: Ms Word, Ms Word Format
: 60 Pages
: ₦10,000
: 1-5 Chapters

THE IMPACT OF TIMESHIFTING STRATEGIES FOR CARBON-EFFICIENT LONG-RUNNING LARGE LANGUAGE MODEL TRAINING

Abstract:

As the demand for advanced natural language processing models continues to grow, so does the environmental impact associated with their training. Large language models, such as GPT-3.5, require extensive computational resources and contribute significantly to carbon emissions. This study explores the implementation of timeshifting strategies to mitigate the environmental footprint of long-running large language model training.

Timeshifting involves optimizing the scheduling of computational tasks during training to leverage periods of lower energy demand or increased availability of renewable energy sources. By shifting the training workload to times when energy grids are powered by cleaner sources, such as wind or solar, the carbon efficiency of large language model training can be improved.

This research investigates various timeshifting approaches, considering factors such as geographical location, time-of-day energy mix, and grid-level demand patterns. Through a comprehensive analysis, we aim to quantify the environmental impact reduction achieved by implementing timeshifting strategies in large language model training scenarios.

Additionally, the study explores the trade-offs between training efficiency and environmental sustainability. The impact on model performance, convergence speed, and resource utilization will be examined to strike a balance between carbon efficiency and training effectiveness.

The findings of this research can guide the development of best practices for carbon-efficient long-running large language model training. By adopting timeshifting strategies, the research community and industry can contribute to the responsible and sustainable advancement of artificial intelligence technologies. The outcomes of this study will provide valuable insights for researchers, developers, and policymakers seeking to address the environmental challenges associated with training state-of-the-art language models.