GraphPTQ: Post-Training Quantization for Graph Models, Memory Savings and Generalization

Post-training quantization (PTQ) is essentially unexplored for graph models. Yet graph foundation models (GFMs) are scaling rapidly, straining memory budgets, while high-stakes deployment demands robustness to distribution shift. We address this with GraphPTQ, the first unified evaluation of how PTQ affects generalization in message passing models, graph transformers, and pretrained GFMs, featuring hardware-measured GPU peak memory. We evaluate eight models and eleven modes, spanning both weight-dominated and activation-dominated memory regimes across covariate shifts and zero-shot tasks. Our findings reveal clear memory-generalization tradeoffs: weight-only INT8 is the safe default for significant memory reduction, while under INT4 weights or activation quantization, out-of-distribution (OOD) accuracy responses become bidirectional and architecture-dependent. We further show that the in-distribution loss curvature along the rounding direction successfully predicts this OOD risk, with the predictive ordering strengthening as bit-width drops.