#linear-attention

Linear Attention

2 articles

arXivLLM

STEPQuant: Spatial-Temporal Quantization for Linear Attention Recurrent States

STEPQuant：空間與時間雙重優化，突破線性注意力循環狀態量化瓶頸

STEPQuant is a spatial-temporal post-training quantization framework for Delta-rule recurrent states, achieving over 5x state compression and reducing serving memory by up to 68.7% with minimal accuracy loss.

Sep 30, 20262 min read

arXivLLM

LeapQuant: Near-Lossless 8-Bit Recurrent State Quantization for Linear Attention LLMs

LeapQuant：突破線性注意力瓶頸，實現近乎無損的 8-bit 遞迴狀態量化

LeapQuant is a training-free quantization method that achieves near-lossless 8-bit recurrent state quantization for linear attention, delivering up to 1.47x end-to-end inference speedup while preserving FP32 accuracy.

Sep 30, 20262 min read