Gravitational-wave inference at GPU speed: A <tt>bilby</tt> -like nested sampling kernel within <tt>blackjax-ns</tt>

Abstract We present a GPU-accelerated implementation of the gravitational-wave Bayesian inference pipeline for parameter estimation and model comparison. Specifically, we implement the ‘acceptance-walk’ sampling method, a cornerstone algorithm for gravitational-wave inference within the bilby and dynesty framework. By integrating this trusted kernel with the vectorized blackjax-ns framework, we achieve typical speedups of 20–40×, when comparing total CPU and GPU core hours, for aligned spin binary black hole analyses, while recovering posteriors and evidences that are statistically identical to the original CPU implementation. While CPU and GPU core hours are not equivalent, we show nevertheless that the GPU-accelerated analyses are up to 3× more cost-effective than their CPU counterparts. This faithful re-implementation of a community-standard algorithm establishes a foundational benchmark for gravitational-wave inference. It quantifies the performance gains attributable solely to the architectural shift to GPUs, creating a vital reference against which future parallel sampling algorithms and machine learning based methods can be rigorously assessed. This allows for a clear distinction between algorithmic innovation and the inherent speedup from hardware. Our work provides a validated community tool for performing GPU-accelerated inference on gravitational-wave data, and demonstrates the scaling potential of nested sampling.