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[2509.22166] Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs
Summary
This article explores lightweight error mitigation strategies for post-training N:M activation sparsity in large language models (LLMs), demonstrating their effectiveness in preserving generative capabilities during pruning.
Why It Matters
As the demand for efficient LLM inference grows, this research highlights innovative methods for activation pruning that can enhance model performance while reducing computational overhead. The findings may influence future hardware designs and optimization strategies in AI.
Key Takeaways
- Activation pruning can preserve generative capabilities better than weight pruning at similar sparsity levels.
- Lightweight error mitigation techniques are effective and require minimal calibration.
- The study identifies the 8:16 sparsity pattern as a superior candidate for practical applications.
Computer Science > Machine Learning arXiv:2509.22166 (cs) [Submitted on 26 Sep 2025 (v1), last revised 21 Feb 2026 (this version, v2)] Title:Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs Authors:Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva, Alina Kostromina, Vladimir Smirnov, Redko Dmitry, Alexey Dontsov, Maxim Zhelnin, Evgeny Burnaev, Egor Shvetsov View a PDF of the paper titled Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs, by Shirin Alanova and 9 other authors View PDF HTML (experimental) Abstract:The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for weights, its application to activation pruning remains underexplored despite its potential for dynamic, input-adaptive compression and reductions in I/O overhead. This work presents a comprehensive analysis of methods for post-training N:M activation pruning in LLMs. Across multiple LLMs, we demonstrate that pruning activations enables superior preservation of generative capabilities compared to weight pruning at equivalent sparsity levels. We evaluate lightweight, plug-and-play error mitigation techniques and pruning criteria, establishing strong hardware-friendly baselines that require minimal calibration. Furthermore, we explore sparsity patterns beyond NVIDIA's standard 2:4, showing that the 16:32 patte...
