Existing methods ā GPTQ, AWQ, llama.cpp's k-quants ā minimize empirical loss heuristically. None of them prove they are optimal in any information-theoretic sense. ICRB-Q builds a quantization scheme that is provably optimal via the CramĆ©r-Rao lower bound (CRB): no unbiased estimator of a weight can have lower variance than [F(Īø)]ā»Ā¹, where F is the Fisher information matrix.