Gravitational wave astronomy has advanced from theoretical prediction to observational reality, with over 200 black hole and neutron star mergers detected in the past decade. The Einstein Telescope, a proposed next-generation detector with a candidate site at the border of the Netherlands, Germany, and Belgium, is expected to detect hundreds of thousands of events per year. However, analyzing this unprecedented volume of observations poses a fundamental challenge, as existing software cannot scale to extract science from such a rich dataset. We present ongoing development of a GPU-native framework written in JAX that accelerates the analysis of gravitational wave data from hours to mere minutes. Crucially, our approach avoids using machine learning surrogates, preserving high fidelity in the results while achieving this speedup. By developing and testing our framework on the Snellius GPU cluster, we underscore the Netherlands' active role in both the instrumentation and the data analysis for the Einstein Telescope.