Vector similarity search plays a pivotal role in modern applications, including recommendation systems, image search, large language models (LLMs), and high-dimensional data retrieval. As data size scales, our research reveals that the search phase imposes substantial demands on DRAM bandwidth, leading to performance limitations in conventional von Neumann architecture with shared memory buses. This data movement bottleneck restricts the efficiency and scalability of vector similarity search due to insufficient memory bandwidth. To mitigate this issue, we leverage UPMEM, an off-the-shelf near-memory processing (NMP) system, to minimize the data movement between memory and compute units. However, UPMEM’s computing engine has certain limitations and requires thorough application integration to unleash its high-parallelism capabilities. In this work, we introduce UPMEM-aware Vector Similarity Search (UPVSS), an architecture-aware system that jointly manages vector similarity search and UPMEM’s NMP technology. UPVSS prioritizes offloading operations based on their strengths and capabilities, effectively alleviating the data movement bottleneck and improving overall system performance.