Molecular dynamics (MD) simulations compute the time evolution of atomic systems by integrating Newton's equations of motion. Applications include drug discovery (binding affinity, ADMET prediction), protein folding, materials science, and chemical engineering.
The dominant GPU-accelerated MD codes in 2026 are GROMACS, AMBER, OpenMM, and NAMD. Performance scales primarily with single-precision and double-precision FLOPs, memory bandwidth, and inter-GPU bandwidth for multi-GPU runs. Unlike most AI workloads, MD often benefits from FP64 capability — though FP32 mixed-precision schemes have become more common.
Workloads range from small single-protein simulations (suitable for workstation cards) to large multi-million-atom systems requiring multi-GPU datacenter configurations. Long timescale simulations and free-energy calculations dominate compute budgets in academic and pharma research environments.