Toward Disaggregated Analytical Database Systems in the AI Hardware Era

Abstract

The AI hardware boom has driven modern data centers toward HPC-style architectures centered on GPU clusters, RDMA-capable networks, and high-throughput NVMe storage. While designed primarily for training and inference, this infrastructure also creates new opportunities for designing the next generation of scalable database systems for analytical workloads on top of such data centers. In particular, the combination of GPU-centric computation, fast networking, and fast storage enables disaggregated architectures that extend beyond single-node, GPU-memory-resident execution. This paper discusses the challenges and design considerations of analytical query processing on such disaggregated GPU-centric systems. We examine how modern networks and storage enable distributed execution and out-of-memory processing, and how their interaction shapes end-to-end performance. Our recent results show that naïve use of existing I/O abstractions can underutilize both compute and I/O bandwidth due to insufficient overlap between computation and data movement. We therefore identify effective I/O-computation overlap as a key requirement for fully exploiting the AI data center stack, and outline future research directions for next-generation analytical database architectures.