Title: GCINT: Dynamic Quantization Algorithm for Training Graph Convolution Neural Networks Using Only Integers. Abstract: Quantization approaches can minimize storage costs while decreasing the computational complexity of a model, although there is minimal study in the GNN field on quantization networks. We studied the four primary reasons why existing quantization approaches cannot be employed extensively with GNNs: (1)Quantifying the distinctions between data sources; (2)Quantifying the distinctions between data streams; (3)Quantifying the distinctions between concentrations; (4)QAT’s Limitations. Based on this, we propose GCINT, which is an efficient quantization framework prepared for GNN training. The entire forward, backward, optimizer, and loss functions are calculated using integer data. We achieved a training acceleration ratio of nearly 10× compared to FP32 Cuda Core in RTX 2080TI INT8 Tensor Core. Our quantization is independent of the dataset and weight distribution, and more than 2,000 randomized trials have been undertaken on the 8 popular GNN benchmark datasets, with all achieving errors within 1% of the FP32.