A French AI model maker has unveiled what it calls a pioneering environmental audit aimed at quantifying the real-world ecological costs of its large language models. The study presents a life-cycle view that isolates emissions, water use, and material depletion across three stages—training, inference, and ongoing usage—while highlighting how tiny, individual prompts accumulate into meaningful aggregate impacts when deployed at scale. The results align broadly with prior scholarly estimates, yet they also emphasize that the environmental footprint of AI grows with the volume of interactions, even if each prompt on its own appears modest.
Methodology and scope of the audit
Mistral, a French innovation in AI model development, released details from what it describes as a first-of-its-kind environmental audit designed to quantify the environmental impacts of its Large 2 family of large language models (LLMs). The study was conducted in collaboration with sustainability consultancy Carbone 4 and the French Agency for Ecological Transition, following guidelines known as Frugal AI. The aim was to deliver a structured, lifecycle-based assessment that captures the eco-costs associated with building, training, and operating an LLM over a defined period.
The audit centers on three primary environmental axes: greenhouse gas emissions, measured as CO2-equivalent, water consumption, and material consumption, focusing on the depletion of non-renewable resources driven by wear and tear on AI server GPUs and related hardware. The framework reflects an effort to standardize assessment across the industry by anchoring the analysis in well-established categories while remaining transparent about where the data come from and what assumptions were used. The approach emphasizes not only direct energy and emissions from data centers but also downstream implications such as infrastructure development and the broader energy system that supports AI workloads. The study is positioned as a practical, near-term estimate—an initial approximation—intended to guide further refinement and encourage broader transparency across the sector.
Crucially, the audit follows the spirit of the Frugal AI guidelines, which advocate measuring environmental impact across life-cycle stages rather than focusing solely on operational metrics. This broader lens helps capture the often overlooked costs tied to hardware production, maintenance, and eventual disposal. It also allows for comparisons across different stages of the model’s life and demonstrates where emission reductions can realistically be achieved. In addition, the audit acknowledges important gaps and limitations, including the need for more granular methodological detail and more comprehensive energy accounting that extends beyond the emissions reported to the total energy used by the model and its infrastructure.
Key findings on emissions and water usage
The audit reveals that the overwhelming share of CO2 emissions and water consumption from Mistral’s Large 2 model arise during the phases of training and inference, rather than during earlier steps such as data center construction or end-user device energy use. Specifically, roughly 85.5 percent of CO2 emissions and 91 percent of water use are attributed to training and inference activities. This leaves a smaller portion tied to the broader manufacturing footprint and other ancillary processes, underscoring the significance of how a model is trained and how it is subsequently queried.
When it comes to the marginal environmental impact of a single average prompt, the results are comparatively modest. Each prompt that generates about 400 tokens of text—a typical page of content—results in approximately 1.14 grams of CO2 and about 45 milliliters of water consumption. While this per-prompt figure may seem small, the aggregate effect becomes substantial when billions of prompts are processed over time. The audited period observed millions, potentially billions, of prompts, which compounds the per-query impact into a sizable overall burden.
The study translates the aggregate figures into tangible milestones. Over the first 18 months of operation, the combination of training and the millions of prompts processed produced around 20.4 kilotons of CO2 emissions. To put that into a familiar lens, the emissions are roughly equivalent to the annual operation of about 4,500 average internal-combustion-engine passenger vehicles, according to widely cited environmental metrics. On the water side, the model’s operation led to the evaporation of about 281,000 cubic meters of water, enough to fill approximately 112 Olympic-sized swimming pools. These comparisons help frame the scale while acknowledging that the environmental cost per unit output varies with the energy mix, hardware efficiency, and usage patterns.
The audit also contextualizes the marginal energy footprint of a single Mistral prompt against other common online activities. The incremental CO2 from a single typical prompt is equated to the emissions from watching about 10 seconds of a streaming show in the United States, or about 55 seconds in France, where the electricity grid tends to be cleaner. The same prompt’s footprint is framed as roughly equivalent to the CO2 of a Zoom call lasting anywhere from four to 27 seconds, depending on the underlying energy mix. In another comparative line, spending 10 minutes composing an email that reaches all 100 recipients can emit as much CO2 as 22.8 Mistral prompts, according to the study’s cited references. These comparisons are intended to help readers gauge the relative scale of AI emissions in everyday activities, while acknowledging the broader value associated with AI-enabled productivity and information access.
