Different Results Between CPU and GPU Nodes with Float32 Precision Models

[winskkk]

Dear DeepMD-kit community,
I am currently working with two different servers - one with CPU nodes and another with GPU nodes. On both servers, I have installed DeepMD-kit using the same command:
conda create -n deepmd deepmd-kit lammps horovod -c conda-forge

I've noticed an interesting behavior regarding precision settings. When I use models trained with default precision settings, the LAMMPS calculations yield identical results whether run on GPU or CPU nodes. However, when using models trained with "precision": "float32", I observe different results between GPU and CPU calculations. The difference is relatively small, approximately -0.0005 eV/atom.
I would greatly appreciate if someone could help me understand:

Why this discrepancy occurs specifically with float32 precision
What potential solutions might be available to ensure consistent results across both computing environments

Thank you in advance for your time and assistance.
Best regards

[njzjz]

In the LAMMPS, does it happen in the first step?