The energy impact of artificial intelligence is an increasingly relevant topic. Training a model like GPT-5 consumes as much electricity as hundreds of homes for a year.
How much energy does AI consume?
Training a large model can emit between 100 and 1000 tons of CO2 equivalent, comparable to the carbon footprint of dozens of transatlantic flights. And inference, the daily use of the model, consumes even more energy in the long run.
It is estimated that by 2027, AI energy consumption could equal that of entire countries.
Why does it consume so much energy?
Training requires thousands of GPUs running 24/7 for weeks or months. Each GPU consumes 300-700W, and data centers need additional cooling.
Inference is also costly: each query to a large model requires multiple GPUs and several seconds of computation.
Strategies for more sustainable AI
Smaller models: Use specific models for each task instead of one giant model for everything. A small classification model consumes 1000 times less energy than GPT-5.
Distillation: Train small models that mimic large models, maintaining much of the performance with a fraction of the consumption.
Efficient hardware: More efficient GPUs and TPUs, and specialized hardware like neuromorphic chips.
Renewable energy: Train in data centers powered by renewable energy.
What businesses can do
Optimize model usage (don't use the largest model for simple tasks), implement caching of frequent responses, and choose providers with sustainability commitments.
Sustainable AI is not an option, it is a necessity. At Vynta we optimize resource usage in our AI projects to minimize environmental impact without sacrificing results. Contact us for a sustainable AI consultation.