Matrix Multiplication Background Users Guide - NVIDIA Docs Performance improves as the M-N footprint of the GEMM increases Duration also increases, but not as quickly as the M-N dimensions themselves; it is sometimes possible to increase the GEMM size (use more weights) for only a small increase in duration
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GitHub - deepseek-ai DeepGEMM: DeepGEMM: clean and efficient FP8 GEMM . . . By providing a mask tensor, the kernel computes only the valid portions Use m_grouped_fp8_gemm_nt_masked for this purpose and consult the relevant documentation An example usage is to use the output of low-latency kernels from DeepEP as input
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General Matrix Multiply (GeMM) - Spatial General Matrix Multiply (GEMM) is a common algorithm in linear algebra, machine learning, statistics, and many other domains It provides a more interesting trade-off space than the previous tutorial, as there are many ways to break up the computation
Why GEMM is at the heart of deep learning - Pete Wardens blog This paper from Nvidia is a good introduction to some of the different approaches you can use, but they also describe why they ended up with a modified version of GEMM as their favored approach