PerfMamba: Performance Analysis and Pruning of Selective State Space Models

• URL: http://arxiv.org/abs/2511.22849v1
• Date: Fri, 28 Nov 2025 03:09:15 GMT
• Title: PerfMamba: Performance Analysis and Pruning of Selective State Space Models
• Authors: Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu, Juan Pablo Muñoz, Ali Jannesari,
• Abstract summary: We study Mamba-1 and Mamba-2 to assess the design principles that contribute to their efficiency in state-space modeling.<n>We propose a pruning technique that removes low-activity states within the SSM component, achieving measurable throughput and memory gains.<n>This approach results in performance improvements across varying sequence lengths, achieving a 1.14x speedup and reducing memory usage by 11.50%.
• Score: 10.047668792033033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequence processing advantages. A comprehensive understanding of selective SSMs in runtime behavior, resource utilization patterns, and scaling characteristics still remains unexplored, thus obstructing their optimal deployment and further architectural improvements. This paper presents a thorough empirical study of Mamba-1 and Mamba-2, systematically profiled for performance to assess the design principles that contribute to their efficiency in state-space modeling. A detailed analysis of computation patterns, memory access, I/O characteristics, and scaling properties was performed for sequence lengths ranging from 64 to 16384 tokens. Our findings show that the SSM component, a central part of the selective SSM architecture, demands a significant portion of computational resources compared to other components in the Mamba block. Based on these insights, we propose a pruning technique that selectively removes low-activity states within the SSM component, achieving measurable throughput and memory gains while maintaining accuracy within a moderate pruning regime. This approach results in performance improvements across varying sequence lengths, achieving a 1.14x speedup and reducing memory usage by 11.50\%. These results offer valuable guidance for designing more efficient SSM architectures that can be applied to a wide range of real-world applications.

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