DGX Spark
4 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
Oh no, 128 GB is not enough for 7B parameters!
A 7B model OOMs on a 128 GB machine. The memory is not missing — the OS page cache is holding it, and unified memory hides that from the usual GPU tooling.
FindingOS page cache, not the model, causes the OOM
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