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Molecular Dynamics (MD) simulation is an essential tool driving innovation in key scientific domains such as physics, materials science, biochemistry, and drug discovery. Enabling larger, longer, and more accurate MD simulations can directly impact scientific discovery and innovation. While domain-specific architectures for MD exist, they are not widely accessible, and MD performance on commodity platforms (i.e., CPUs and GPUs) remains critical for supporting broad and agile scientific progress.This paper aims at characterizing MD simulation on commodity platforms with a benchmark campaign on modern systems available in public cloud offerings. We focus on LAMMPS, one of the prevalent MD frameworks, and characterize several representative and diverse MD experiments. We find that the benchmarked CPU instance provides good scalability to many cores, while the reference LAMMPS GPU implementation struggles with scaling to multiple devices. Additionally, we evaluate the performance impact of application-specific parameters such as error threshold and arithmetic precision.Our study indicates that key drivers for further improvement of LAMMPS performance on commodity systems are: 1) improving scalability and offload efficiency in multiaccelerator systems and 2) reducing work imbalance in the CPU parallelization. Set initial positions and velocities Initial integration step: Get x(t+Δt), v(t+(½)Δt) Apply boundary conditions Update neighbor list data structures Compute non-bonded forces with an appropriate force field, compute bonded forces

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