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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.

Is AI really destroying the planet?

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.

Through its audit, Mistral found that the marginal "inference time" environmental impact of a single average prompt (generating 400 tokens' worth of text, or about a page's worth) was relatively minimal: just 1.14 grams of CO₂ emitted and 45 milliliters of water consumed. Through its first 18 months of operation, though, the combination of model training and running millions (if not billions) of those prompts led to a significant aggregate impact: 20.4 ktons of CO₂ emissions (comparable to 4,500 average internal combustion-engine passenger vehicles operating for a year, according to the Environmental Protection Agency) and the evaporation of 281,000 cubic meters of water (enough to fill about 112 Olympic-sized swimming pools).

The marginal impact of a single Mistral LLM query compared to some other common activities.
Credit: Mistral

Comparing Mistral's environmental impact numbers to those of other common Internet tasks helps put the AI's environmental impact in context. Mistral points out, for instance, that the incremental CO₂ emissions from one of its average LLM queries are equivalent to those of watching 10 seconds of a streaming show in the US (or 55 seconds of the same show in France, where the energy grid is notably cleaner). It's also equivalent to sitting on a Zoom call for anywhere from four to 27 seconds, according to numbers from the Mozilla Foundation. And spending 10 minutes writing an email that's read fully by one of its 100 recipients emits as much CO₂ as 22.8 Mistral prompts, according to numbers from Carbon Literacy.

Directly comparing the social and environmental "value" of all of these activities isn't easy and depends heavily on how much value you place on the output of AI tools in general. However, the level of social taboo, personal guilt, and overall online griping associated with these different tasks might not align with their similar environmental footprints. That's worth keeping in mind the next time you hear someone warn that AI energy use in particular is destroying the planet.

A call for more data

Mistral's numbers are broadly comparable to other studies that have sought to estimate AI's environmental impact. A study from researchers at the University of California, Riverside, for instance, estimated the average US AI data center used for OpenAI's GPT-3 consumed nearly 17 ml of water per LLM prompt. And a 2024 study published in the journal Nature estimated an average of 2.2g of CO₂ emissions per query for ChatGPT (across training and inference time).

Compared to those previous third-party estimates, the fact that Mistral provided information directly for this latest study definitely lends some additional weight to its reported numbers. Still, Mistral writes that its data represents "a first approximation" of the model's total environmental impact, with important estimates used for the life-cycle impact of GPUs, for instance. Hugging Face AI & Climate Lead Sasha Luccioni also notes that the information Mistral has released lacks important methodological details and information on the model's total energy use (rather than the estimated emissions from that energy use).

Still, Luccioni calls the report "a great first step in terms of environmental impact assessment of AI models," which she hopes other AI companies will be inspired to emulate. Mistral is also urging other model makers to be more transparent about their environmental impact, saying that such comparative results "could enable the creation of a scoring system, helping buyers and users identify the least carbon-, water- and material-intensive models."