PyTorch
3 of 4 posts
More performance checks of NVIDIA PyTorch on DGX Spark
Following up with fp32, fp16 and bf16 across GEMM, convolution and TorchBench: where the NGC container actually wins, and where the DGX Spark is simply bandwidth-bound.
FindingGEMM gains hold; convolution unchanged; bandwidth-bound
Is it worth using the official NVIDIA PyTorch image?
An 8192² bfloat16 matmul run 100 times, inside NVIDIA's NGC PyTorch container and against a stock pip install, measured on the same DGX Spark.
Finding+50% TFLOPS · 8192² bf16 matmul
Unsloth your DGX Spark
Getting Unsloth to build and run on the DGX Spark's GB10: the NGC PyTorch image, uv with pinned overrides, and the TORCH_CUDA_ARCH_LIST setting that makes xformers compile.
Findingxformers compiles against CUDA 12.1 once TORCH_CUDA_ARCH_LIST=12.0