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[2507.12442] Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Summary
This article explores the performance of State Space Models (SSMs) and hybrid language models in processing long-context inputs, highlighting their advantages over traditional Transformer models in specific applications.
Why It Matters
As applications like augmented reality demand efficient processing of long-context data, understanding the performance of emerging models like SSMs is crucial. This research provides insights into their computational efficiency and potential for on-device AI, which can influence future AI architecture developments and optimizations.
Key Takeaways
- SSMs and hybrid models offer near-linear scaling for long-context processing.
- While Transformers excel at short sequences, SSMs significantly outperform them at long contexts.
- Custom SSM kernels can dominate inference runtime, highlighting the need for hardware-aware optimizations.
- The study provides a framework for benchmarking these models on consumer and embedded GPUs.
- Open-sourcing the characterization framework encourages further research in this area.
Computer Science > Hardware Architecture arXiv:2507.12442 (cs) [Submitted on 16 Jul 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:Characterizing State Space Model and Hybrid Language Model Performance with Long Context Authors:Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon View a PDF of the paper titled Characterizing State Space Model and Hybrid Language Model Performance with Long Context, by Saptarshi Mitra and 4 other authors View PDF HTML (experimental) Abstract:Emerging applications such as AR are driving demands for machine intelligence capable of processing continuous and/or long-context inputs on local devices. However, currently dominant models based on Transformer architecture suffers from the quadratic computational and memory overhead, which hinders applications required to process long contexts. This has spurred a paradigm shift towards new architectures like State Space Models (SSMs) and SSM-Transformer hybrid models, which provide near-linear scaling. The near-linear scaling enabled efficient handling of millions of tokens while delivering high performance in recent studies. Although such works present promising results, their workload characteristics in terms of computational performance and hardware resource requirements are not yet thoroughly explored, which limits our understanding of their implications to the system level optimizations. To address this gap, we present a comprehensive, compara-ive benchmarking of...