A recent study has highlighted the water footprint of AI models, particularly in relation to data centers that power and train these models. The water footprint of AI refers to the amount of water needed for electricity generation and cooling in data centers. It consists of direct water consumption, which is the water evaporated or discharged during the cooling process, and indirect water consumption, which is the water used for electricity production.
The water
footprint of AI varies depending on factors such as the AI model's type and
size, data center location and efficiency, and the source of electricity. For
instance, training a large AI model like GPT-3 can directly consume up to 700,000
liters of clean freshwater, which is equivalent to producing hundreds of cars.
Engaging in
conversations with AI chatbots like ChatGPT also has a water consumption
impact. The study estimates that a single conversation with ChatGPT can consume
around 500 ml of water for 20-50 questions and answers. Considering the large
user base and multiple conversations, the cumulative water consumption becomes
significant. The upcoming GPT-4 model is expected to increase water consumption
even further, although estimating its water footprint is challenging due to
limited data availability.
Despite the
digital nature of AI activities, physical data storage and processing occur in
data centers, which require substantial cooling systems. These systems rely on
water-intensive processes like evaporative cooling towers. Additionally, data
centers need pure freshwater for maintaining system integrity and significant
water for power generation.
These findings raise concerns about the environmental impact of AI and emphasize the need to address the water footprint of AI models to ensure sustainable development in the field.
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