Modern high-performance computing (HPC) increasingly relies on GPUs, but integrating GPU acceleration into complex scientific frameworks like OpenFOAM remains a challenge. Existing approaches either fully refactor the codebase or use plugin-based GPU solvers, each facing trade-offs between performance and development effort. In this work, we address the limitations of plugin-based GPU acceleration in OpenFOAM by proposing a repartitioning strategy that better balances CPU matrix assembly and GPU-based linear solves. We present a detailed computational model, describe a novel matrix repartitioning and update procedure, and evaluate its performance on large-scale CFD simulations. Our results show that the proposed method significantly mitigates oversubscription issues, improving solver performance and resource utilization in heterogeneous CPU-GPU environments.