Estimate annual carbon footprint of model usage based on query volume and usage patterns.
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Enter your AI query volume and infrastructure details.
Energy per Query = GPU Power (kW) x Model Multiplier x (Tokens / 1000) x 0.001
Daily Energy = (Query Energy + GPU Direct Energy) x PUE Ratio
CO2 Emissions = Energy (kWh) x Grid Carbon Intensity (gCO2/kWh)
Trees to Offset = Annual CO2 (kg) / 22 kg absorbed per tree per year
PUE (Power Usage Effectiveness) accounts for cooling and overhead in data centers. Average is 1.1-1.5.
AI models, particularly large language models, consume significant energy for both training and inference. While a single query uses a tiny amount of energy, at scale the impact is substantial. A company making 100,000 API calls per day to a large model generates a carbon footprint equivalent to several cross-country flights per year.
Use smaller models when possible (a 7B model uses roughly 10% the energy of a 100B+ model for many tasks). Cache frequent responses, batch queries, choose cloud regions with renewable energy, and optimize prompts to reduce token counts. Switching from GPT-4 class to a capable smaller model for routine tasks can reduce emissions by 80-90%.
France (nuclear heavy) averages ~60 gCO2/kWh, while the US average is ~400 and coal-dependent grids can exceed 800. Running workloads in low-carbon regions is one of the most impactful choices you can make. Major cloud providers publish regional carbon data to help you optimize.