Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models

• URL: http://arxiv.org/abs/2508.02128v1
• Date: Mon, 04 Aug 2025 07:22:36 GMT
• Title: Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
• Authors: Tai An, Ruwu Cai, Yanzhe Zhang, Yang Liu, Hao Chen, Pengcheng Xie, Sheng Chang, Yiwu Yao, Gongyi Wang,
• Abstract summary: We introduce Amber Pruner, a training-free N:M activation sparsity method designed specifically for the prefill stage.<n>Experiments show that Amber Pruner can effectively sparsify and accelerate more than 55% of linear computations without requiring model retraining.<n>We propose Outstanding-sparse, a unified framework that integrates Amber Pruner with post-training W8A8 quantization.
• Score: 10.779802084898206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focused on weight sparsity, it often suffers from significant accuracy degradation. Activation sparsity, though promising, is typically training-dependent and faces challenges in generalization. To address these limitations, we introduce Amber Pruner, a training-free N:M activation sparsity method designed specifically for the prefill stage, targeting the acceleration of linear projection layers in LLMs. Extensive experiments across multiple models and sparsity ratios (2:4, 4:8, and 8:16) demonstrate that Amber Pruner can effectively sparsify and accelerate more than 55% of linear computations without requiring model retraining. To further enhance generality and efficiency, we propose Outstanding-sparse, a unified framework that integrates Amber Pruner with post-training W8A8 quantization. Our approach preserves strong performance across a range of downstream tasks, with notable advantages in generative tasks. This work pioneers a new frontier in activation sparsity, providing foundational insights that are poised to guide the co-evolution of algorithms and architectures in the design of next-generation AI systems.

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