The article presents an implementation of generalized Born (GB) implicit solvent all-atom classical molecular dynamics (MD) simulations within the AMBER program package, running entirely on CUDA-enabled NVIDIA GPUs. The authors discuss the algorithms used to leverage the processing power of GPUs and compare the performance with conventional CPU clusters. The implementation supports three precision models: single precision floating-point arithmetic with double precision accumulation (SPDP), single precision (SPSP), and double precision (DPDP). The study focuses on the implications of different precision models on the outcome of implicit solvent MD simulations, including accuracy of force evaluations, energy conservation, and structural properties relevant to protein dynamics. The numerical noise in the SPSP model can lead to significant errors in long-time scale simulations, while the SPDP model provides comparable numerical results to the DPDP model with significantly reduced computational cost. The implementation achieves performance comparable to and in some cases exceeding that of traditional supercomputers, making it a powerful tool for researchers.The article presents an implementation of generalized Born (GB) implicit solvent all-atom classical molecular dynamics (MD) simulations within the AMBER program package, running entirely on CUDA-enabled NVIDIA GPUs. The authors discuss the algorithms used to leverage the processing power of GPUs and compare the performance with conventional CPU clusters. The implementation supports three precision models: single precision floating-point arithmetic with double precision accumulation (SPDP), single precision (SPSP), and double precision (DPDP). The study focuses on the implications of different precision models on the outcome of implicit solvent MD simulations, including accuracy of force evaluations, energy conservation, and structural properties relevant to protein dynamics. The numerical noise in the SPSP model can lead to significant errors in long-time scale simulations, while the SPDP model provides comparable numerical results to the DPDP model with significantly reduced computational cost. The implementation achieves performance comparable to and in some cases exceeding that of traditional supercomputers, making it a powerful tool for researchers.