The Energy Cost of AI/ML, Iceland to the Rescue

Wednesday May 01, 2024

In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), the computational demands for training sophisticated models have surged, paralleling the exponential growth in model complexity. This escalation signifies advancement in AI capabilities and highlights the burgeoning challenge of substantial energy consumption and its ensuing environmental impact. Amidst growing concerns over climate change and sustainability, the industry must reconcile technological innovation with ecological stewardship.

This paper explores the energy demands of AI and ML models, emphasizing the significant variance in energy requirements across different model types. This paper delineates the scale of energy use and its implications from modest models that run efficiently on standard laptops to the colossal energy appetite of the GPT family and other large language models (LLMs). Further, it investigates the paradigm of training versus operation energy costs, underscoring the environmental challenges posed by the one-time, intensive energy use of model training.

Highlighting the intersection of technology and sustainability, the paper presents a case study around Iceland. With its abundant renewable energy resources, Iceland is a beacon of sustainability, offering a pragmatic solution to the industry’s energy dilemma. By tapping into Iceland’s utilization of geothermal and hydroelectric power for AI/ML data centers, this discussion not only showcases the potential for renewable energy in mitigating the carbon footprint of AI development but also sets the stage for a broader conversation on global sustainability practices.

Energy Demand of AI and ML Models

AI and ML models demand considerable computational power for training, a requirement that has grown exponentially with the introduction of LLMs. The energy needs vary significantly across different types of AI/ML models. For instance, training a modest model with fewer than 1,000 parameters on a standard laptop within a single charge cycle is feasible, reflecting an energy consumption at most typical high-load computer use. However, the energy requirements escalate dramatically for larger models.

For example, models belonging to the GPT family are more demanding. GPT-2 alone incorporates 1.7 billion parameters, necessitating far greater computational power and energy consumption. The scale of model complexity and energy demand has continued to expand exponentially.

GPT-4, purported to contain over 1 trillion parameters, exemplifies this trend with its training costs surpassing 100,000,000 USD. Though specifics are sparse, such training was likely conducted in a cloud computing environment. This approach minimizes maintenance costs and, theoretically, reduces the compute time cost to nearly that of the energy price. Under this assumption, the energy required to train GPT-4 approximates the monthly energy consumption of a major city like Houston, illustrating the profound energy implications of training cutting-edge AI/ML models.

However, we must understand the difference between training and operations. Training should be a one-time event, and while operations take advantage of that result over a long period, operations do not require the same kind of computing resources. Many models can operate in size, weight, and power (SWaP)-constrained environments, and even an LLM like GPT-2 can be run on a consumer-grade laptop. From an accounting perspective, we consider the training effort capitalizable and amortizable over the model’s life. This does not work from an environmental perspective as the one-time cost of training leads to a sizeable past release of carbon through energy consumption. That is not amortizable in the same way.

Furthermore, the comparative energy consumption between AI/ML models and other computational tasks reveals a staggering disparity. For example, the training of a single large-scale AI model can consume as much energy as several thousand homes do in a year. This discrepancy highlights the need for more energy-efficient computing techniques in the field of AI.

The energy sources powering these computational feats also play a critical role in the overall carbon footprint. Data centers relying on non-renewable energy sources significantly exacerbate the environmental impact of training large AI models. Conversely, the shift towards renewable energy sources in powering these data centers presents a viable path towards reducing the carbon footprint associated with AI development.

Over time, notable efforts have been made to improve the energy efficiency of AI/ML training processes. Innovations in algorithm design, hardware optimization, and software frameworks have contributed to reductions in energy consumption per model parameter. However, the pace of model size expansion often needs to improve on these efficiency gains, posing ongoing challenges to sustainability efforts.

The global impact of AI’s energy demand calls for concerted policy action. Different regions may face unique challenges regarding their energy infrastructure’s capacity to support sustainable AI development. Policies that incentivize the use of renewable energy sources in data centers, alongside regulations that encourage energy-efficient model design, could play pivotal roles in mitigating the environmental impact of AI.

