TF32x3 and BF16x9 speed up Point Edge Transformer MLIP inference while keeping FP32-like accuracy
Evaluation on the Point Edge Transformer (PET) from Lab COSMO (EPFL) shows NVIDIA’s BF16x9 on Blackwell GPUs speeds large GEMMs by 2.2–2.5x (17,280-atom) and 1.2–1.4x (192-atom), while TF32x3 on Ampere speeds major GEMMs 1.7–1.9x and full MD steps up to 1.5x; TF32x3 preserves potential-energy smoothness, energy conservation in NVE trajectories, and converges structural relaxation (539-atom Cantor-alloy slab) in 218–222 steps with ~1.5x per-step speedup, whereas one-pass TF32 fails to reach force tolerance after 1,000 steps. The post notes how to enable BF16x9 via cuBLAS and points to the NVIDIA ALCHEMI Toolkit and the UPET repository for integration and model checkpoints.