We're getting a better idea of AI's true carbon footprint

To test its new approach, Hugging Face estimated the overall emissions for its own large language model, BLOOM, which was launched earlier this year. It was a process that involved adding up lots of different numbers: the amount of energy used to train the model on a supercomputer, the energy needed to manufacture the supercomputer's hardware and maintain its computing infrastructure, and the energy used to run BLOOM once it had been deployed. The researchers calculated that final part using a software tool called CodeCarbon, which tracked the carbon emissions BLOOM was producing in real time over a period of 18 days. Hugging Face estimated that BLOOM's training led to 25 metric tons of carbon emissions. But, the researchers found, that figure doubled when they took into account the emissions produced by the manufacturing of the computer equipment used for training, the broader computing infrastructure, and the energy required to actually run BLOOM once it was trained. While that may seem like a lot for one model--50 metric tons of carbon emissions is the equivalent of around 60 flights between London and New York--it's significantly less than the emissions associated with other LLMs of the same size.