Life Cycle Assessment of Mistral Large 2: A Report on Environmental Impacts and Alignment with Sustainable Development Goals

Introduction and Executive Summary

Mistral AI has released a comprehensive Life Cycle Assessment (LCA) for its large language model, Mistral Large 2, establishing a benchmark for environmental transparency in the artificial intelligence sector. The report quantifies the model’s environmental costs across its training and an 18-month operational period. The findings highlight significant consumption of energy, water, and material resources, directly impacting several of the United Nations’ Sustainable Development Goals (SDGs). This analysis underscores the critical need for standardized reporting and sustainable practices in AI development to mitigate adverse environmental effects.

Analysis of Environmental Footprint

Greenhouse Gas Emissions and Climate Action (SDG 7, SDG 13)

The assessment reveals that the model’s lifecycle generated a total of 20.4 kilotons of CO₂ equivalents. This substantial carbon footprint presents a direct challenge to SDG 13: Climate Action. A detailed breakdown indicates that the initial training phase is the most energy-intensive activity, responsible for 85.5% of total greenhouse gas emissions. This heavy reliance on computational power, often sourced from carbon-intensive grids, emphasizes the urgency for the AI industry to transition towards SDG 7: Affordable and Clean Energy by powering data centers with renewable sources.

Water Consumption and Resource Management (SDG 6)

The model’s lifecycle consumed 281,000 cubic meters of water, with the training phase alone accounting for 91% of this total. This level of water usage has significant implications for SDG 6: Clean Water and Sanitation, particularly as data centers are frequently located in regions already experiencing water stress. On a granular level, a single 400-token query consumes an estimated 45 milliliters of water. This figure stands in contrast to unverified claims from competitors, highlighting the need for transparent, standardized reporting to accurately assess the industry’s collective impact on global water resources.

Material Consumption and Responsible Production (SDG 12)

The report quantifies the consumption of abiotic resources using a metric of 660 kilograms of antimony equivalents. This figure represents the rare metals and minerals required for the production of essential hardware, such as GPUs. This aspect of the AI lifecycle directly relates to SDG 12: Responsible Consumption and Production. It calls attention to the challenges of sustainable sourcing, supply chain ethics, and the management of electronic waste, urging the industry to adopt circular economy principles for its physical infrastructure.

Implications for Sustainable AI Development

The Principle of Proportionality: Model Size and Impact

A key finding of the study is the direct correlation between a model’s size and its environmental footprint. The analysis indicates that a tenfold increase in model size results in a corresponding order-of-magnitude increase in environmental cost. This underscores the importance of right-sizing models for specific applications, a practice that aligns with the efficiency and waste-reduction principles of SDG 12.

Proposed Metrics for Industry-Wide Reporting

To foster accountability and enable sustainable choices, Mistral proposes an industry-wide reporting standard based on three key metrics. Adopting these standards would advance SDG 9: Industry, Innovation, and Infrastructure by promoting sustainable industrial practices and supporting SDG 12 by providing consumers with transparent environmental data.

1. Total Impact of Model Training: A mandatory disclosure of the full environmental cost associated with training a model.
2. Per-Inference Impact: A mandatory disclosure of the environmental cost per user request, allowing for direct comparison between models.
3. Ratio of Inference to Overall Life Cycle Impact: An internal or optional metric to provide a complete view of a model’s operational efficiency over time.

Recommendations and Future Outlook

Pathways to a Greener AI Ecosystem

The report outlines a dual approach to reducing AI’s environmental footprint, reinforcing the collaborative nature of SDG 17: Partnerships for the Goals.

• Corporate Responsibility: AI companies must commit to publishing their models’ environmental impacts using internationally recognized standards, enabling users to select more sustainable options.
• User and Institutional Action: Users can contribute by adopting efficient practices, such as bundling requests and choosing the smallest effective model for their needs. Public institutions can leverage their procurement power to drive the market toward greater efficiency, in line with SDG 12.

