Traditional databases have focused on the issue of reducing I/O cost as it is the bottleneck in many operations. As databases become increasingly accepted in areas such as Geographic Information Systems (GIS) and Bio-informatics, commercial DBMS need to support data types for complex data such as spatial geometries and protein structures. These non-conventional data types and their associated operations present new challenges. In particular, the computational cost of some spatial operations can be orders of magnitude higher than the I/O cost. In order to improve the performance of spatial query processing, innovative solutions for reducing this computational cost are beginning to emerge. Recent literature propose using hardware acceleration to reduce the computational cost of spatial operations. However, these proposals are preliminary in that they establish the feasibility of their approaches in a stand-alone setting but not in a real-world commercial database. In this paper we present an architecture to show how hardware acceleration of an off-the-shelf graphics card can be integrated into a popular commercial database to speed up spatial queries. Extensive experimentation with real-world datasets shows that significant improvement in the performance of spatial operations can be achieved with this integration. The viability of this approach underscores the significance of a tighter integration of hardware acceleration into commercial databases for spatial applications.