Graph Neural Networks (GNNs) commonly employ sampling-based methods for inference on large-scale real-world graphs. However, the inherent characteristics of sampling lead to redundant data loading during GNN inference, while slow data transfer between the host and GPU exacerbates the issues of slow inference and low resource utilization. Current methods to accelerate GNN inference face several challenges: (1) low GPU resource utilization; (2) neglect of adjacency matrix locality; and (3) long preprocessing time. To address these issues, we propose DCI, a system designed to accelerate GNN inference. The system provides a simple and effective cache capacity allocation and filling strategy that can adapt flexibly to different workload demands. During the pre-sampling phase, DCI allocates and fills cache capacities for node features and adjacency matrices based on workload patterns. Experimental results show that DCI accelerates sampling and node feature loading, achieving end-to-end inference speedups of 1.18 \(\times \) to 11.26 \(\times \) compared to DGL, and 1.14 \(\times \) to 13.68 \(\times \) compared to RAIN, while reducing preprocessing time by 52.8% to 98.7%. Additionally, DCI outperforms existing single-cache inference systems with speedups ranging from 1.08 \(\times \) to 1.32 \(\times \) . We also compared DCI with DUCATI’s dual-cache population strategy, and DCI achieves nearly identical inference speeds while reducing preprocessing time to less than 20% of DUCATI’s time.