A sustainable approach to AI is essential — the energy demands of training large models are growing exponentially, and Green AI seeks ways to reduce them.
Training GPT-3: ~502 tons of CO2. GPT-4 estimated at 10x more. The AI boom has an environmental cost. Green AI seeks answers.
Strategies¶
- Model selection: Do you need GPT-4? Or is Mistral 7B at 1/100th the energy sufficient?
- Quantization: INT8 = ~4x less energy
- Distillation: 90% quality at 10% compute
- Caching: Zero inference cost for cached responses
Measurement¶
CodeCarbon (Python library), ML CO2 Impact Calculator, cloud sustainability dashboards.
Our Rules¶
- Always the smallest model that meets the task
- Production inference always quantized
- Caching for recurring patterns
- Carbon footprint in quarterly reviews
Efficient AI Is Green AI¶
Smaller model, better prompt, smart caching — saves energy and money.
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