Initially, KVTC employs principal component analysis (PCA) to organize KV cache features by significance. PCA is a statistical method commonly applied in machine learning to enhance efficiency by extracting the most vital data characteristics and eliminating duplicates. This stage occurs just once during a preliminary calibration period for each model. Since the PCA alignment matrix is precomputed and reused, it doesn't hinder compression speed during live inference for user prompts.
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