RAPIDS cuVS is a GPU-accelerated library designed for vector search and clustering, essential for data mining and artificial intelligence applications. Built on low-level CUDA primitives, cuVS offers optimized algorithms for approximate nearest neighbors and clustering, providing efficient handling of massive workloads.

Features

• Improved Performance: Provides higher throughput and lower latency for efficient index building and searching large vector spaces, significantly reducing inference time and cost compared to traditional CPU-based solutions.
• Flexible Integration: Supports multiple languages including C, C++, Python, and Rust, making it easy to integrate into vectorized data applications. Offers interoperability between CPU and GPU, enabling index building on a GPU and searching on a CPU.
• Advanced Algorithms: Includes advanced algorithms for approximate nearest neighbor search, performance-tuned for the latest compute architectures.
• Scalability: Enables databases to scale up and out for processing massive-scale vector search and clustering workloads with GPU acceleration.
• World's Fastest Vector Search: Achieves unmatched speed by delivering better index build times, higher throughput, and lower latency at every level of recall. Supports complex algorithms like IVF-PQ, IVF-flat, and CAGRA.

Benefits

• Optimized Performance: Delivers higher throughput and lower latency, enhancing the efficiency of vector search operations.
• Efficiency: Reduces inference time and cost, making it suitable for large-scale applications.
• Versatility: Suitable for a wide range of applications, including large language models, recommender systems, computer vision, and data mining.
• Scalability: Supports massive-scale vector search and clustering workloads, ensuring high performance and efficiency.
• Developer-Friendly: Provides comprehensive support for GPU-accelerated vector search workflows with rich APIs and reusable code.