  We introduce galmoss, a python-based, torch-powered tool for two-dimensional
fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss
meets the high computational demands of large-scale galaxy surveys, placing
galaxy profile fitting in the LSST-era. It incorporates widely used profiles
such as the S\'ersic, Exponential disk, Ferrer, King, Gaussian, and Moffat
profiles, and allows for the easy integration of more complex models. Tested on
8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single
NVIDIA A100 GPU, galmoss completed classical S\'ersic profile fitting in about
10 minutes. Benchmark tests show that galmoss achieves computational speeds
that are 6 $\times$ faster than those of default implementations.