Context and comparison with other studies
Placed in a wider research landscape, Mistral’s numbers align with several independent efforts to quantify the environmental implications of AI workloads, yet differences in methodology make direct comparisons nuanced. A study by researchers at the University of California, Riverside, for instance, estimated that the average US data center supporting GPT-3 consumed about 17 milliliters of water per LLM prompt. Separately, a 2024 Nature-published study estimated an average emission of around 2.2 grams of CO2 per query for ChatGPT, taking into account both training and inference phases. These benchmarks illustrate a broad consensus that AI activities contribute non-trivial energy and water footprints, even if absolute numbers differ based on model size, data center efficiency, sampling strategies, and energy sources.
What sets Mistral’s release apart is the direct contribution of a major model developer who presents its own data and articulates a clear method for calculating life-cycle impacts. The company contends that its data represent a meaningful first approximation of the model’s total environmental footprint, with particular emphasis on the life-cycle impact of GPUs and related hardware. However, it also concedes that the methodology lacks certain crucial details and may not fully capture the model’s total energy consumption. In that sense, the audit serves as both a milestone in corporate transparency and a prompt for continued methodological refinement across the industry. The commentary from experts in AI and climate science reflects a cautious optimism: the work is a useful starting point and a blueprint for other organizations to follow, even as it underscores the need for standardized, comprehensive reporting.
Sasha Luccioni, an AI and climate lead at a prominent AI organization, notes that the information provided by Mistral lacks some of the deeper methodological specifics and a complete accounting of total energy use beyond the reported emissions. Despite these gaps, Luccioni regards the report as a commendable initial step in environmental impact assessment for AI models. She emphasizes that broader transparency—especially around the energy flows and the full energy budget—would enable more precise comparisons and a more robust framework for evaluating environmental performance across different model families. The overall takeaway is that while the audit does not resolve all questions, it signals industry momentum toward transparent, comparable metrics and invites other developers to share similar data sets.
In parallel, Mistral advocates for a broader, comparative approach, suggesting that the availability of transparent, standardized environmental data could enable the creation of a scoring system. Such a framework would help buyers and users evaluate models not only on capabilities and accuracy but also on their carbon, water, and material intensities. The underlying premise is that better information will drive demand toward models with lower environmental footprints, thereby encouraging improvements across the ecosystem. This position reflects a growing consensus that environmental accountability should be embedded in AI procurement and development decisions, influencing the design choices and optimization strategies built into future models.
Implications for policy, procurement, and industry practice
The release carries meaningful implications for policymakers, enterprise buyers, and model developers. By detailing life-cycle emissions, water use, and material depletion, Mistral’s audit provides concrete data points that can inform policy discussions around the environmental standards for AI. Regulators and industry groups can use such findings to propose benchmarks, reporting requirements, and best practices for energy efficiency in data centers, hardware procurement, and software optimization. The audit’s framing around Frugal AI guidelines further demonstrates a practical approach to regulatory compliance: measuring, reporting, and improving environmental performance without sacrificing innovation or access to AI capabilities.
From a procurement perspective, the availability of comparable environmental data could influence decisions in corporate and research settings. Buyers may begin to favor models with transparent, auditable footprints, incorporating environmental criteria into vendor selection, contract terms, and lifecycle management plans. A standardized scoring system, as proposed by Mistral, could become a decisive factor alongside cost, accuracy, latency, and feature sets. For model developers, the audit underscores the value of early and ongoing environmental accounting, from model architecture choices and hardware utilization to data center efficiency and energy sourcing. The incentive is clear: design decisions that reduce the life-cycle footprint can become a differentiator in a competitive market where performance and cost are balanced with sustainability.
Another implication concerns the broader energy ecosystem. The audit highlights that the lion’s share of environmental impact arises from how models are trained and how often they are invoked. This emphasizes the importance of cleaner energy mixes, more efficient GPUs and data-center cooling, and smarter load management. It also spotlights the potential benefits of optimization strategies such as model distillation, parameter sharing, and more efficient inference techniques that can reduce energy intensity without compromising accuracy or usefulness. Policymakers and industry stakeholders may push for accelerated deployment of renewable energy in data centers and more aggressive efficiency standards for AI hardware and software stacks.
