As the applications of AI proliferate, it is critical to increase the throughput of online DNN inference services. Multi-process service (MPS) improves the utilization rate of GPU resources by spatial-sharing, but it also brings unique challenges. First, interference between co-located DNN models deployed on the same GPU must be accurately modeled. Second, inference tasks arrive dynamically online, and each task needs to be served within a bounded time to meet the service-level objective (SLO). Third, the problem of fragments has become more serious. To address the above three challenges, we propose an Intelligent Scheduling orchestrator for multi-GPU inference servers with spatio-temporal Sharing (InSS), aiming to maximize the system throughput. InSS exploits two key innovations: i) An interference-aware latency analytical model which estimates the task latency. ii) A two-stage intelligent scheduler is tailored to jointly optimize the model placement, GPU resource allocation and adaptively decides batch size by coupling the latency analytical model. Our prototype implementation on four NVIDIA A100 GPUs shows that InSS can improve the throughput by up to 86% compared to the state-of-the-art GPU schedulers, while satisfying SLOs. We further show the scalability of InSS on 64 GPUs.