Summary of A Water Efficiency Dataset For African Data Centers, by Noah Shumba et al.
A Water Efficiency Dataset for African Data Centers
by Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, Shaolei Ren
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a dataset that estimates water usage efficiency for data centers in 41 African countries across five climate regions. It also evaluates the water consumption of two large language models, Llama-3-70B and GPT-4, in 11 selected African countries. The findings show that writing tasks with these AI models can consume varying amounts of water, with some African countries consuming less than the global average. However, other African countries with a steppe climate may consume more water than the U.S. and global averages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how much freshwater is used by data centers in Africa. It also compares how much water two popular AI models use when doing different tasks. The results show that some African countries are actually pretty good at using less water, but others might use more than expected. This information could help people make better choices about where to put their AI technology. |
Keywords
» Artificial intelligence » Gpt » Llama
