cuBLAS

Headlines · 2

1. 2d
   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
2. Oct 24
   Developer reports llama.cpp gains on legacy NVIDIA GPUs

   A developer reported improvements to llama.cpp for older NVIDIA GPUs, particularly Kepler and other pre-Ampere architectures. The changes include fixes to cuBLAS compute-type selection that let some hardware that previously failed during initialization run, along with CUDA graphs and Windows NCCL integration. In preliminary testing with MTP Qwen3.6-35B-A3B at Q8_0 quantization on four Tesla K40c GPUs, generation speed rose from about 35 to 55 tokens per second after enabling CUDA graphs under the same reported clock configuration. The developer also reported a 5–10% end-to-end improvement with Windows NCCL, throughput above 60 tokens per second, and prompt-processing speeds ranging from 70 to 100 tokens per second. These were preliminary results from the developer’s own system, not universal benchmarks.

   r/LocalLLaMA · 🔥 0

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