# Tensor Core FP32 methods

Machine-learning interatomic potentials (MLIPs) have largely kept inference in FP32 because molecular dynamics is sensitive to small force errors. For PET, an MLIP from EPFL's Lab COSMO, BF16x9 on Blackwell GPUs and TF32x3 on Ampere GPUs decompose each FP32 matrix-multiply operand into lower-precision components evaluated on Tensor Cores, with no retraining or architecture change. On a 17,280-atom water box, BF16x9 sped PET's large GEMMs by 2.2–2.5x and the full MD step by up to 1.7x; TF32x3 sped major GEMMs on A100 by about 1.7–1.9x and the MD step by up to 1.5x, while keeping energy conservation and structural relaxation close to FP32.

Machine-learning interatomic potentials (MLIPs) have largely kept inference in FP32 because molecular dynamics is sensitive to small force errors. For PET, an MLIP from EPFL's Lab COSMO, BF16x9 on Blackwell GPUs and TF32x3 on Ampere GPUs decompose each FP32 matrix-multiply operand into lower-precision components evaluated on Tensor Cores, with no retraining or architecture change. On a 17,280-atom water box, BF16x9 sped PET's large GEMMs by 2.2–2.5x and the full MD step by up to 1.7x; TF32x3 sped major GEMMs on A100 by about 1.7–1.9x and the MD step by up to 1.5x, while keeping energy conservation and structural relaxation close to FP32.