# AI Sustainability The environmental cost of AI. Training a large model can emit as much carbon as five cars over their lifetime. Inference at scale consumes enormous energy. This is not a future problem; it is a current one that scales with adoption. Key dimensions: - **Energy consumption per query**: Frontier models consume orders of magnitude more compute than [[Small Language Models (SLMs)]]. A single GPT-4 class query uses roughly 10x the energy of a web search. Choosing the right model for the task matters (see [[AI Model Selection]]). - **Carbon footprint of training vs inference**: Training gets the headlines, but inference at scale often exceeds training costs. A model trained once but served billions of times accumulates carbon fast. - **Water usage**: Data centers require massive cooling. A single large training run can consume millions of liters of water. This is a resource cost that rarely appears in cost calculations. - **E-waste from GPU hardware cycles**: The race for faster hardware creates rapid obsolescence. GPU generations turn over every 1-2 years, generating significant electronic waste. Practical implications: - Choose smaller models when they are good enough. Not every task needs a frontier model. - Use [[Running AI Models Locally]] to reduce cloud compute dependency and gain visibility into actual resource consumption. - Batch requests and cache results to avoid redundant computation. - Apply [[AI Quantization]] to reduce compute per inference without proportional quality loss. - Factor sustainability into [[AI Cost Management]]; energy costs and carbon costs are converging. Increasingly a compliance and reporting requirement for enterprises. [[AI Governance]] frameworks now include environmental impact assessments. [[Responsible AI]] cannot ignore the environmental dimension. ## References ## Related - [[AI Model Selection]] - [[AI Cost Management]] - [[Running AI Models Locally]] - [[AI Quantization]] - [[Small Language Models (SLMs)]] - [[AI Governance]] - [[Responsible AI]]