Many inference systems leverage spatial multiplexing technologies, such as Multi-Process Service (MPS) and Multi-Instance GPU (MIG), to serve deep learning models concurrently on a single GPU. However, existing solutions suffer from interference under MPS and rigid partition sizes in MIG. To address these limitations, we propose BOER, a system that combines MPS atop MIG partitions to reduce interference and enhance GPU utilization. BOER identifies key challenges in integrating MPS with MIG and introduces a hierarchical scheduling framework that jointly determines model colocation, workload distribution, MIG partitioning, and MPS configurations, while minimizing resource fragmentation and MIG reconfiguration overhead. Since MPS interference is difficult to predict accurately, BOER avoids performance models and instead employs a Bayesian optimization with tailored acceleration strategies to efficiently explore the MPS configuration space. Evaluation on a real testbed demonstrates that BOER outperforms state-of-the-art spatial multiplexing solutions, improving inference throughput by up to 46.04%–77.19% while preserving Quality-of-Service requirements.