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    2. Vector Database
    3. VAST AI OS

    VAST AI OS

    GPU-accelerated platform from VAST Data that includes a native vector database, designed for enterprise AI workloads including multi-agent systems, video-reasoning, and high-volume RAG. It combines vector embeddings with structured data and metadata in unified tables, enabling hybrid queries across modalities without orchestration layers or external indexes.

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    Information

    Websitewww.vastdata.com
    PublishedApr 4, 2026

    Categories

    1 Item
    Vector Database

    Tags

    3 Items
    #GPU-accelerated#enterprise#hybrid-search

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    VAST CNode-X

    GPU-accelerated server from VAST Data that combines the VAST AI OS with NVIDIA data-processing libraries and onboard NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Designed for enterprise AI workloads requiring high-throughput vector search, data vectorization, and inference, it leverages the NVIDIA AI Data Platform reference design.

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    Overview

    VAST AI OS is a GPU-accelerated data platform that includes a native vector database as a core capability of the broader VAST DataBase. It is designed for demanding enterprise AI workloads such as multi-agent systems, video-reasoning, and high-volume RAG, which require real-time processing, high throughput, complex queries, and extremely low latency.

    The system combines the VAST AI OS with NVIDIA data-processing libraries (cuVS, cuDF) and onboard NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on the VAST CNode-X server, leveraging the NVIDIA AI Data Platform reference design.

    Architecture

    • Vector embeddings are stored alongside structured data and metadata in the same tables
    • Natively integrated with unstructured data in the VAST DataStore
    • Enables hybrid queries across modalities without orchestration layers or external indexes
    • Eliminates the need for separate CPU resources dedicated to indexing

    Performance Capabilities

    • Sustained ingestion rate of 1 million vectors/second
    • Benchmarked at very high throughput, performance, and recall across 50 billion vectors
    • Indexing lifecycle (read 20% sample → K-means clustering → vector assignment → read/write) is 4.5x faster on GPU vs CPU-only approach
    • K-means clustering and assignment phases especially benefit from GPU processing, producing more balanced clusters with fewer cycles
    • A 10-hour CPU indexing process can execute in ~2.5 hours on GPU

    Use Cases

    • Real-time RAG pipelines with near real-time ingestion and indexing
    • Recommendation systems requiring high speed and accuracy
    • Video-reasoning over public safety streams
    • Finance, cybersecurity, genomics/life sciences, retail, and chemistry applications
    • Multi-agent concurrent SQL query execution

    GPU Acceleration

    • Uses NVIDIA cuVS as the GPU backend for vector search
    • Uses NVIDIA cuDF for GPU-accelerated data processing (Spark, pandas, Polars)
    • Eliminates the need for expensive memory-based indexing and third-party vector databases
    • Reduces indexing time by 4.5x compared to CPU-only FAISS approach

    Sirius SQL Engine

    VAST's native SQL engine leverages the Sirius library to build GPU-accelerated operators for fast SQL-query execution. The Sirius database is built on NVIDIA cuDF, is natively compatible with DuckDB, and has shown up to 20x speedup over CPU-based DuckDB on NVIDIA RTX PRO 6000 GPUs.

    Pricing

    Commercial product from VAST Data. Contact VAST Data for pricing details.