Multimedia AI now characterizes multiple concurrent streams at high resolutions, spanning video, audio, language, and multimodal fusion. Energy, bandwidth, and even water for cooling have become first-order constraints for real deployments. Training a single large NLP model has been estimated to emit up to 284 tons of CO2, while infrastructure and model choices can shift emissions by orders of magnitude; at the same time, data centers consume huge volumes of water for cooling (hundreds of thousands). In this position paper, we argue that we cannot continue to choose models based on accuracy and throughput alone. We propose a Sustainability Card for Multimedia AI - Correct Outputs per kWh, Joules per Sample, Bytes per Output, and optional Water per 1,000 Outputs - and outline a simple measurement protocol.
The Sustainability Card: Measuring Sustainability of Multimedia AI Models
Pistolesi, Francesco
;Baldassini, Michele;Mugnai, Matteo;Lazzerini, Beatrice
2025-01-01
Abstract
Multimedia AI now characterizes multiple concurrent streams at high resolutions, spanning video, audio, language, and multimodal fusion. Energy, bandwidth, and even water for cooling have become first-order constraints for real deployments. Training a single large NLP model has been estimated to emit up to 284 tons of CO2, while infrastructure and model choices can shift emissions by orders of magnitude; at the same time, data centers consume huge volumes of water for cooling (hundreds of thousands). In this position paper, we argue that we cannot continue to choose models based on accuracy and throughput alone. We propose a Sustainability Card for Multimedia AI - Correct Outputs per kWh, Joules per Sample, Bytes per Output, and optional Water per 1,000 Outputs - and outline a simple measurement protocol.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
