PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Thu, 02 Jul 2026 00:00:00 +0000

Modern sequence models are becoming increasingly powerful, but their computational cost, memory behavior, and deployment efficiency remain important challenges. This is especially relevant for emerging architectures such as Mamba and selective state space models, which have been proposed as efficient alternatives to Transformer-based sequence models.

Our paper, “PerfMamba: Performance Analysis and Pruning of Selective State Space Models,” presents an empirical study of Mamba-1 and Mamba-2. The work analyzes runtime behavior, resource utilization, computation patterns, memory access, I/O characteristics, and scaling properties across sequence lengths from 64 to 16,384 tokens.

The study shows how different components of selective state space models contribute to performance and efficiency. In particular, the paper examines the SSM component, which plays a central role in Mamba-style architectures, and identifies opportunities for optimization through pruning.

Based on the profiling results, PerfMamba explores pruning low-activity states in the SSM component to improve throughput and reduce memory usage while maintaining model accuracy under moderate pruning.

This work contributes to research on Mamba, selective state space models, performance analysis, runtime profiling, model pruning, model compression, efficient AI, high-performance computing, and hardware-aware optimization.

Researchers working on large language models, sequence modeling, AI systems, neural network acceleration, scalable machine learning, and efficient deep learning may find this work useful.

Paper

PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu, Juan Pablo Muñoz, Ali Jannesari

Published in: International Symposium on Benchmarking, Measuring and Optimization
Publisher: Springer, Singapore
Year: 2026
Pages: 27–44

Paper page: https://mobinakashaniyan.github.io/publications/perfmamba/

PDF: https://mobinakashaniyan.github.io/papers/perfmamba-performance-analysis-pruning-selective-state-space-models.pdf

Keywords

PerfMamba, Mamba, Mamba-1, Mamba-2, selective state space models, state space models, SSM, sequence modeling, Transformer alternatives, performance analysis, runtime profiling, benchmarking, resource utilization, memory access patterns, I/O characteristics, scaling analysis, model pruning, state pruning, model compression, efficient AI, high-performance computing, hardware-aware optimization, large language models, AI systems, neural network acceleration.

Citation

Al Asif, A., Kashaniyan, M., Yu, S., Muñoz, J. P., & Jannesari, A. (2026). PerfMamba: Performance analysis and pruning of selective state space models. In International Symposium on Benchmarking, Measuring and Optimization (pp. 27–44). Springer, Singapore.

PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Thu, 01 Jan 2026 00:00:00 +0000

This paper contributes to research on PerfMamba, Mamba-1, Mamba-2, selective state space models, performance analysis, model pruning, benchmarking, efficient AI, and high-performance computing by providing a systematic empirical study of the runtime behavior and optimization opportunities of Mamba-style architectures.

Recent sequence modeling research has introduced selective state space models as efficient alternatives to Transformer architectures. However, their real-world performance behavior, memory access patterns, I/O characteristics, resource utilization, and scaling properties require deeper analysis for effective deployment.

PerfMamba profiles Mamba-1 and Mamba-2 across sequence lengths from 64 to 16,384 tokens. The study analyzes computation patterns, memory behavior, I/O characteristics, and scaling trends to identify the components that dominate runtime and resource usage.

Based on these insights, the paper proposes a pruning technique that removes low-activity states within the SSM component. This supports improved throughput and reduced memory usage while maintaining accuracy under moderate pruning.

This work is relevant to researchers working on Mamba, selective state space models, state space models, large language models, sequence modeling, model pruning, model compression, runtime profiling, AI systems, neural network acceleration, and hardware-aware optimization.

Keywords: PerfMamba, Mamba, Mamba-1, Mamba-2, selective state space models, state space models, SSM, sequence modeling, Transformer alternatives, performance analysis, benchmarking, runtime profiling, resource utilization, memory access patterns, I/O characteristics, scaling analysis, model pruning, state pruning, model compression, efficient AI, high-performance computing, hardware-aware optimization, large language models, AI systems.

Citation:

Al Asif, A., Kashaniyan, M., Yu, S., Muñoz, J. P., & Jannesari, A. (2026). PerfMamba: Performance analysis and pruning of selective state space models. In International Symposium on Benchmarking, Measuring and Optimization (pp. 27–44). Springer, Singapore.

Efficient AI | Mobina Kashaniyan

PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Paper

Keywords

Citation

PerfMamba: Performance Analysis and Pruning of Selective State Space Models