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Large Language Models (LLMs) have high electricity and water consumption due to the resource requirements of serving them to millions of users. This footprint can be reduced using methods developed by Professor Samin Aref (MIE) and his team, which produce smaller LLMs through quantizing their parameters. (image generated by ChatGPT)

How ‘slimmed-down’ large language models can reduce AI’s environmental and energy footprint

Farah Ghizzawi

How a passion for sustainable transportation brought this graduate student from Beirut to Toronto

Professor Craig Simmons receives the Engineers Canada Medal for Distinction in Engineering Education

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The study was led by Professor Milica Radisic (BME), left, and Mary Chuan Liu (BME PhD student). (photo courtesy of BME)

U of T study shows that fractal geometry can help kidney cells grow in a more mature form

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Iron Pin director Jenn Xu (Year 3 ChemE)

U of T Engineering hosts inaugural Iron Pin ceremony

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Construction by Toronto's waterfront

U of T Engineering researchers create first-ever tool to generate emissions budgets for 1,000 cities worldwide

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