Scale Sensitivity in Low-Bit Post-Training Quantization: Curvature of the Quantization Error Landscape
Post-training quantization (PTQ) methods in the GPTQ family minimize a layer-wise reconstruction error on a uniform grid whose scale must be chosen; the common max-based choice degrades sharply at low bit-widths. We study how sensitive this objective is to the scale. For a layer with i.i.d. Gaussian weights and calibra...
Jonas von Berg, Massimiliano Datres, Carlo Kneissl et al.
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