Modern search demands high-performance vector indexing and scalability to keep pace with the rapid growth of generative AI applications. As datasets grow into the billions, traditional CPU-based indexing often becomes a bottleneck, stalling productivity and innovation velocity.
With GPU-accelerated vector (k-NN) indexing now available on Amazon OpenSearch Service and Amazon OpenSearch Serverless, you can scale to billions of vectors efficiently. Powered by NVIDIA cuVS, an open-source library for GPU-accelerated vector search, this capability offloads compute-intensive vector index building to specialized GPU workers while your existing CPU infrastructure continues serving search. The result is faster, more cost-efficient construction of large-scale vector indexes without sacrificing query performance.
Our earlier post went into those performance and cost benefits in detail. This post goes a level deeper into how the capability works. We walk through the…