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arXiv · 2507.03117

BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers

Patrik Okanovic·Sameer Deshmukh·Grzegorz Kwasniewski·Yi Zhu·Haruto Fujii·Sakina Fatima·Maciej Besta·Kentaro Katayama·Takumi Honda·Yusuke Nagasaka·Torsten Hoefler
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Abstract

The energy consumption of large-scale ML models is dominated by data movement, shuffling billions of parameters across memory hierarchies and data centers. Sparsification offers a principled way to mitigate these costs by pruning redundant weights and activations, thereby reducing data movement. Effective sparsification to prune redundant parameters is still challenging: existing methods incur significant accuracy degradation, performance overhead, or both. We introduce (Bl)ock (a)nd (S)parse (T)ransformers (BLaST), a general, robust, and reliable method for sparsification, applicable to linear layers in all settings. Our method iteratively sparsifies weight matrices into a block sparsity pattern suitable for efficient sparse matrix-matrix (SpMM) multiplication. BLaST achieves up to 95% sparsity in MLP weights with negligible accuracy loss (majority <2.25%). We show a 2.2x inference speedup for Llama 3.2 with 16 GPUs, and up to 4.45x reduction in inference memory footprint resulting in a 2.9x reduction in GPU setup and operating costs.

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Patrik Okanovic, Sameer Deshmukh, Grzegorz Kwasniewski, Yi Zhu, Haruto Fujii, Sakina Fatima, Maciej Besta, Kentaro Katayama, Takumi Honda, Yusuke Nagasaka, Torsten Hoefler. 2025-07-03. BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers. https://arxiv.org/abs/2507.03117

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