.jpg)
The growing use of large language models (LLMs) in industry poses an environmental challenge that goes beyond energy consumption during operation. A recent life cycle assessment (LCA) on the pre-training of the Lucie 7B model, developed by the OpenLLM-France consortium and executed on the Jean Zay supercomputer, reveals key data: the total carbon footprint of training reached 21 tCO2eq, considering both hardware manufacturing and electricity consumption. This analysis, framed within the AFNOR SPEC 2314 specification for frugal AI, breaks down emissions by subsystems (computing, storage, cooling) and reports a water consumption of 76 m³ during the campaign. The conclusion is clear: sustainability in artificial intelligence depends not only on efficient algorithms, but on a comprehensive vision that encompasses infrastructure, cooling, and heat recovery.
For companies adopting artificial intelligence, this type of study offers a strategic lesson: measuring environmental impact allows for optimizing resources and aligning innovation with corporate responsibility goals. At Q2BSTUDIO, we understand that the adoption of AI for businesses must be accompanied by a technical approach that considers everything from model selection to the underlying infrastructure. Therefore, we develop custom applications that integrate AI agents and automation processes, always evaluating computational efficiency and the software life cycle.
Beyond training, the continued operation of models requires optimized cloud architectures. Our AWS and Azure cloud services allow scaling AI workloads with visibility into energy consumption and associated emissions. Likewise, cybersecurity and data governance are pillars in any artificial intelligence project; from Q2BSTUDIO we offer custom software solutions that secure data pipelines and ensure regulatory compliance. We also implement business intelligence services with Power BI to monitor environmental and performance indicators, helping organizations make data-driven decisions.
The integration of AI agents into business processes, combined with a frugal design, not only reduces costs but also minimizes the environmental footprint. The case of Lucie 7B demonstrates that it is possible to train multilingual models with a manageable impact if metrics such as WUE (water usage) and ERF (heat reuse factor) are applied. At Q2BSTUDIO, we accompany companies in this transition, offering consulting and software development that incorporates these principles from the design phase, delivering solutions that balance innovation, performance, and sustainability.
