FEATURE

With fossil fuels proving disastrous for the planet , paired with overloaded , often unstable power grids , it ’ s clear that more innovative and sustainable solutions must be prioritised if organisations are to utilise compute-heavy AI technologies while overcoming these challenges .

Here , the data centre industry has a key role to play . Data centres that are designed to deal with the high-intensity compute required to meet AI ’ s needs efficiently while utilising renewable energy sources , represent the best of both worlds .

Generative AI : Unparalleled power and carbon emissions to match is more complex and can produce new or original content , like chat responses , software code and even deepfakes . Unsurprisingly , this complexity requires more computing power and consumes more energy .

In 2019 , researchers at the University of Massachusetts found that the energy consumed in creating a Generative AI model named BERT , consisting of 110 million parameters , was equivalent to the energy used for a round-trip transcontinental flight by a single person . More recently , researchers from Google and the University of California , Berkeley , estimated that creating the much larger GPT-3 , which has 175 billion parameters , consumed 1,287MW hours of electricity and generated 552 tons of carbon dioxide , the equivalent of 123 gasoline-powered passenger vehicles driven for one year .

The more powerful the AI model , the higher these emissions will be . With organisations across industries now utilising Generative AI for a variety of purposes , the potential impact on the environment is staggering – and that ’ s not to mention the introduction of ChatGPT to the public , which has created another path for carbon emissions through Generative AIpowered searches . Reuters reports that ChatGPT is the fastest-growing consumer application in history , estimated to have reached 100 million monthly active users in January 2023 , just two months after launch .

Tate Cantrell , CTO , Verne Global

The enormous computational power needed for AI technologies and the extremely high energy consumption this entails is naturally a cause for concern . The carbon cost of these applications is especially high when taking into consideration the energy required to keep equipment cool – with the average data centre committing 40 % of its energy consumption to cooling alone .

As we face an increasingly acute climate crisis , it ’ s more important than ever that both data centres and organisations investing in AI technologies consider the carbon impact and find solutions to optimise energy efficiency and sustainability .

Optimising data centre infrastructure

Fortunately , there are steps organisations can take to access innovative AI

It ’ s no secret that AI is power-hungry . Training Deep Learning applications , for example , requires an extraordinary amount of computational power – OpenAI ’ s research found that this has been doubling every 3.4 months since 2012 . This necessitates high energy consumption and therefore , often , significant carbon emissions .

Generative AI goes beyond traditional AI , which can only classify and analyse the data it is provided to detect patterns and make conclusions . Generative AI

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