As vector databases and retrieval-augmented generation (RAG) systems scale, engineering teams must make critical infrastructure decisions—chief among them: should vector search run on CPUs or GPUs?
This talk breaks down the cost-performance trade-offs, architecture differences, and practical considerations when deploying vector search systems at scale. Using real benchmarks and deployment experiences from production systems, we’ll explore how to choose the right hardware depending on query latency targets, QPS (queries per second), dataset size, and budget.
You’ll walk away with a framework to guide hardware decisions based on your use case—from lightweight semantic search on CPUs to ultra-fast multimodal search on GPUs.