Such domains include computer aided drug discovery, investigating enzymatic reaction mechanisms, predicting spectroscopic properties, etc. In QM/MM, the molecular system under investigation is partitioned into two regions and one region is described by an accurate quantum chemistry method while the other using a molecular mechanics force field. This allows modeling and simulation of chemical reactions with sufficient accuracy at an affordable computational cost. Often, the limiting factor to use large QM regions or carry out longer time scale simulations is the cost of QM methods. Typically, computing QM forces takes more than 95% of the total QM/MM time. In the recent past, various efforts have been undertaken to develop computationally affordable novel QM methods 8 or reimplement traditional QM methods to harness the power of massively parallel CPU and GPU hardware platforms. 9-40 Most notably, a number of leading quantum chemistry software packages have been empowered with GPU acceleration allowing users to achieve unprecedented simulation speeds and model larger molecular systems efficiently. For instance, our own GPU accelerated QUICK ab initio quantum chemistry and density functional theory package is highly efficient on NVIDIA hardware. QM/MM simulations with QUICK/AMBER have displayed respectable speedups of up to 53x for a single GPU with respect to a CPU core for a moderate sized QM region size that was benchmarked at the time. However, the GPU hardware landscape has started to significantly expand with new devices flowing in from other vendors such as AMD and Intel. There is a growing demand for such devices due to their attractive price to performance ratios. Therefore, in addition to supporting exist-ing NVIDIA hardware, enabling support for new GPU hardware has become important for traditional computational chemistry software packages. In the present work, we attempt to achieve this task by further improving the performance of QUICK on NVIDIA GPUs, and porting and optimizing it on AMD cards. By making some necessary changes to AMBER, we also enable more efficient QM/MM calculations on NVIDIA cards and add the ability to utilize AMD GPUs for QM/MM simulations. The next sections of this manuscript are organized as follows. First, we briefly present the theory of QM/MM and concepts of GPU computing. This is followed by details of new improvements to QUICK that enable better performance on NVIDIA cards. The porting of this QUICK version to AMD cards, optimization and changes to AMBER are then discussed. Finally, we benchmark the performance of the latest QM/MM implementation on NVIDIA cards against QUICK/AMBER v21, and on AMD cards.