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arXiv · 2502.18886

On Pruning State-Space LLMs

Tamer Ghattas·Michael Hassid·Roy Schwartz
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Abstract

Recent work proposed state-space models (SSMs) as an efficient alternative to transformer-based LLMs. Can these models be pruned to further reduce their computation costs? We adapt several pruning methods to the SSM structure, and apply them to four SSM-based LLMs across multiple tasks. We find that such models are quite robust to some pruning methods (e.g. WANDA), while using other methods lead to fast performance degradation.

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Tamer Ghattas, Michael Hassid, Roy Schwartz. 2025-10-05. On Pruning State-Space LLMs. https://arxiv.org/abs/2502.18886

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