SVD Contextual Sparsity Predictors for Fast LLM Inference
Contextual sparsity is one of the approaches used to reduce computational complexity in the inference process of large language models (LLMs). Existing techniques for efficient LLM inference acceleration based on contextual sparsity with minimal accuracy degradation require training sparse pattern predictors. This paper presents a framework for accelerating inference of ReGLU-based feed-forward networks (FFNs) within LLMs. The proposed framework provides a fast, training-free method for building sparse pattern predictors using truncation-aware singular value decomposition (SVD) of the gate projection matrix, along with a threshold calibration algorithm, and inference executors supporting ...
| Authors | Georgii Serbin, Kirill Koshkin, Zhongao Sun et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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