Lastly, the advent of cloud computing has introduced challenges and opportunities in the context of AI’s energy consumption. While cloud environments offer the potential for more efficient resource utilization and access to renewable energy sources, they also centralize the energy demand of AI/ML training, necessitating careful consideration of their environmental impact. Cloud providers are increasingly aware of this responsibility, with many committing to sustainability goals to reduce their operations’ carbon footprint.

While the energy demand of AI/ML models presents significant environmental challenges, a combination of technological innovation, policy intervention, and the responsible use of cloud computing resources can contribute to more sustainable practices in AI development.

In From the Cold, a Solution in Iceland

Thanks to its robust geothermal and hydroelectric power infrastructure, Iceland is an exemplary model for powering data centers with renewable energy. This island nation harnesses its volcanic activity to produce geothermal energy. It capitalizes on its river systems for hydroelectric power, providing a stable and sustainable energy supply that is 85 percent renewable. These attributes make Iceland an attractive location for data centers, particularly for operations requiring intensive computational resources, like the training of AI/ML models.

Integrating renewable energy into high-demand computing environments, such as those needed for AI/ML training, involves several technical and infrastructural considerations. In Iceland, the challenge of round-trip time (RTT) to other global networks is mitigated because AI/ML model training is localized. Training nodes need to communicate with each other efficiently, a requirement that can be satisfied within Iceland’s data centers. The deployment of trained models, which requires less intensive computational resources, can be strategically located closer to end-users worldwide, optimizing performance without compromising sustainability.

Although less numerous than in larger countries, Iceland’s data centers, such as those operated by Verne Global and Advania, are prime examples of leveraging renewable energy for computing operations. These facilities specialize in high-performance computing tasks, including AI/ML model training, and are powered entirely by Iceland’s renewable energy resources.

Verne Global, for instance, operates a data center campus in Iceland that is powered by geothermal and hydroelectric power. This setup provides a sustainable and cost-effective solution for energy-intensive computations. The company has attracted clients from various sectors, including AI/ML research institutions, looking to reduce their carbon footprint while accessing the computational power necessary for advanced model training.

A secondary advantage to locating in Iceland is the generally cooler climate, which requires less active cooling of heat-generating data centers. Advania Data Centers offer another example, with facilities that utilize Iceland’s renewable energy and take advantage of the country’s cool climate for natural cooling, further reducing the energy needed for temperature management. Advania is taking this further with locating new data centers coming to cold-weather climates in Finland.

Beyond the direct benefits of utilizing renewable energy, Iceland’s approach offers broader lessons for the industry. In addition to providing cheap renewable energy, Iceland also provides a well-educated populace that is well-versed in English. Further, Iceland has a well-developed economy with a highly computer-literate population. Therefore, while there may be some physical barriers to making Iceland an AI/ML training hub, the social barriers to entry are lowered.


The energy landscape of AI and ML models reveals a critical crossroads for the industry. As models grow in complexity and capability, so does their energy consumption, posing new environmental challenges. However, exploring renewable energy-powered data centers in Iceland offers a glimpse into a sustainable path forward. Iceland’s success story, characterized by its innovative use of geothermal and hydroelectric power, provides a blueprint for harnessing renewable energy sources in the computationally intensive AI/ML model training realm.

This case study highlights the feasibility of reducing the carbon footprint associated with AI development and underscores the importance of location and infrastructure in achieving sustainability goals. The Icelandic model demonstrates that with thoughtful integration of renewable energy sources and efficient data center design, the industry can mitigate its environmental impact without compromising on the computational power necessary for AI/ML advancements.

As the world grapples with the dual challenges of technological progress and environmental sustainability, the lessons from Iceland serve as a beacon. They remind us that innovation and ecological responsibility can coalesce, guiding the tech industry toward a more sustainable future. This paper’s insights into the energy demands of AI/ML models and the potential of renewable energy solutions underscore a pivotal message that sustainable AI development is not just a possibility but a necessity, urging the industry to embrace renewable energy as the cornerstone of future AI/ML efforts.