Graphics processing unit (GPU) databases have attracted a lot of attention from the academic and industrial communities in recent years. Although quite a few prototype systems and commercial systems (including open-source systems) have been developed as next-generation database systems, whether GPU-based online analytical processing (OLAP) engines really outperform central processing unit (CPU)-based systems is still in doubt. If they do, more in-depth research should be conducted on what kind of workload/data/query processing models are more appropriate. GPU-based OLAP engines have two major technical roadmaps: GPU in-memory processing mode and GPU-accelerated mode. The former stores all the datasets in the GPU device memory to take the best advantage of GPU’s computing power and high bandwidth memory. Its drawbacks are that the limited capacity of the GPU device memory restricts the dataset size and that memory-resident data in the sparse access mode reduces the storage efficiency of the GPU display memory. The latter only stores some datasets in the GPU device memory and accelerates computation-intensive workloads by GPU to support large datasets. The key challenges are how to choose the optimal data distribution and workload distribution models for the GPU device memory to minimize peripheral component interconnect express (PCIe) transfer overhead and maximize GPU’s computation efficiency. This study focuses on how to integrate these two technical roadmaps into the accelerated OLAP engine and proposes OLAP Accelerator as a customized OLAP framework for hybrid CPU-GPU platforms. In addition, this study designs three calculation models, namely, the CPU in-memory calculation model, the GPU in-memory calculation model, and the GPU-accelerated model for OLAP, and proposes a vectorized query processing technique for the GPU platform to optimize device memory utilization and query performance. Furthermore, the different technical roadmaps of GPU databases and corresponding performance characteristics are explored. The experimental results show that the vectorized query processing model based on GPU in-memory achieves the best performance and memory efficiency. The performance is 3.1 and 4.2 times faster than that achieved with the datasets OmniSciDB and Hyper, respectively. The partition-based GPU-accelerated mode only accelerates the join workloads to balance the workloads between the CPU and GPU ends and can support larger datasets than those the GPU in-memory mode can support.