Training and running AI models is straining power grids. The industry is scrambling for sustainable solutions.
The energy cost of artificial intelligence has become impossible to ignore. Data centres now consume approximately 4% of total US electricity generation—up from 2.5% in 2023—with AI workloads accounting for an estimated 40% of that growth. The International Energy Agency projects data centre power consumption will double again by 2030.
The numbers are staggering at the individual model level. Training GPT-5 consumed an estimated 50 GWh of electricity—enough to power 4,500 US homes for a year. Google DeepMind's Gemini Ultra 2 training run was comparable. And training is just the beginning; inference (running the models to serve user requests) consumes 5–10x more energy over a model's lifetime than training.
The industry response has been a rush toward nuclear and renewable energy. Microsoft signed a deal to restart the Three Mile Island nuclear plant to power its AI data centres. Amazon purchased a nuclear-powered data centre campus in Pennsylvania. Google and Meta have both signed long-term power purchase agreements for new solar and wind capacity.
Efficiency improvements are helping at the margins. New model architectures like Mixture-of-Experts reduce inference energy by activating only a subset of parameters per query. Quantisation techniques (running models at lower numerical precision) can cut energy consumption by 50–75% with minimal quality loss. NVIDIA's Blackwell GPU architecture delivers 4x better energy efficiency per FLOP than its predecessor.
Vincony's platform runs on energy-efficient infrastructure and supports quantised model variants that deliver equivalent quality at a fraction of the energy cost. When you run a model on Vincony, the system automatically selects the most efficient variant that meets your quality requirements.
The environmental implications of AI growth are a legitimate concern, but the answer is unlikely to be 'stop building AI.' The more productive path is investing in efficiency, renewable energy, and transparent reporting of AI's environmental footprint.