Flood simulation and forecasting in mid- to large-sized catchments has long relied on coupling hydrological and hydrodynamic models, a two-tiered modelling strategy driven mainly by the assumption that physically-based hydrodynamic simulations at the resolutions needed to resolve channels and floodplains are computationally prohibitive at catchment scale. Recent advances in multi-GPU high-performance computing, together with increasing availability of high-resolution geospatial data are challenging this assumption. In this study, we test whether a fully hydrodynamic, rain-on-grid approach can be a standalone alternative to the traditional coupled chain, using the July 2021 flood in the Ahr catchment, Germany. We apply the performance-portable multi-GPU shallow-water solver SERGHEI to the entire ~900 km² Ahr catchment at dx = 2, 5, and 10 m, forcing it directly with the 5-minute RADOLAN precipitation product. Assessed against the observed event, the rain-on-grid setup reproduces the flood characteristics remarkably well. Model skill metrics are comparable to –and in some metrics improving–, those reported by previous inundation studies relying on reconstructed hydrographs or hydrological-hydrodynamic two-tiered modelling chains for the same event. Remarkably the present setup using no hydrological model, no reconstructed inflow hydrograph, and no parameter calibration, and covering a domain roughly an order of magnitude larger, achieves very good results. We further show that the approach scales efficiently across two generations of GPU supercomputers, completing the 96-hour event between roughly 18 and 420 times faster than real time, well within operational early-warning requirements. The results suggest that, for catchments of this scale and event types of this nature, catchment-scale rain-on-grid hydrodynamic modelling has reached a level of maturity that justifies its use as a standalone alternative to the more traditional coupled hydrological–hydrodynamic chain.