Energy-intensive processes

Mitigation strategy

1. Prioritize model optimization techniques such as quantization, parameter pruning, and knowledge distillation to create smaller, task-specific models, which drastically reduce the computational load and associated energy consumption during both training and inference phases. 2. Mandate the strategic co-location of AI compute infrastructure in geographical regions with verified access to low-carbon or renewable energy grids, simultaneously integrating energy-efficient hardware (e.g., specialized accelerators) and advanced cooling systems like liquid cooling to minimize the operational carbon and water footprint. 3. Implement rigorous data efficiency and governance protocols across the AI lifecycle, emphasizing data minimization, cleaning, and curation to prevent the use of irrelevant or redundant datasets, thereby decreasing the substantial energy and storage overhead associated with massive data acquisition and pre-training steps.