We present the development of a portable high-level Python implementation for the next-generation ECMWF global dynamical core designed to facilitate simulations at extreme numerical resolutions. This new model framework, called the Portable Model for Multi-Scale Atmospheric Prediction (PMAP), is an advancement of the Finite-Volume Module (FVM) originally developed at ECMWF using Fortran. The key aspect of the global PMAP is its implementation utilizing the latest version of the GridTools for Python (GT4Py) domain-specific library, named gt4py.next. This library is tailored to the applied conservative finite-volume discretization methods that support, among others, the operational octahedral grid at ECMWF. The gt4py.next library itself is being co-developed with various Swiss partners and is under continuous extension, optimization, and refinement alongside the PMAP framework. The model’s distributed multi-node configuration employs the Generic exascale-ready library for halo-exchange operations (GHEX). We present recent model validation results and report on performance, portability, and scalability across selected CPU- and GPU-based supercomputers.