Commitment to Standardization and Regulatory Context

Mistral acknowledges that this initial analysis is an estimate, limited by the absence of established LLM assessment standards and reliable LCA data for GPUs. The company has committed to refining its reports and contributing to the development of international standards, including publishing its findings in the French Base Empreinte database. This initiative aligns with the EU’s AI Act, which mandates detailed documentation of energy consumption for general-purpose AI models. This regulatory oversight, which links energy use to the classification of “systemic risk,” demonstrates a policy-driven effort to align the AI industry with global sustainability targets, particularly SDGs 7, 12, and 13.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 6: Clean Water and Sanitation – The article explicitly mentions the significant water consumption associated with training and operating large language models.
• SDG 7: Affordable and Clean Energy – The text highlights the energy consumption of AI models, particularly the use of GPU clusters in regions with “carbon-heavy electricity,” and references the EU AI Act’s requirement to document energy usage.
• SDG 9: Industry, Innovation, and Infrastructure – The article discusses the need for new industry standards for environmental reporting, the development of more efficient AI technology, and the role of public procurement in promoting greener infrastructure.
• SDG 12: Responsible Consumption and Production – The core of the article is a life cycle assessment, which is a key tool for promoting sustainable production. It discusses resource consumption (CO₂, water, antimony) and encourages companies to report on their environmental impact and users to make more efficient choices.
• SDG 13: Climate Action – The article directly addresses climate change by quantifying the greenhouse gas emissions (CO₂ equivalents) of an AI model and discussing the overall environmental footprint of the AI industry.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 6: Clean Water and Sanitation

  • Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity. The article’s focus on quantifying water consumption (281,000 cubic meters for the model’s life cycle and 45 ml per request) and the mention of “water stress” in regions with data centers directly relate to improving water-use efficiency in the tech industry.

• SDG 7: Affordable and Clean Energy

  • Target 7.3: By 2030, double the global rate of improvement in energy efficiency. The article discusses the need to use AI more efficiently, choose the right-sized model for the task, and the EU AI Act’s requirement to document energy consumption, all of which are aimed at improving energy efficiency.

• SDG 9: Industry, Innovation, and Infrastructure

  • Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes. Mistral’s proposal for industry-wide reporting standards and the development of life cycle assessments for AI models are steps toward making the AI industry more sustainable and its processes more environmentally sound.

• SDG 12: Responsible Consumption and Production

  • Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources. The article details the consumption of water, energy (implied through CO₂), and rare metals (antimony), directly addressing the use of natural resources in AI production.
  • Target 12.6: Encourage companies, especially large and transnational companies, to adopt sustainable practices and to integrate sustainability information into their reporting cycle. Mistral’s publication of its life cycle assessment and its call for mandatory reporting are direct actions supporting this target.

• SDG 13: Climate Action

  • Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, impact reduction and early warning. The entire report by Mistral serves as an awareness-raising tool to educate the public, users, and policymakers about the climate impact of AI, thereby increasing institutional capacity to make informed, greener decisions.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• For Target 6.4 (Water-use efficiency):

  • Indicator: Total water consumption for model training and operation (281,000 cubic meters).
  • Indicator: Water consumption per inference/request (45 milliliters). These metrics directly measure water use and can be tracked to show changes in efficiency.

• For Target 7.3 (Energy efficiency):

  • Indicator: Detailed documentation of energy usage, as required by the EU AI Act. This serves as a basis for measuring energy intensity.
  • Indicator: The proposed “per-inference impact” metric, which includes energy consumption as a key component (measured via CO₂ equivalents).

• For Target 9.4 (Sustainable industries and technologies):

  • Indicator: CO₂ emissions from the industrial process (20.4 kilotons of CO₂ equivalents for training and operation). This is a direct measure of the environmental soundness of the technology’s life cycle.
  • Indicator: Consumption of rare metals and minerals (660 kilograms of antimony equivalents), which measures resource-use efficiency in hardware production.

• For Target 12.2 (Efficient use of natural resources):

  • Indicator: Material consumption measured in antimony equivalents (0.16 milligrams per request). This is a specific metric for tracking the use of non-renewable natural resources.

• For Target 12.6 (Corporate sustainability reporting):

  • Indicator: The publication of a comprehensive life cycle assessment report itself.
  • Indicator: Adoption of the three proposed metrics for industry reporting: total impact of model training, per-inference impact, and the ratio of inference to overall life cycle impact.

• For Target 13.3 (Climate change awareness):

  • Indicator: Total greenhouse gas emissions generated (20.4 kilotons of CO₂ equivalents).
  • Indicator: Per-request greenhouse gas emissions (1.14 grams of CO₂ equivalents). Publishing these figures raises awareness of the technology’s climate impact.

4. Summary Table of SDGs, Targets, and Indicators

Source: the-decoder.com