Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices
Pruning techniques that introduce sparsity into weight matrices can accelerate inference. However, maintaining model quality typically limits pruning to moderate unstructured sparsity (around 50\%). At these sparsity levels, none of the existing GPU kernels for sparse matrix multiplication (SpMM) can outperform their dense counterparts.
- ▪Pruning techniques that introduce sparsity into weight matrices can accelerate inference.
- ▪However, maintaining model quality typically limits pruning to moderate unstructured sparsity (around 50\%).
- ▪At these sparsity levels, none of the existing GPU kernels for sparse matrix multiplication (SpMM) can outperform their dense counterparts.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.08786 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
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Computer Science > Machine Learning arXiv:2607.08786 (cs) [Submitted on 13 Jun 2026] Title:Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices Authors:Tao Lu, Haoyu Wang, Zonghui Wang, Keshen Xiang, Jiaheng Zhang, Wenzhi Chen View a PDF of the paper titled Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices, by Tao Lu and 5 other authors View PDF HTML (experimental) Abstract:With the growing deployment of large language models (LLMs), LLM inference cost has become a key challenge. Pruning techniques that introduce sparsity into weight matrices can accelerate inference. However, maintaining model quality typically limits pruning to moderate unstructured sparsity (around 50\%).
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