Atmospheric aerosols are a key component to understand the Earth’s climate, as they have a strong influence on clouds, which in turn strongly impact the global radiative budget. The ICON-HAM model couples ICON (Zängl et al., 2015) to the aerosol module HAM (Tegen et al., 2019). The aerosols are represented by seven log-normal distributions, where both their mass mixing ratio and number concentration are prognostically computed (Stier et al., 2005), requiring several tens of transported tracers. The cloud microphysics is implemented in a two-moment cloud scheme (Neubauer et al., 2019), which provides a dynamical link between the aerosols and their effects on clouds and precipitation. To reach cloud-resolving resolutions, the full HAM code was ported to GPUs using OpenACC directives. As expected, the new GPU-enabled ICON-HAM provided promising results in a first test setup, using 40 km horizontal resolution globally and 90 vertical levels over several model-months. However, while a substantial speedup was achieved (more than a factor 2), more complex performance issues likely stemming from the large number of tracers used in ICON-HAM exist, limiting optimal usage of the GPU architecture. To tackle these issues, we envision to extend the OpenACC parallelization to the tracers dimension.