Point Edge Transformer
AI overview
Sign in and the AI will write an overview from our coverage.
Headlines · 1
- Tensor Core FP32 methods speed up MLIP molecular dynamics
NVIDIA Technical Blog reports that TF32x3 and BF16x9 Tensor Core methods accelerated PET machine learning interatomic potential workloads without retraining or changing model weights or architecture. On an A100, TF32x3 sped up major GEMMs by up to 1.9x and complete molecular dynamics steps by up to 1.5x, while BF16x9 on an NVIDIA B300 reached 2.5x and 1.7x speedups, respectively. The tests also examined force accuracy, energy conservation, structural relaxation, and numerical behavior close to FP32.
NVIDIA Technical Blog · 🔥 0
Experience and discussion from the community
Share my Point Edge Transformer experienceAsk about Point Edge Transformer
Nobody has shared their experience with Point Edge Transformer yet.