Limitations, critiques, and paths forward
No single study can capture the full complexity of AI’s environmental impact, and Mistral’s audit is explicit about its boundaries. The report characterizes its data as a first approximation, acknowledging that some methodological details are lacking and that total energy use remains partially unreported. Critics argue that without a complete accounting of all energy inputs, including those tied to the end-to-end energy consumption of infrastructure and facilities beyond the immediate model, the footprint could be underestimated or not fully contextualized. The lack of comprehensive energy accounting may complicate cross-model comparisons and may risk underrepresenting certain environmental costs.
The audit also recognizes that the results are sensitive to assumptions about energy sources, hardware efficiency, and usage patterns. For example, the carbon intensity of a given prompt depends on the electricity mix feeding the data center and the efficiency of GPUs and related cooling systems. Regional variations can therefore lead to substantially different footprints for similar workloads. Critics suggest that to enable more precise comparisons, future studies should publish detailed methodologies, data sources, and model configurations in a standardized, auditable format. Such transparency would help build trust and improve the reliability of environmental assessments across the AI industry.
From a research perspective, the audit invites further exploration of how to measure the energy and material costs of AI in a scalable, reproducible way. It also raises questions about whether current metrics adequately capture the social and ecological value generated by AI tools. Stakeholders may debate the difficulty of assigning an environmental price tag to outputs like enhanced productivity, access to information, and new capabilities that AI enables. In this light, the audit becomes part of a broader conversation about balancing environmental costs with the societal benefits of AI innovation.
Industry response, transparency, and future directions
Mistral’s release has sparked conversations about transparency as a core obligation for AI developers. The company emphasizes its willingness to publicly share environmental data and its ambition to set a positive example for the sector. By documenting the life-cycle costs and offering a pathway toward a standardized scoring approach, Mistral signals a shift from bespoke, company-only metrics toward more comparable benchmarks that can inform procurement and policy decisions. This stance is reinforced by calls from industry observers for other model makers to publish similar data, enabling industry-wide comparisons and driving improvements in environmental performance.
The audit has also highlighted the need for deeper methodological disclosures. While the presented figures are informative, their usefulness depends on the extent to which researchers and practitioners can audit the underlying assumptions, data sources, and calculation methods. The partnership with Carbone 4 and the French Agency for Ecological Transition adds credibility, but the broader community is likely to circulate requests for even more granular documentation. The goal is not only to reveal numbers but also to reveal the story behind those numbers: what drives them, where uncertainties lie, and how stakeholders can meaningfully reduce them.
Looking ahead, stakeholders anticipate a growing appetite for standardized reporting frameworks, open data practices, and collaborative efforts to benchmark AI environmental performance. If the industry converges on transparent reporting and shared methodologies, buyers can make more informed decisions, researchers can compare results with greater confidence, and policymakers can craft more precise guidance. The long-term trajectory could include the establishment of an auditable, comparable scoring ecosystem that weights energy efficiency, water stewardship, and material management alongside model capability and performance. Such a framework would harness market incentives to push the ecosystem toward lower environmental footprints without compromising innovation.
Technical breakdown: where the costs come from
The life-cycle analysis differentiates between training and inference as two distinct phases with amplifying effects on the overall environmental footprint. Training a large model involves substantial energy consumption over extended periods, driven by the computational demands of learning from vast datasets. Inference—the process of generating outputs for user prompts—also demands continuous GPU utilization, especially when models are deployed at scale and accessed by many users concurrently. The audit highlights that these two phases together constitute the bulk of emissions and water use, overshadowing other components such as the construction of data centers or the energy used by end-user devices.
Materials consumption is another critical dimension, tied largely to the wear and tear on GPUs, memory modules, and other essential hardware. The depletion of non-renewable resources occurs as servers are manufactured, upgraded, and eventually decommissioned. The audit frames these resource costs within the broader opportunity for hardware optimization and recycling strategies, as well as the potential for longer hardware lifespans and more efficient life-cycle management practices. The net effect is a reminder that environmental costs are not solely about energy; they also involve material resource stewardship and the circular economy implications of AI infrastructure.
