The AI act is a new European Union-wide regulation of AI system. It includes specific provisions for general-purpose AI models which however need to be further interpreted in terms of technicalstandards and state-of-art studies to ensure practical compliance solutions. This paper examines the AI act requirements for providers and deployers of general-purpose AI and further focuses on uncertainty estimation as a suitable measure forlegal compliance and quality assurance in training of such models. We argue that uncertainty estimation should be a required component for deploying models in the real world, and under the EU AI Act, it could fulfill some requirements for transparency and trustworthiness. However, generally using uncertainty estimation methods increases the amount of computation, producing a dilemma, as computation might go over the threshold (10^25 FLOPS) to classify the model as systemic risk.