Quantising weights to 4 bits hurts most where the inputs are large: a few activation channels carry outliers. AWQ (Lin et al., 2024) scales those weight columns up before quantising and divides the activations by the same factor, which is mathematically a no-op in full precision but protects the important weights from rounding error.
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