How GPU acceleration builds billion-scale vector indexes on Amazon OpenSearch Service

This article explains how GPU acceleration builds billion-scale vector indexes on Amazon OpenSearch Service, enabling faster index construction without sacrificing query performance.

• GPU workers offload vector index building using NVIDIA cuVS, converting GPU-built CAGRA graphs to CPU-searchable HNSW indexes
• Decoupled architecture separates indexing from search: CPU nodes ingest and serve queries while GPU workers build indexes via Amazon S3
• Automatic scaling of GPU workers based on pending jobs; pay only for active builds with no standing infrastructure cost
• Successfully indexed one billion 1024-dimensional vectors in 274 minutes with 0.93 recall and sub-100ms search latency
• Best practices include disabling refresh during bulk ingestion, using memory-optimized instances, and monitoring via CloudWatch metrics
• Supports quantization (2×, 8×, 16×, 32×) and out-of-core construction to handle datasets larger than GPU memory

GPU-accelerated indexing enables organizations to adopt new embedding models faster, handle bursty writes, and right-size clusters for mixed workloads while maintaining search quality.