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arXiv cs.LG
arXiv cs.LG
7/13/2026
Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices

Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices

Short summary

Researchers propose a three-layer GPU matrix storage format enabling sparse tensor cores and CUDA cores to jointly accelerate sparse matrix multiplication for moderately pruned LLMs. Their SpMM kernel is the first to outperform dense multiplication on HBM-equipped GPUs, achieving up to 1.64x kernel-level speedup over SpInfer and 1.41x end-to-end over FlashLLM. Source code is publicly available.

  • •Three-layer sparse matrix format enables first GPU SpMM kernel to beat dense multiplication on HBM GPUs
  • •Up to 1.64x kernel speedup over SpInfer and 1.41x end-to-end over FlashLLM
  • •Open-source implementation available on GitHub

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Read full article at arXiv cs.LG

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