RotateK uses online PCA-based rotation to align token-dependent key channel importance into a shared subspace, enabling accurate head-wise structured pruning and faster decoding in VLMs compared to prior token or channel methods.
Duquant: Distributing outliers via dual transformation makes stronger quantized llms
2 Pith papers cite this work. Polarity classification is still indexing.
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TAH-Quant introduces tile-wise adaptive Hadamard quantization for activations in pipeline parallelism, achieving 3-4 bit compression with up to 4.3x throughput speedup and O(1/sqrt(T)) convergence matching SGD.
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Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference
RotateK uses online PCA-based rotation to align token-dependent key channel importance into a shared subspace, enabling accurate head-wise structured pruning and faster decoding in VLMs compared to prior token or channel methods.
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TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network
TAH-Quant introduces tile-wise adaptive Hadamard quantization for activations in pipeline parallelism, achieving 3-4 bit compression with up to 4.3x throughput speedup and O(1/sqrt(T)) convergence matching SGD.