SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality
Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providing a viable alternative to Transformers. Building upon this, Mamba-2 introduces the Structured State Space Duality (SSD), which integrates recurrent and attention modes to achieve efficiency and scalability. However, this architectural expansion substantially increases memory and latency overhead, underscoring the need for efficient compression strategies tailored to SSD. In this work, we present SSDi8, the first post-training quantization framework specifically designed for SSD to maintain a persistent INT8 path. SSDi8 introduces a reformu...
| Authors | Hyunwoo Kim, Byoungchan Ko, Minseok Kang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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