Gen AI Might burn the planet

The very high costs of Gen AI (especially AI Art)

3 min readDec 4, 2023

Before you get all gung-ho about Gen AI, make sure you consider the environmental impacts.

A fantastic new paper- Power Hungry Processing: Watts Driving the Cost of AI Deployment?- sheds light on the energy costs of running Gen AI Models. The research brings concrete numbers to what many have long speculated: the energy costs of deploying LLM costs would put a giant dent in our environment.

“We find that multi-purpose, generative architectures are orders of magnitude more expensive than task-specific systems for a variety of tasks, even when controlling for the number of model parameters. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions.”

Keep in mind the research likely we must also consider the costs associated with data pipelines, indexing and other energy-intensive processes that

My first impressions- this is yet another argument for why organizations should consider rearchitecting AI systems bottom-up. Instead of running after the biggest/fanciest model being used for most use cases: build a system of system of smaller task-specific expert models that handle 90% of the operations. The more specific the model, the more we can reduce the costs of training and inference. Good data/feature engineering can really make up for a large difference in model capabilities.

Another observation- This is not a good look for multi-modal RAG with documents/charts, which will rely extensively on generating/processing images. For some design work, we might need to explore alternatives for design generation. My guess is something like strong profiling combined with Evolutionary Algorithms might be worthwhile for improving designs long term

Paper- https://arxiv.org/abs/2311.16863

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