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This blog post outlines why large language models, thus generative AI models need so much power to be trained, deployed and maintained. We also look why some solutions are more sustainable compared to others.
There are three main values that impact the carbon footprint of llms like GPT-4:
1) The footprint of the training model
2) The footprint from inference. "Inference" in large language models is referring to the capability of llms predicting outcomes using new input data. The models essentially use past knowledge to make educated guesses about the meaning of new sentences or to predict what comes next in a conversation. It's like connecting the dots using what they've learned from reading lots of text.
3) The footprint needed to produce all the required hardware and capabilities of the cloud data center.
Most energy-intensive is the trainint part of such models. Importantly, larger models do use more energy during their deployment. However, this study shows that inference is also a major consumer of energy, with up to 90% of the ML workloads is due to inference processing.
We summarized some important facts for you to shed lights on the environmental impact of large language models:
If possible, use existing, pre-trained models from llm providers such as Microsoft OpenAI, Google (bard, PaLM), Meta AI (LlaMA) and do not create your own generative models (sidemark: this also costs around $ 100 Mio. according to a recently published article on HuggingFace. You should - instead - still fine-tune a model for your use case.
Please find a comparison between llms and their sizes in the previous blog. Additional material can be found here:
Responsible AI is a crucial foundation for all generative AI products. It is important that we consider carbon footprint with AI models to protect our earth and future.
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