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Artificial intelligence promises lightning-speed efficiency for businesses and consumers, but powering this technology requires vast amounts of energy. Whether it’s training a new AI model, assessing or optimizing performance, or even maintaining it, supporting AI consumes astronomical quantities of watts. While this energy consumption supports some of our favorite AI solutions, its exponential increase raises serious environmental concerns. An understanding of AI energy consumption is relevant not just to the builders of the technology but to everyone who interacts with it.
KEY TAKEAWAYS
- •AI models—particularly large language models (LLMs)—consume vast amounts of energy due to the powerful hardware and infrastructure required to process their complex computations. (Jump to Section)
- •AI’s growing energy demands raises dire environmental concerns, which can be lessened by using more energy-efficient hardware, optimization techniques, and reliance on renewable energy sources for data centers. (Jump to Section)
- •The future of AI rests on striking a balance between its rapid technological advancements and its environmental sustainability. International regulatory standards will be an important driver of sustainable and responsible deployment of AI systems. (Jump to Section)
TABLE OF CONTENTS
What is AI Energy Consumption?
As the phrase suggests, artificial intelligence energy consumption refers to the amount of electricity required to run AI systems, from training to deployment to maintenance. Large AI models like GPT-4 or DALL-E require a great deal of computation resources in order to run successfully. The current version of ChatGPT powered by GPT-4, for example, has about 1.8 trillion parameters that help determine how it responds to inputs. For context, that’s six times larger than the 175 billion parameters of GPT-3, and 1,200 times bigger than GPT-2. It took the same amount of power to train GPT-3 as 120 average American homes consume in a year. Imagine how much power it took to train GPT-4.
Remarkably, because the amount of energy required to train an AI model is so massive, the amount of CO2 it emits is greater than many other energy-intensive tasks.
Factors Influencing AI Energy Consumption
From its use of advanced hardware to the sophistication of its models to the sheer volume of data needed, an array of complex and interlocking factors influence the amount of energy required to power AI.
Hardware and Infrastructure
Specialized processors like the graphic processing units (GPUs) and tensor processing units (TPUs), which form the backbone of AI technologies need vast levels of energy to process the complex computations for which AI is famous. This is why there’s now a gradual shift toward measuring “power consumption per chip” and not just total compute and memory. The maximum power consumption of NVIDIA’s A100 GPU, used in many of the modern Al training setups, is about 400 watts per GPU. Training a big model may require over 1,000 A100 GPUs, using up to 400 kilowatts per hour. More recent generations of these hardware components like the NVIDIA H100 deliver improved performance per watt, consuming less energy than previous generations.
Then there are the data centers where AI models get trained and deployed—their power usage used to be relatively stable but has skyrocketed with the AI boom, leading to an estimated growth of 160 percent by 2030, according to Goldman Sachs. All that power is needed constantly to keep the power on and the hardware cool.
Algorithmic Complexity and Model Size
The main determinant of AI models energy consumption is their size. AI models with billions or trillions of parameters require massive computational power to train and deploy them. And as they grow in size, their energy demands increase as well. Goldman Sachs reports that one ChatGPT query needs nearly 10 times as much electricity to process as a Google search. This is largely because of the billions of inferences it has to make to respond to users’ queries. In addition to their size, the complexity of the algorithms they are built on also affects power consumption. Since algorithm optimization is yet to match the pace of model size increase, energy efficiency is typically sacrificed first.
Data Volume and Training Requirements
The more data an AI model processes, the more computational power is needed to run it. Training a model on terabytes of data means that thousands of machines are running for extended periods, further increasing the overall energy usage. For example, training the BERT machine learning framework on a large dataset requires approximately 64 tensor processing unit (TPU) chips and about four days. The complexity of the model, the time required to train it, the choice of optimization techniques, hyperparameter AI tuning processes, and other training needs have a direct implication on how much energy is consumed. Generative AI models like GPTs and DALL-E are typically the most energy-intensive AI systems for these same reasons.
The Environmental Impact of AI Energy Consumption
There’s been a push for AI companies to be more transparent about the impact of AI on the environment. Research has since proven that the impact is quite significant, causing troubling changes in greenhouse gas emissions, waste generation, and ultimately human health.
Carbon Footprint, Greenhouse Gas Emissions, and Climate Change
There’s a causal relationship between the energy AI consumes and the carbon footprint it leaves. Today, most data centers are powered by non-renewable sources of energy that result in high emission of greenhouse gasses. For example, a study by researchers at the University of Massachusetts showed that training a single large AI model like BERT could generate a carbon footprint of 626,000 pounds of CO2—equivalent to five times the lifetime emissions of one car. As AI usage grows, this carbon impact is expected to increase substantially. A BBC news report predicted that by 2027, the AI industry may consume more energy annually than the Netherlands. The amount of extra burden this will place on the environment in terms of emissions outputs and environmental degradation is deeply concerning.
Resource Depletion and Waste Generation
Apart from carbon emissions, the production of the GPU and other hardware needed to run AI models raises additional environmental concerns. Electronic waste, for example, contains dangerous chemicals that contaminate the environment when discarded. The World Economic Forum (WEF) already projects that by 2050, generated e-waste will have surpassed 120 million metric tonnes. To visualize that, imagine nearly 12,000 Eiffel Towers of waste. Increasing demand for natural resources like water and earth metals to power AI hardware will cause not only environmental degradation but also geopolitical tension.
