Author

R. Saab

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Preprint Aug 2026

FastKron: Efficient Quantization with Kronecker-Factored Hessians

We accelerate a family of algorithms for neural network quantization which utilize a two-sided version of the GPTQ/LDLQ algorithm. Standard GPTQ-style adaptive rounding uses one-sided correlation information derived from input activations. A natural two-sided extension can additionally capture correlations across outpu...

Johann Birnick, R. Saab · 1 citation

Preprint Aug 2026

BaKron: Efficient Quantization with Kronecker-Factored Hessians

BaKron is an efficient solver that combines anti-diagonal parallelism with a recursive divide-and-conquer construction that matches the cubic scaling of GPTQ while exploiting richer curvature information.

Johann Birnick, R. Saab · 2 citations