Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, its computational cost grows with the number of model parameters, data points, and candidate models, often rendering practical applications infeasible. We propose a GPU-accelerated approach in which a sequential Monte Carlo sampler (SMCS) is executed in parallel on a GPU to perform Bayesian model selection for the number of spectral peaks and Bayesian estimation of peak-function parameters. Numerical experiments demonstrate that the GPU-parallelized SMCS reaches the free-energy accuracy of an optimized CPU-parallelized replica exchange Monte Carlo (REMC) baseline up to approximately $37\times$ faster—with smaller gains, down to near parity, in the least favorable settings—and is faster than GPU-parallelized REMC in many of the tested cases. The method is validated on artificial data designed to emulate x-ray photoelectron spectroscopy and x-ray diffraction measurements, as well as on real experimental spectra. As measurement techniques such as microscopic spectroscopy and in situ methods continue to drive rapid growth in the volume of spectral data, the proposed approach offers a practical computational foundation for advanced analysis of each individual dataset.

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• Bayesian methods
• Monte Carlo methods
• X-ray photoelectron spectroscopy
• X-ray powder diffraction