Modern AI applications such as semantic search, RAG pipelines, and recommendation systems rely on large-scale vector search across millions to billions of embeddings. As datasets grow, CPU-only OpenSearch clusters struggle with slow vector indexing, rising query latency, and increasing infrastructure costs, making production-grade AI search difficult to operate reliably.

This talk explores how GPU-accelerated vector search transforms OpenSearch into a scalable platform for modern AI workloads. By offloading compute-intensive tasks such as vector index construction and similarity search from CPUs to GPUs, OpenSearch achieves faster indexing, lower query latency, and predictable performance at scale.

Attendees will learn how GPU acceleration can reduce index build times from hours to minutes, increase search throughput, and support large-scale embedding experimentation without impacting production stability.