How can I use generative AI tools in a more sustainable way?
Generative AI tools consume energy in several ways. When the large language model is being trained and being fine-tuned, it consumes a lot of energy; up to 33 times the amount consumed by computers running task-specific software (2023). For outputs, the amount of energy consumed will vary depending on the task being performed.
LLMs, both for training and their operation, require a lot of processing on the servers housed in data centres. The servers are cooled with water, which may have an impact on the local environment. Many AI centre are aiming to use 100% renewable energy in the near future; this could be a factor when considering for procurement. To save energy, action can be taken by the developers of the large language model and by you when you use the tool(s) using the LLM.
By applying a method called quantisation (using fewer decimal places to round down the numbers used in calculations), the energy usage of the model dropped by up to 44% while maintaining at least 97% accuracy compared to the baseline1. This is because it is easier to get to the answer, in much the same way as most people could calculate two plus two much more quickly than calculating 2.34 plus 2.17, for example. (2025).
Quantisation, combined with cutting down user prompt and AI response length from 300 to 150 words, could reduce energy consumption by 75%. The biggest gains in energy efficiency can be achieved by switching from large models to smaller, specialised models in certain tasks such as translation or knowledge retrieval” (2025).
Newer AI models such are DeepSeek use a Mixture-of-Experts (MoE) architecture, which activates only the relevant sub-models for each specific task – thereby reducing the computing power requirements. It requires one-tenth of the GPU hours (the time that GPUs operate at full capacity during model training), used by Meta's model, resulting in a reduced carbon footprint, decreased server usage and lower water requirements for cooling systems. There are cautions that this benefit may be short lived as global use continues to rise.
Do you need to use generative AI tools for the task you're planning to use it for?
References
Luccioni, Alexandra Sasha, et al. (2023) Power Hungry Processing: Watts Driving the Cost of AI Deployment? Cornell University. arXiv:2311.16863. Available at: https://arxiv.org/abs/2311.16863. (Accessed: 30 July 2025)
UCL News (2025) Practical changes could reduce AI energy demand by up to 90%. Available at: https://www.ucl.ac.uk/news/2025/jul/practical-changes-could-reduce-ai-energy-demand-90. (Accessed: 30 July 2025)