A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model
Most carbon footprint reports focus only on training compute, but this work expands the scope to include data collection, storage, research budgets, pretraining, future inference costs, and other project-related expenses.
Key points
- Study evaluates Noor’s full carbon footprint: training, data, storage, R&D, and future inference costs
- Inference and external factors account for a major share of the model’s total emissions
- Researchers propose pathways to reduce carbon impact of extreme-scale language models
The authors highlight that inference costs and external factors like international collaboration significantly contribute to the model’s total carbon footprint. They propose strategies to mitigate the environmental burden of such large-scale models, emphasizing the need for holistic assessments beyond training alone.
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