PPCC: Price-Preserving Calibration Coresets for Post-Training Quantization

Calibration coreset selection is essential for efficient post-training quantization (PTQ) of large language models, as limited calibration data must support reliable layer-wise choices of bitwidths, quantization granularity, and pre-quantization transformations. However, existing criteria based on data diversity or activation coverage do not directly characterize how calibration errors affect these decisions. To address this, we propose Price-Preserving Calibration Coresets (), a framework that reinterprets calibration selection through the preservation of pairwise quantization price gaps. This perspective decomposes price-gap distortion into distribution shift, finite-pool estimation, and coreset compression, where the compression term, which coreset selection controls, motivates a price-weighted moment-matching objective. Building on this foundation, inspired by traditional herding algorithms, we introduce an efficient greedy algorithm with a correction term derived by minimizing the expected terminal discrepancy. Theoretically, we establish finite-pool concentration bounds and prove that the resulting price-weighted squared moment discrepancy is no larger than its expectation under uniform random selection at the same budget. Empirical results show that recovers the full-pool price minimizer at all three tested budgets on Llama-3.2-1B and improves average downstream task scores in most configuration selection settings, with perplexity reductions of up to $35.2\%$ over the best baseline on Llama-3.1-8B.