A new open-source high-performance implementation of Born Oppenheimer molecular dynamics based on semiempirical quantum mechanics models using PyTorch called PYSEQM is presented. PYSEQM was designed to provide researchers in computational chemistry with an open-source, efficient, scalable, and stable quantum-based molecular dynamics engine. In particular, PYSEQM enables computation on modern graphics processing unit hardware and, through the use of automatic differentiation, supplies interfaces for model parameterization with machine learning techniques to perform multiobjective training and prediction. The implemented semiempirical quantum mechanical methods (MNDO, AM1, and PM3) are described. Additional algorithms include a recursive Fermi-operator expansion scheme (SP2) and extended Lagrangian Born Oppenheimer molecular dynamics allowing for rapid simulations. Finally, benchmark testing on the nanostar dendrimer and a series of polyethylene molecules provides a baseline of code efficiency, time cost, and scaling and stability of energy conservation, verifying that PYSEQM provides fast and accurate computations.