Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference
Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting informative slices along their contraction dimensions, without modifying model weights. Under a simple retention-ratio control, RMM provides a smooth and predictable accuracy-efficiency trade-off. Across language models ranging from 1B to 70B parameters, we find that reduction tolerance depends on the model family, task, component, and retention ratio, although it often improves with mode...
| Authors | Zixuan Lan, Yanhong Li, Jiawei Zhou |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 31 August 2026 |
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