, equating to enough energy for 22% of households.
The share of US electricity going to data centers is projected to triple:
It will increase from 4.4% to 12% from 2025 to 2028.
By 2030, electricity consumption by data centers in the U.S. is projected to rise to 606 terawatt-hours (TWh), accounting for 11.7% of the country’s total power demand.
The increase in energy demand for AI is driving the need for new infrastructure, including additional power generation capacity.
It is obvious that mitigating AI’s carbon footprint is essential for sustainable development and combating climate change. Data centers increasingly use renewable energy sources like solar or wind for cooling systems, enhancing overall sustainability, but it is not enough. Realistic integration of renewable energy sources is crucial for reducing the carbon footprint of AI data centers, but the demand is growing faster than the supply.
While transitioning to sustainable energy practices will help minimize the carbon footprints associated with AI, no one is sure where that energy will come from. Companies like Meta and Microsoft are pursuing nuclear energy to power their new data centers, aiming to reduce fossil fuel reliance. The growing energy demand from data centers highlights the need for investment in renewable solutions to meet future requirements, which means going back to the energy drawing board.
But there is some hope. Companies are increasingly exploring renewable energy sources to power AI data centers, including:
Solar and wind energy, aiming to reduce reliance on fossil fuels.
Nuclear power, as a strategy adopted by tech giants to reduce the carbon footprint of their AI data centers.
Locating data centers near renewable energy sources to minimize environmental impact.
Transition fuels, like LNG and Natural Hydrogen, which are easy to use with existing infrastructure with a lower carbon footprint than the carbon based fuels we have used up till now.
Integrating renewable energy sources in AI data centers can significantly reduce carbon emissions. And AI can help help by optimizing energy usage in smart grids, predicting demand and integrating renewable sources more effectively. Which in turn will reduce grid losses.
Techniques and approaches to lower the energy consumption required for AI systems include:
Model compression and quantization
Adopting smaller, specialized AI models, which can significantly lower energy consumption compared to larger, generalized models
New hardware designs, such as power-capped systems, which can cut AI energy usage by about 15% without greatly affecting performance
But ongoing research is essential to enhance the energy efficiency of AI systems in response to increasing power demand. These advancements are crucial for making AI more sustainable and reducing its overall carbon impact.
Meanwhile, legislators and regulators in the U.S. and the EU are actively seeking accountability for AI’s environmental impact. Policy and regulation are crucial for managing AI’s environmental impact by promoting sustainable practices while setting parameters to safeguard the future.
Effective regulation can ensure that AI development aligns with global climate goals and reduces its carbon footprint.
Training GPT-3 required around 1,287 MWh of electricity, equating to the energy used by an average American household over 120 years. This staggering figure highlights the scale of energy consumption involved in training large AI models, which requires as much electricity as the detailed data from the Lawrence Berkeley National Laboratory underscores the significant energy demands of such projects.
These examples emphasize the need for more energy-efficient AI systems and practices that do not consume too much energy. As AI advances, understanding and managing its energy usage will be crucial for sustainable development.
As artificial intelligence reshapes industries and drives exponential growth in global computing power, the world faces a new energy challenge. How to sustainably fuel the data centers, cloud infrastructure, and high-performance systems behind this digital transformation in the near future and moving beyond. Liquefied Natural Gas (LNG) is emerging as a key solution—providing a cleaner, more reliable energy source that can scale quickly and support 24/7 operations.
The Lost Soldier Project is strategically positioned to answer this call. Located in a resource-rich region with existing infrastructure, it offers a legacy opportunity to meet surging LNG demand tied to AI adoption and the broader energy transition. For investors and stakeholders looking to align with both innovation and sustainability, Lost Soldier represents a timely and powerful proposition.
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In summary, the rise of artificial intelligence is reshaping global energy demands in profound ways. From the immense power requirements of large language models and real-time applications to the crucial role of data centers and their cooling systems. It is obvious that AI’s energy consumption is a pressing issue that needs immediate attention. The projections for future energy demand underscore the importance of sustainable practices and innovations in energy efficiency.
Mitigating AI’s carbon footprint through renewable energy integration, advances in energy-efficient AI, and effective policy regulation is essential for a sustainable digital future. By understanding and addressing these challenges, we can harness the power of AI while minimizing its impact on our planet. The journey towards a more sustainable AI landscape is not just a necessity but an opportunity for innovation and growth.
A single ChatGPT query utilizes approximately 0.3 watt-hours, equivalent to 1,080 joules. This energy consumption is relatively low compared to many other digital applications.
Training GPT-3 consumed approximately 1,287 MWh of electricity, which is equivalent to the energy used by an average American household over 120 years. This significant energy requirement highlights the extensive resources involved in advanced AI model training.
The energy consumption of AI data centers is significant, representing around 20% of the total energy use in global data centers. This highlights the substantial impact AI technology has on energy resources.
To effectively reduce AI’s carbon footprint, it is essential to integrate renewable energy sources, adopt energy-efficient techniques, and implement robust policy regulations. These strategies collectively promote sustainability in AI operations.
LNG is crucial for AI’s energy needs as it offers a cleaner and more reliable energy source that can efficiently scale to support continuous operations, particularly for data centers. This makes LNG an effective near-term solution for powering the increasing demands of AI technologies.