Performance analysis highlighted the importance of optimizing both the ML and the MM regions for efficient ML/MM simulations. The computational cost of our approach, although higher than MM simulations, remains significantly lower than QM/MM, with the use of LibTorch ensuring efficient GPU scaling for ML/MM tasks. Notably, alternative frameworks such as the EMLE engine leverage OpenMM for GPU-accelerated MM backends, providing an example of how MM optimization can further enhance performance. While our approach emphasizes tight integration within the Amber framework, future efforts could focus on integrating advanced ML/MM features with GPU-enabled molecular dynamics software to enhance both scalability and efficiency.