Despite concerns over the environmental impacts of AI models, it's surprisingly hard to find precise, reliable data on the CO2 emissions and water use for many major large language models. French model-maker Mistral is seeking to fix that this week, releasing details from what it calls a first-of-its-kind environmental audit "to quantify the environmental impacts of our LLMs."
The results, which are broadly in line with estimates from previous scholarly work, suggest the environmental harm of any single AI query is relatively small compared to many other common Internet tasks. But with billions of AI prompts taxing GPUs every year, even those small individual impacts can lead to significant environmental effects in aggregate.
To generate a life-cycle analysis of its "Large 2" model after just under 18 months of existence, Mistral partnered with sustainability consultancy Carbone 4 and the French Agency for Ecological Transition. Following the French government's Frugal AI guidelines for measuring overall environmental impact, Mistral says its peer-reviewed study looked at three categories: greenhouse gas (i.e., CO2) emissions, water consumption, and materials consumption (i.e., "the depletion of non-renewable resources," mostly through wear and tear on AI server GPUs). Mistral's audit found that the vast majority of CO2 emissions and water consumption (85.5 percent and 91 percent, respectively) occurred during model training and inference, rather than from sources like data center construction and energy used by end-user equipment.
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