In today's era of computational science, approaches powered by artificial intelligence (AI) have found their way into nearly every scientific domain. From natural sciences to the humanities, especially the emergence of large language models (LLMs) and their general purpose capabilities have opened new ways of processing unstructured data in a scientific context. However, training state-of-the-art AI models, even running them for inference, requires substantial computational resources. High-Performance Computing (HPC) systems, like the ones at the Max Planck Computing and Data Facility (MPCDF), are fitting solutions for executing these demanding AI workloads, and their integration into HPC workflows is increasingly sought after by the scientific community. In this talk we showcase two projects in which such workloads are successfully handled at the MPCDF in close collaboration with the Neurophysics group at the CBS. These projects use AI models to reconstruct and automatically segment high-resolution MRI images. Additionally, we will present a couple of projects revolving around LLMs at the MPCDF and highlight their computational challenges and solutions for HPC systems. [mehr]