Researchers quantify the carbon footprint of generating AI images

Creating a photograph using artificial intelligence is like charging your phone.


Researchers at the AI startup Hugging Face collaborated with Carnegie Mellon University and discovered that generating an image using artificial intelligence, whether it's to create stock images or realistic ID photos, has a carbon footprint equivalent to charging a smartphone. However, researchers discern that generating text, whether it be to create a conversation with a chatbot or clean up an essay, requires much less energy than generating photos. The researchers quantify that AI-generated text takes up as much energy as charging a smartphone to only 16 percent of a full charge.

The study didn't just look into image and text generation by machine learning programs. The researchers examined a total of 13 tasks, ranging from summarization to text classification, and measured the amount of carbon dioxide produced per every 1000 grams. For the sake of keeping the study fair and the datasets diverse, the researchers said they ran the experiments on 88 different models using 30 datasets. For each task, the researchers ran 1,000 prompts while gathering the “carbon code” to measure both the energy consumed and the carbon emitted during an exchange.

Graph from study
Hugging Face/Carnegie Mellon

The findings highlight that the most energy-intensive tasks are those that ask an AI model to generate new content, whether it be text generation, summarization, image captioning, or image generation. Image generation ranked highest in the amount of emissions it produced and text classification was classified as the least energy-intensive task.

The researchers urge machine learning scientists and practitioners to “practice transparency regarding the nature and impacts of their models, to enable better understanding of their environmental impacts.” While the energy consumption associated with charging a smartphone per AI image generated may not seem dire, the volume of emissions can easily stack up when considering how popular and public AI models have become. Take ChatGPT for instance – the authors of the study point out that at its peak, OpenAI’s chatbot had upward of 10 million users per day and 100 million monthly active users today.