Solid models traditionally use boundary-representation (B-rep) to define and model their geometry. However, performing modeling operations such as Boolean operations or computing point membership classification with B-rep is computationally intensive, since B-reps do not have volumetric information. Voxelized representations, on the other hand, can be extended to include volumetric information of solid models. However, in order to use voxelized representations for solid modeling, efficient methods for voxelizing a B-rep solid model needs to be developed. In this thesis, GPU-accelerated methods are presented for creating and rendering a multi-level voxelization of a solid model that can be used along with the existing B-rep for modeling operations. Two GPU-accelerated algorithms are described; one for creating a multi-level voxelization given a B-rep of a solid model and another for ray casting to render the multi-level voxelization of the solid model. Compact and flat data structures are described that can be used to store the multi-level voxelization data and can be efficiently retrieved in parallel using GPU-algorithms for rendering and modeling operations. The GPU-accelerated multi-level voxelization method can generate models with an effective voxel count of up to 8 billion voxels. In addition, the GPU voxelization algorithm is more than 40x faster than the CPU implementation in generating the voxelization. Finally, we outline a few applications for the hybrid representation, which include fast point-membership classification, volume computation, and collision detection.