Optimizing Sustainability in Training Large GenAI Models

Objectives to cover:

Introduction to GenAI Model Training and Environmental Impact

• Brief overview of large GenAI models
• Environmental concerns associated with AI training

Current Energy Consumption in AI Training

• Energy demands of GenAI model training
• Emissions generated from traditional AI infrastructure

Factors Contributing to High Carbon Footprint

• Data center energy use and inefficiencies
• Computational intensity and resource usage
• Model size and complexity considerations

Strategies for Reducing Carbon Emissions in AI Training

• Optimizing model architectures
• Efficient hardware utilization (e.g., using GPUs and TPUs)
• Data center energy efficiency improvements

Leveraging Renewable Energy Sources

• Transitioning AI training centers to renewable power
• The role of carbon offset programs

Case Studies of Sustainable AI Initiatives

• Examples of companies and research institutions focused on reducing emissions
• Practical impact and results achieved

Future Directions for Sustainable AI Development

• Innovations in low-energy algorithms
• Potential for regulatory standards and incentives

Conclusion

Reference:  Harvard style