Artificial Intelligence, Real Consequences: Confronting AI’s Growing Energy Appetite

I’m an avid user (and paying customer) of OpenAI’s GPT-4o. It helps me with all kinds of tasks, from coding, research, blog writing, to visualizing how our kitchen could look like if we painted the walls in a different color. When GPT-4o was released, its speed was mind-blowing – it was outputting content much faster than I could read. Faster output means higher work efficiency! But as always, there was a catch: I live in Europe, and as soon as 2 PM comes around, that’s when the US East Coast starts working, the output speed slows down to what feels like 10% of the morning hours. There goes the dream of the 10x more productive software engineer. I need OpenAI to add more GPUs to their data centers! It’s funny how quickly we get used to new tools and just as quickly change our expectations to match.

It’s easy to get caught up in the excitement of new technology and its productivity gains. However, those GPUs powering the lightning-fast outputs don’t run on hopes and dreams. They require massive amounts of electricity, contributing to an ever-growing carbon footprint. As AI models become more complex and widely adopted, the energy demands continue to skyrocket.

In a stunning display of ambition, Elon Musk’s xAI aims to evolve the AI landscape by constructing an unprecedented “gigafactory of compute” by Fall 2025, harnessing the power of a staggering 100,000 Nvidia H100 GPUs. In comparison, their current model, Grok 2, required around 20,000 GPUs for training. This ambitious project highlights the critical need to address the substantial energy consumption associated with such technological advancements.

Artificial intelligence, particularly generative AI and large language models (LLMs), presents a dual impact on our world. While AI drives unprecedented progress across various sectors, its energy-intensive nature raises significant environmental concerns, contributing to higher carbon emissions and straining global energy resources. As we push the boundaries of AI, we must have an honest conversation about its energy consumption and explore innovative solutions to mitigate its environmental footprint.

In this article, we will explore the challenges posed by the increasing energy demands of AI technologies, analyze key factors influencing this consumption, and highlight strategies to mitigate their environmental impact up to 2030. Our goal is to propose actionable solutions that balance the growth of AI with the imperative of sustainability.

The energy-intensive nature of AI

This chart was presented by Ben Fowke, the interim CEO of American Electric Power Co. (AEP), as part of his prepared testimony for a hearing held by the Senate Energy and Natural Resources Committee on May 21, 2024. AI’s energy demand is expected to skyrocket from just eight terawatt-hours in 2024 to a staggering 652 terawatt-hours by 2030, representing an over 80-fold increase in electricity consumption by AI in just six years.

To put this 652 TWh figure into perspective, it is equivalent to nearly 3.5% of total global electricity demand today. The rapid growth illustrated in the chart highlights the critical need to make AI systems much more energy-efficient in the coming years to avoid AI becoming a significant driver of rising electricity demand and associated carbon emissions.

GPT-4, the LLM behind ChatGPT, boasts an impressive 1.7 trillion machine learning parameters. Training this model required a staggering 25,000 Nvidia A100 GPUs over a period of 90-100 days, costing around $100 million. Although they have not disclosed exact energy usage figures, estimates suggest that GPT-4 consumed between 51,773 MWh and 62,319 MWh of energy — over 40 times the consumption of its predecessor, GPT-3. To put this into perspective, this amount of energy is comparable to the consumption of 1,000 average US households over a span of 5 to 6 years.

Key factors influencing AI’s energy consumption

The efficiency of hardware, particularly GPUs, plays a crucial role in AI’s energy consumption. These high-performance units require significant power to operate. Innovations in GPU design, such as improved energy efficiency and advanced cooling techniques, are essential to reducing the overall energy footprint of AI systems. Additionally, integrating specialized AI hardware, such as tensor processing units (TPUs) and application-specific integrated circuits (ASICs), can further enhance computational efficiency, lowering energy consumption.

The scale and complexity of AI models directly impact their energy consumption. As models grow, with more parameters and larger training datasets, the computational resources required to train and run them increase exponentially. Complex models demand extensive data processing and longer training times, both of which contribute to higher energy consumption. The enormous energy requirement of these brute-force statistical models is due to several attributes:

1. Extensive Training Examples: AI models require millions or billions of training examples to learn effectively. For instance, in a model designed to understand and generate text about cats, sentences are needed that describe cats from different perspectives, contexts, breeds, colors, and environments. The model must be trained on a vast amount of information related to millions of different concepts involving cats.
2. Iterative Training Cycles: Training an AI model involves learning from errors. If the model incorrectly predicts an outcome, it readjusts its parameters, refines its classification, and retrains. This iterative process of learning from mistakes requires many iterations through the entire dataset, consuming significant energy.
3. Retraining for New Information: When presented with new information, such as learning about dogs after being trained on cats, the model can improvise using the context provided in the prompt. However, this may lead to inaccuracies, and the model won’t retain this new information for future use. To effectively learn about both cats and dogs, the model needs to be retrained from scratch with the combined dataset, further increasing energy consumption.
4. Computational Complexity: AI models rely on neural networks with numerous connections, or weights, represented by matrices. Computing an output requires millions of matrix multiplications through subsequent layers until a pattern emerges. A typical network might contain dozens to hundreds of layers, making the computations incredibly energy-intensive.

As AI models become more sophisticated, finding ways to manage and optimize their scale and complexity becomes increasingly important to mitigate their environmental impact. While the energy demands of AI present significant challenges, the tech industry isn’t standing still. Innovators and researchers are actively working on solutions to make AI more sustainable. In the next part of this blog, we’ll explore these cutting-edge approaches and their potential to reshape the future of AI.