Speed + max GPU memory allocated on an NVIDIA H200 for the computation of energies, forces and stress. The batch size is fixed to 1, but authors vary the number of atoms across the subplots. Relative times are computed with respect to the fastest model: orb-v3 Direct (20 neighbors). Times include both model inference and graph construction, with the latter marked by hatched lines. The graph construction method for Orb is a function of the number of atoms, as described in Appendix D. A key takeaway from this figure is that extreme scalability requires a confluence of i) efficient graph construction ii) Finite max neighbors iii) Non-conservative direct predictions. For the baselines, the authors use mace-medium-mpa-0 (v0.3.10, cuequivariance-torch v0.1.0), mattersim-v1.0.0-5m (v1.1.2), 7net-mf-ompa (v0.11.0). All models are benchmarked using PyTorch v2.6.0+cu124.

Speed + max GPU memory allocated on an NVIDIA H200 for the computation of energies, forces and stress. The batch size is fixed to 1, but authors vary the number of atoms across the subplots. Relative times are computed with respect to the fastest model: orb-v3 Direct (20 neighbors). Times include both model inference and graph construction, with the latter marked by hatched lines. The graph construction method for Orb is a function of the number of atoms, as described in Appendix D. A key takeaway from this figure is that extreme scalability requires a confluence of i) efficient graph construction ii) Finite max neighbors iii) Non-conservative direct predictions. For the baselines, the authors use mace-medium-mpa-0 (v0.3.10, cuequivariance-torch v0.1.0), mattersim-v1.0.0-5m (v1.1.2), 7net-mf-ompa (v0.11.0). All models are benchmarked using PyTorch v2.6.0+cu124.