VectorLiteRAG: Latency-Aware and Fine-Grained Resource Partitioning for Efficient RAG

Retrieval-Augmented Generation leverages vector similarity search to enhance large language models with up-to-date, external knowledge, enabling accurate and reliable responses. While CPU-only vector search incurs high latency on large, high-dimensional indices, co-locating the retriever and the LLM on the GPU leads to resource sharing that can create resource contention. Specifically, vector search is memory and I/O intensive, placing it in direct conflict with LLM inference, which demands memory for KV cache and compute for higher throughput. We present VectorLiterAG, a latency-aware RAG serving system that explicitly orchestrates data placement and execution across retrieval and inference to meet strict end-to-end SLOs. VectorLiterAG is driven by access-pattern analysis and performance estimation to regulate how retrieval variability can be mitigated and managed in the system with LLM inference, enabling SLO-compliant execution under skewed and dynamic workloads. By jointly modeling search latency and query hit-rate distributions, VectorLiterAG identifies an optimal index partitioning point across CPU and GPU that minimizes contention and stabilizes batching behavior, thereby maximizing sustained throughput under skewed access patterns. A low-overhead online index update mechanism allows VectorLiterag to continuously adapt to evolving request distributions, preserving batching efficiency and throughput as access patterns evolve. Our evaluations demonstrate that VecTORLITERAG consistently expands the range of SLO-compliant request rate across all tested configurations. Without increasing the generation latency or requiring additional hardware, VecTORLITERAG outperforms both naive and existing alternative frameworks, improving attainable SLO-bound throughput by up to$\mathbf{1. 5} \times$.