FINAL WORD
Organisations wishing to create an AI model should weigh the added value of the AI model against its environmental impact .
Think of any topic and an AI tool can effortlessly generate an image or text . Yet the environmental impact of , say , generating an image by AI is often forgotten . For example , generating one image by AI consumes about the same amount of power as charging your mobile phone . A relevant fact when you consider that increased number of organisations are betting on AI .
After all , training AI models requires huge amounts of data , and data centres are needed to store all this data . In fact , there are estimates that AI servers , in an average scenario could consume 85 to 134 terawatt hours of power annually by 2027 . The message is clear : AI consumes a lot of energy and will therefore have a clear impact on the environment .
To create a useful AI model , a number of things are needed . These include training data , a stable Internet connection , sufficient storage space and GPUs . Each component consumes energy to some extent , but the computing power required by GPUs consumes the most . According to researchers at OpenAI , the amount of computing power used has been doubling every 3.4 months since 2012 .
This is a huge increase that is likely to continue in the near future , given the popularity of various AI applications . This increase in computing power is having an increasing impact on the environment . To illustrate , a study by the University of Massachusetts found that training popular AI models could lead to the emission of 284,000 kg of CO 2
, as much as an average car driving 31 times around the world .
Organisations wishing to create an AI model should therefore carefully weigh the added value of the AI model against its environmental impact . In addition to this , the underlying infrastructure and the GPUs themselves need to become more , energy-efficient .
A number of industries are important during the process for making an AI model : the data centre industry , the power sector , the semiconductor industry , telecom operators and the storage industry . To reduce AI ’ s impact on the environment , steps need to be taken in each of these sectors to improve sustainability .
In the storage industry , concrete steps can be taken to reduce the environmental impact of AI . An example is all-flash storage solutions , which are significantly more energy-efficient than traditional disk-based storage , HDD . In some cases , all-flash solutions can deliver an 85 % reduction in energy consumption compared to HDD .
Some vendors are even going beyond off-the-shelf SSDs and developing their own flash modules , allowing all-flash arrays to communicate directly with flash storage . This makes it possible to maximise flash ’ s capabilities and achieve even better performance , energy usage and efficiency i . e . data centres require less power , space and cooling .
An additional advantage of all-flash solutions is that they are also better suited to running AI projects compared to HDD solutions . This is because linking AI models with data requires a storage solution that provides reliable and easy access to data across silos and applications at all times ,
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