AINeutralarXiv – CS AI · 10h ago6/10
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Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Researchers provide the first rigorous theoretical analysis of OPTQ (GPTQ), a widely-used post-training quantization algorithm for neural networks and LLMs, establishing quantitative error bounds and validating practical design choices. The study extends theoretical guarantees to both deterministic and stochastic variants of OPTQ and the Qronos algorithm, offering guidance for regularization parameter selection and quantization alphabet sizing.