Potential Health Risks and Human Impact
The culmination of these environmental risks often finds their way to the human body. For example, the intensification of emissions from data centers and energy generation facilities affects air quality. According to the World Health Organisation (WHO), air pollution is the cause of seven million deaths per year.
Strategies for Reducing AI Energy Consumption
AI power consumption is quickly becoming a mission-critical challenge, prompting humankind to refocus on strategies to reduce and mitigate AI’s impact. These strategies include optimization techniques, energy efficiency, and renewable energy.
Optimization Techniques for Deep Learning Models
Optimization is one effective way of managing AI’s energy appetite. Model pruning, knowledge distilling, and quantization are three notable examples. Model pruning is the method of selectively removing redundant neurons from neural networks while at the same time reducing model size, with little or no loss in performance. This technique and knowledge distillation—in which a small model is trained to replicate the behavior of a larger model—are both favored by the AI development community.
Quantization reduces the numerical precision of AI calculations—which raises issues among developers—but it leads to as much as 50 percent computational cost savings. With these techniques, AI systems scale down computational costs to more manageable levels and reduce energy consumption.
Energy-Efficient Hardware and Cooling Technologies
Another important strategy is the development of energy efficient hardware. For instance, NVIDIA’s A100 GPUs purpose-built for AI workloads are more energy efficient than previous-generation GPUs. Some data centers adopt cooling systems like liquid immersion cooling, which can reduce the energy used by up to 95 percent. Front-of-the-meter (FTM) solutions—a utility company or other third party energy provider’s connection technology that enhances reliability and supports the electrical grid—can also play a role in helping data centers and AI infrastructure draw power more sustainably and cost-effectively.
Renewable Energy Sources for Data Centers
In an attempt to reduce the environmental footprint of AI’s energy consumption, some companies are also leaning toward renewable energy. For instance, a portion of Google’s data centers now run solely on renewable energy, which offsets the carbon footprint incurred in training and deploying AI. Other companies are joining in, exploring options like drawing power from solar and wind farms, and otherwise considering methods to drastically cut emissions arising from power supply of AI—which serves both the environment and the bottom line.
Future Directions and Challenges
As humankind fully realizes the need to manage AI’s massive consumption of natural resources, expect greater focus on balancing AI upside with its true costs, more sustainable AI technologies, and greater regulation.
Balancing AI Benefits with Environmental Costs
The challenge moving forward will be to harness the incredible AI potential while also understanding that it comes at an environmental cost. AI is capable of greatly enhancing healthcare, finance, and education, but we need to think of ways to make sure AI advances do not compromise our environment, our health, and our future. To achieve this balance, practical approaches like new methods in algorithmic efficiency and energy-efficient hardware design will be needed. But in a larger sense, businesses must fully understand the larger implications of using AI in terms of natural resources and the environment.
Embracing Emerging Technologies for Sustainable AI
As a way to reduce energy costs for AI, emerging technologies like neuromorphic computing, which mimics the human brain’s neural structure, are being studied. That’s 1,000 times less energy than traditional CPUs, making these chips are promising for sustainable AI research and hinting at far greater improvements in the future.
Legislating New Regulatory Standards and Industry Initiatives
Fortunately, governments and businesses are realizing the need for regulating AI’s environmental footprints. A number of green AI initiatives, for instance, emphasize that organizations establish industry-wide standards for energy efficient AI development. AI companies could soon be required by regulation to reveal the carbon ‘footprint’ of their models, as is the case in other industries.
Frequently Asked Questions (FAQs)
Current estimates of AI energy consumption are typically created by measuring data center consumption, as they are the critical infrastructure supporting the AI ecosystem. The global data center power usage, according to the International Energy Agency (IEA), was estimated in 2022 as 240-340 terawatt hours (TWh). In addition, the forecast for AI energy consumption by 2030 is 20 percent of global electricity supply, if current growth trends continue.
Yes, it can. Although AI data centers are currently contributing to increased carbon emissions, the World Economic Forum reports that AI tools can also help to facilitate energy transition and help to improve energy efficiency. Adopting AI technologies can optimize energy use in different industries, therefore reducing overall electricity consumption and carbon emissions.
Yes. The development, deployment and maintenance of AI systems require significant amounts of both energy and water. Energy is needed to power the systems and run the queries, while water is required to cool the hardware, especially in data centers.
Yes, but it’s gradual. Advances in model design and the development of more efficient AI accelerators help reduce AI energy usage. New cooling technologies are already being explored in data centers and this can help with AI energy efficiency.
Not exactly. The development process of AI models is energy-intensive, especially during the training phase of large models. This may not be considered waste in the traditional sense if the system eventually delivers value.
Bottom Line: There’s a Growing Need to Address AI Energy Consumption
It’s inarguable that AI energy consumption will increase as the technology advances. To ensure our environment and quality of life are not sacrificed at the expense of AI’s growth, implementing sustainable AI practices must become a priority. Only by placing sustainability at the forefront can we balance AI’s transformative potential with the need to protect our planet for future generations.
Read eWeek’s guide to AI for climate change to learn more about the evolving relationship between the environment and artificial intelligence.