Expected Outcome: This topic aims to advance the scientific and technical foundations for the scaling of AI models, enhancing the performance of training and inference processes and providing European AI labs with the methodologies required to compete at the global frontier. Project results are expected to contribute to some of the following outcomes: Establishment of robust scaling laws and predictive frameworks that allow for the reliable estimation of model performance before large-scale compute investment. Enhanced competitiveness of European advanced AI models through optimised dataset mixtures, refined architectures, and superior hyperparameter configurations. Optimised development for frontier AI models, achieved via automated and scientifically grounded scaling methodologies. Scope: To maintain a leading position in AI, Europe must master the methodologies required to scale AI models’ performance, training and inference processes. Establishing a predictive methodology for AI model development ensures that limited computational resources are deployed with maximum efficiency, thereby bolstering the EU and AC’s technological sovereignty. While previous actions have focused on
Datos de la Comisión Europea. La convocatoria oficial y sus bases son las que valen.
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