The challenge is to measure and improve the environmental impact of the Swiss Large Language Model (Apertus, from the Swiss AI Initiative) and compare it with other models. Participants should design methods to quantify energy consumption, carbon emissions, and resource use during inference. In addition to transparent measurement frameworks or dashboards, solutions should propose concrete prompting strategies for impact reduction. The goal is to enable Switzerland to lead in sustainable AI by combining rigorous evaluation with actionable improvements.

Header photo: CSCS - Swiss National Supercomputing Centre

Purpose

The project aims to measure and improve the environmental impact of Large Language Models. It will create transparent benchmark and metrics as well as practical strategies to quantify and reduce energy use, carbon emissions, and resource consumption for inference. The goal is to enable sustainable AI that aligns with Switzerland’s leadership in responsible technology.

Inputs

A few initiatives (e.g. AI EnergyScore, MLCO2) have proposed frameworks for tracking carbon and energy usage, but these are not yet widely adopted or standardized. Based on one of these, we may try to:

• Develop measurement methods for the Swiss LLM’s energy and carbon footprint across its lifecycle.
• Explore prompting strategies for reducing impact
• Compare the energy consumption and the prompting strategies with other LLMs
• Document best practices and propose guidelines for sustainable Swiss AI.

Access to open source LLMs and underlying infrastructure is required to log compute usage, energy consumption, and hardware efficiency. We will provide a test machine (12GB VRAM 32GB RAM) on location, and remote access to a Mac Studio (192GB Unified Memory) for measurements.

Some technical information on the new Swiss LLM can be found here:

https://swissai.dribdat.cc/project/40

The following resources may be of use:

• https://ss64.com/mac/powermetrics.html
• https://codecarbon.io/
• https://huggingface.co/AIEnergyScore/spaces
• https://app.electricitymaps.com/
• https://mlco2.github.io/impact/
• https://ml.energy/zeus/

Papers of note:

• Green Prompting
• Evaluating the Energy–Performance Trade-Offs of Quantized LLM Agents (Weber et al 2025)
• Large Language Model Supply Chain: A Research Agenda (Wang et al 2025)
• Characterizing the Carbon Impact of LLM Inference (Lim et al 2024)
• Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference (Stojkovic et al 2024)
• LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models (Faiz et al 2023)
• MLPerf Inference Benchmark (Reddi et al 2020)

Comparison chart by Sourabh Mehta - ADaSci 2024

Outputs

The project will deliver measurements, guidelines for measuring and prompting techniques for reducing AI impact. It directly promotes open science, climate consciousness, and responsible AI. The outcomes can catalyze a larger initiative on sustainable foundation models in Switzerland, influencing both public policy and industry adoption.

Most foundation models today are evaluated primarily on accuracy, scale, and downstream performance. Environmental impact is often reported inconsistently or not at all. Large-scale LLMs typically lack region-specific sustainability benchmarks or actionable improvement strategies, so efforts to crowdsource such results will be valuable to the community.

The activities align with the Swiss AI Initiative’s goals of advancing responsible and trustworthy AI. By focusing on Switzerland’s energy mix and regulatory context, the project addresses local sustainability priorities while producing globally relevant methods. It strengthens Switzerland’s role as a leader in sustainable and ethical AI across Europe and beyond.

Compliance

Environmental impact measurement and mitigation align with Swiss and European climate targets, responsible AI guidelines, and open science principles. Data used will be technical (compute and energy metrics), avoiding personal or sensitive information.

Illustration by Nhor CC BY 3.0