The Carbon Footprint of Generative AI Tasks

Saturday - December 2 2023, 17:37 UTC - 11 months ago

Researchers at the AI startup Hugging Face and Carnegie Mellon University have found that generating images with an AI model takes as much energy as fully charging your smartphone, while generating text 1,000 times only uses as much energy as 16% of a full smartphone charge. The least carbon-intensive text generation model was found to generate as much CO2 as driving 0.0006 miles in a similar vehicle. The study provides useful insights into AI’s carbon footprint by offering concrete numbers and technology companies are now using generative-AI models billions of times every single day.

Each time you use AI to generate an image, write an email, or ask a chatbot a question, it comes at a cost to the planet. In fact, generating an image using a powerful AI model takes as much energy as fully charging your smartphone, according to a new study by researchers at the AI startup Hugging Face and Carnegie Mellon University. However, they found that using an AI model to generate text is significantly less energy-intensive. Creating text 1,000 times only uses as much energy as 16% of a full smartphone charge.

Their work, which is yet to be peer reviewed, shows that while training massive AI models is incredibly energy intensive, it’s only one part of the puzzle. Most of their carbon footprint comes from their actual use.

The study is the first time researchers have calculated the carbon emissions caused by using an AI model for different tasks, says Sasha Luccioni, an AI researcher at Hugging Face who led the work. She hopes understanding these emissions could help us make informed decisions about how to use AI in a more planet-friendly way.

Luccioni and her team looked at the emissions associated with 10 popular AI tasks on the Hugging Face platform, such as question answering, text generation, image classification, captioning, and image generation. They ran the experiments on 88 different models. For each of the tasks, such as text generation, Luccioni ran 1,000 prompts, and measured the energy used with a tool she developed called Code Carbon. Code Carbon makes these calculations by looking at the energy the computer consumes while running the model. The team also calculated the emissions generated by doing these tasks using eight generative models, which were trained to do different tasks.

Generating images was by far the most energy- and carbon-intensive AI-based task. Generating 1,000 images with a powerful AI model, such as Stable Diffusion XL, is responsible for roughly as much carbon dioxide as driving the equivalent of 4.1 miles in an average gasoline-powered car. In contrast, the least carbon-intensive text generation model they examined was responsible for as much CO2 as driving 0.0006 miles in a similar vehicle. Stability AI, the company behind Stable Diffusion XL, did not respond to a request for comment.

The study provides useful insights into AI’s carbon footprint by offering concrete numbers and reveals some worrying upward trends, says Lynn Kaack, an assistant professor of computer science and public policy at the Hertie School in Germany, where she leads work on AI and climate change. She was not involved in the research.

These emissions add up quickly. The generative-AI boom has led big tech companies tointegrate powerful AI models into many different products, from email to word processing. These generative AI models are now used millions if not billions of times every single day.

The team found that using large generative models to create outputs was far more energy intensive than using smaller AI models tailored for specific tasks. For example, using a generative model to classify movie reviews according to whether they are positive or negative consumes around 30 times mote energy than using a model with the same task, it’s just been tailored for that use.