Artificial intelligence has been driven by a race toward ever-larger models. The next revolution, however, is making AI efficient enough to run where it creates the most value. Model compression reduces size and computational cost without sacrificing performance, enabling advanced Edge AI with lower energy consumption and greater privacy. This talk explores the latest advances in model compression, their industrial applications, and their role in building sovereign, efficient, and sustainable AI.

CIDAI | LinkedIn

Director, CIDAI

Joan Mas is a Telecommunications engineer (UPC) and MBA (Webster University). He worked for 10 years at the European Space Agency, the Netherlands, in the ENVISAT and METOP projects, Earth observation satellites. From 2000 to 2008 he developed at NTE S.A., equipment for the International Space Station and life support systems for manned space missions. Until 2024 he was Director of the Digital Technologies Division at Eurecat, promoting developments in areas such as Data Analytics and Big Data, Artificial Intelligence, Cybersecurity and others for multi-sector applications. He is currently Scientific Director in the digital field at Eurecat and, from 2021, he is also Director of CIDAI.

MULTIVERSE COMPUTING | LinkedIn

Jezabel R. Garcia is an AI Research Manager at Multiverse, where she leads research lines on reinforcement learning and alignment, model compression and efficiency, and model evaluation. Much of her work focuses on reinforcement learning for large language models, from training and alignment methods to the design of curricula and reward signals that make models more capable and reliable, alongside a strong hands-on interest in evaluation and failure analysis. She contributes to her company’s multilingual model efforts, with a particular interest in models that perform well across European languages.