Accurately modelling atomic-scale processes, such as protein folding and catalysis, is crucial in computational biology, chemistry, and materials science. While machine learning force fields (MLFFs) have emerged as powerful tools, approaching quantum mechanical accuracy with promising generalisation capabilities, their application is hindered by prohibitive inference times, particularly for long timescale simulations. In this work, we introduce BoostMD, a MLFF surrogate architecture, designed to mitigate this computational bottleneck. BoostMD leverages node features from previous molecular dynamics time steps to predict forces and energies, enabling the use of a smaller, faster model between evaluations of a large reference MLFF. The approach provides up to 8x speedup over the ground truth reference model. Testing on unseen dipeptides demonstrates that BoostMD accurately reproduces Boltzmann-distributed samples, making it a robust tool for efficient, long-timescale molecular simulations.