TECH & AI
As AI becomes woven into modern business, the need for evergreater computing power has driven rapid technological innovation, infrastructure development and skill adaptation.
Large language models( LLMs) from players like OpenAI, Anthropic and Google have captured the world’ s attention with their ability to parse and generate natural language with an apparently encyclopedic knowledge, powering everything from enterprise chatbots to advanced analytics.
But these models’ appetite for resources – particularly energy and water – is immense. A single ChatGPT query can consume up to 10 times more electricity than a traditional Google search, while the data centres training these models use millions of gallons of water for cooling.
The scale is staggering: training GPT-3 consumed an estimated 1,287 MWh of electricity, equivalent to powering 120 US homes for a year, while Microsoft’ s water consumption jumped 34 % in 2022, largely attributed to AI operations.
In recent years, small language models( SLMs) – models capable of supporting many powerful use cases but with a distinctly leaner footprint – have emerged as a nimble alternative to LLMs.
So, how are some of the world’ s largest organisations adopting SLMs at the heart of their sustainability and operational strategies?
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