# A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model

Digest AI · Research · published 2026-10-02T04:00:00Z

Canonical: https://digestai.news/story/a-holistic-assessment-of-the-carbon-footprint-of-noor-a-very-large-ara

## Summary

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.

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.

## 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

## Why it matters

This is the first known attempt to quantify the full lifecycle carbon cost of a large Arabic model, offering a framework for future sustainability assessments in multilingual AI.

## Sources

1. [A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model](https://arxiv.org/abs/2610.00223) (arXiv cs.CL, 2026-10-02, primary source)

## Cite

Digest AI, "A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model", 2 October 2026, https://digestai.news/story/a-holistic-assessment-of-the-carbon-footprint-of-noor-a-very-large-ara

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