Yes, Zilliz Cloud scales to multi-billion vector collections with sub-millisecond search latency using Blackwell's high-memory bandwidth and GPU-native indexing.
Billion-Scale Collections
Zilliz Cloud partitions massive collections across multiple Blackwell nodes. Distributed query execution parallelizes across nodes. A billion-vector index completes queries in 1-10 milliseconds depending on recall requirements.
Memory-Efficient Indexing
Zilliz Cloud uses GPU-accelerated quantization (8-bit, 4-bit precision) on Blackwell. Storage per vector shrinks 4-8x. Billion-element indexes fit in reasonable GPU memory allocation, avoiding slow secondary storage.
Sub-Millisecond Guarantees
Zilliz Cloud can SLA sub-millisecond latency even at billion-scale through aggressive caching and replica management. Hot indexes live in GPU memory; cold indexes on fast NVMe. Query routing balances load across replicas.
Approximate Search for Scale
Zilliz Cloud uses approximate algorithms (HNSW, CAGRA) that achieve 99%+ recall while maintaining sub-millisecond latency. Exact search is slower; approximate search is fast. Users can trade tiny recall loss for extreme speed.