The marginal per-prompt footprint draws attention to the efficiency of inference software and hardware, but it also depends on the data center efficiency and energy sources. A clean energy mix and advanced cooling technologies can dramatically lower the carbon intensity of even large-scale inference tasks. In turn, this underscores the potential of energy policy, grid decarbonization, and on-site renewables as levers to reduce AI’s environmental impact. The study’s per-prompt measurements—1.14 grams of CO2 and 45 milliliters of water for 400 tokens—serve as a useful baseline for contrast with other tasks and models, yet they must be interpreted alongside the total energy budget for an accurate picture of total environmental costs.
Societal and behavioral context: values, perceptions, and trade-offs
Beyond the raw numbers, the audit invites readers to reflect on the social and behavioral dimensions of AI’s environmental footprint. The reported comparisons show that individual activities perceived as relatively harmless in daily life—like a short streaming clip, a quick Zoom call, or writing an email—carry comparable or greater carbon footprints when scaled across large user bases and repeated over time. This juxtaposition challenges common narratives that frame AI as uniquely unsustainable, instead highlighting that many online activities share environmental costs that accumulate rapidly with scale. It also raises questions about how society values the outputs and benefits created by AI relative to their ecological costs.
There is a tension between social taboos, personal guilt, and the practical reality of online activity. The audit suggests that the fear of AI energy use destroying the planet may not align with the measured environmental footprints per unit of output, especially when energy grids become cleaner and hardware efficiency improves. These nuanced insights can help policymakers, educators, and industry leaders communicate about AI in a balanced way, avoiding fear-based narratives while maintaining accountability for environmental impacts. The broader lesson is to approach AI’s footprint with a data-driven mindset that accounts for scale, energy sources, and the societal value delivered by advanced models.
Call to action: transparency, standardization, and responsible innovation
A central takeaway from the audit is a clear invitation to the AI industry to embrace transparency as a core operational practice. Mistral argues that making environmental data publicly available can enable the creation of a standardized scoring system, helping buyers and users identify models with lower carbon, water, and material intensities. The proposed scoring framework would complement traditional performance metrics, adding an environmental dimension to model selection and procurement decisions. In this vision, environmental reporting becomes a competitive differentiator and a driver of responsible innovation.
The release also serves as a practical prompt for more extensive data sharing. While acknowledging its limitations, Mistral’s openness invites other model developers to publish their own environmental data, enabling cross-comparisons and the refinement of methodology. The overarching goal is to build a robust, transparent knowledge base that enables stakeholders to make informed choices, accelerate improvements, and set industry-wide benchmarks. The industry’s move toward standardized reporting would also facilitate policy engagement, helping regulators craft clearer guidelines, disclosures, and incentives for sustainable AI development.
In the longer horizon, the audit points toward continued collaboration among researchers, industry players, and policymakers to develop standardized, auditable methodologies. The vision is an ecosystem in which environmental accounting is integrated into the fabric of AI development—from model design and data center operations to supply chain management and end-of-life disposal. Such an approach would promote continuous improvement, encourage investment in greener infrastructure, and align AI growth with broader climate objectives.
Conclusion
Mistral’s environmental audit presents a comprehensive, life-cycle view of the ecological costs associated with its Large 2 LLMs. The findings show that the bulk of emissions and water use stem from training and inference, with a per-prompt footprint that remains small on its own but grows significantly when multiplied across billions of interactions. The results align with, and in some respects extend, earlier independent studies by illustrating how scale translates into aggregate impact. The audit also underscores the need for greater methodological transparency and standardized reporting to enable meaningful cross-model comparisons and industry-wide improvements.
Beyond the numbers, the release invites a broader conversation about how AI can be developed and deployed responsibly. It advocates for openness, the development of scoring systems, and the establishment of shared benchmarks that balance environmental stewardship with the societal benefits AI provides. The path forward involves enhanced energy efficiency, cleaner power sources, smarter hardware utilization, and more robust data-sharing practices. As the industry moves toward greater transparency and standardized measurement, stakeholders can better navigate the trade-offs between AI innovation and ecological responsibility, ensuring that progress in artificial intelligence proceeds in harmony with climate goals.