Instructions to use replicate/trimul_gpumode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use replicate/trimul_gpumode with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("replicate/trimul_gpumode") - Notebooks
- Google Colab
- Kaggle
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Download README.md from replicate/trimul_gpumode: direct link, hf CLI and curl.
- Browser
- Download file 897 Bytes
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https://huggingface.co/replicate/trimul_gpumode/resolve/main/README.md
- Command line
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hf download hf://replicate/trimul_gpumode/README.md
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curl -L -o README.md https://huggingface.co/replicate/trimul_gpumode/resolve/main/README.md
897 Bytes
| tags: | |
| - kernels | |
| - universal | |
| > [!CAUTION] | |
| > Starting from September 13, 2026, we will be removing the "model" type repositories of kernels (e.g., kernels-community/flash-attn3). Make sure you're using a latest version of kernels. If you face any disruption, please report them here: https://github.com/huggingface/kernels/issues/new. | |
| These are kernels from the [GPUMODE](https://www.gpumode.com/v2/news) trimul competition written in Triton | |
| The `kernel_mi300` ranked first for MI300, `kernel_a100` ranked first for A100, `kernel_b100` ranked second for B200, and `kernel_h100` ranked third for H100. These kernels are copied from the [trimul](https://github.com/arseniivanov/trimul) repository. | |
| The `kernel_global` has the highest average ranking across all architectures, and was copied and adapted from the [gpumode-trimul](https://github.com/davidberard98/gpumode-trimul) repository. |