The second major opportunity lies in leveraging hardware advancements, which play a crucial role in boosting the performance of de novo sequencing models. The integration of graphical processing units (GPUs) allows for faster and more efficient processing of large datasets, while tensor processing units (TPUs) are specifically optimized for accelerating deep learning model training and inference. Furthermore, field-programmable gate arrays (FPGAs), such as those used in RapidOMS for spectral library searching, offer a 60-fold speedup compared to CPUs, and a 2.72-fold speedup compared to GPUs, along with an 11-fold improvement in energy efficiency compared to GPUs. Such efficiency improvements are essential as deep learning models become more complex and datasets grow in size.