Asking ChatGPT on Sustainability

Another question Feilner IT asked chatGPT shows the AI struggling with giving an answer that isn’t really easy…

Feilner IT asked: “How much Co2 does an average chatGPT request cause and how can we save energy with chatGPT?”

Here’s what ChatGPT answered:

“It is difficult to estimate the exact amount of CO2 emissions caused by a single request to a language model like ChatGPT, as it would depend on factors such as the energy source used by the servers running the model, the efficiency of the servers, and the number of requests being processed at the same time. However, it’s clear that machine learning models require a lot of computational power, which can be energy-intensive.

In terms of reducing the energy consumption of AI models, there are a few potential approaches:

Model compression: Techniques such as pruning, quantization, and knowledge distillation can be used to reduce the size and computational requirements of a model without sacrificing too much accuracy.

Energy-efficient hardware: Using hardware specifically designed to be energy-efficient, such as Google’s Tensor Processing Units (TPUs) for training and inference, can help to reduce the overall energy consumption of a model.

Cloud-based inference: Instead of running models on local devices, inference can be done on cloud-based servers. This allows for more efficient use of resources, as the servers can be used by multiple clients and can be powered by renewable energy sources.

Model distillation: Distilling a complex model into a simpler one, while preserving the accuracy. This can significantly decrease the energy consumption during the inference stage.

Overall, it is important to consider the environmental impact of AI and to make efforts to minimize the energy consumption of models where possible.”