Nowadays, the usage of GPU accelerators in scientific computing is rapidly increasing. In various fields such as molecular and astrophysics simulations, geophysics, and artificial intelligence models, modern graphic cards show significant improvement in computational speed and energy efficiency. Multiple hardware brands and their GPU products offer competitive performance at wide range of prices. Therefore, it is crucial that proper benchmarking of suitable test systems set be carried out in order to estimate the best configuration for certain computational cluster or software application. Here, we present benchmarks of Nvidia A100, Nvidia A40, AMD MI250X and Intel Ponte Vecchio graphic cards using the GROMACS 2023 and Amber22 molecular modeling packages. To test the GPU occupancy and eventual computational saturation, we selected a series of systems, each increasing in size by a factor of two from the previous. To estimate the maximum productivity that can be obtained from multiple GPUs located on a single node, we carried out the simulations in such a way so that all GPUs on a node were occupied during the benchmark.