AMD: hipVS — GPU vector search for Instinct, cuVS-compatible, with agentic RAG demo

What are vector search and RAG?

Vector search is a technique for finding the most similar vectors in a high-dimensional space — the core of modern semantic search and RAG systems. Instead of comparing keywords, it compares numerical representations of meaning. RAG (Retrieval-Augmented Generation) is an approach that supplements a language model with relevant documents retrieved from an external database, enabling more accurate and verifiable responses without retraining the model. GPU-accelerated vector search is precisely what makes RAG systems capable of operating in real time over large data corpora.

Four algorithms and compatibility with the NVIDIA ecosystem

hipVS is built on AMD’s hipRAFT algorithm and provides support for four vector search methods. CAGRA is a graph-based algorithm for approximate nearest neighbor (ANN) search, known for high performance on dense indexes. IVF-Flat offers exact search within clusters without compression, while IVF-PQ uses product quantization for compressed ANN search with a reduced memory footprint. The fourth algorithm is Brute-Force exact k-NN, which guarantees precise results without approximation.

AMD particularly emphasized compatibility with NVIDIA’s cuVS API. This means development teams already using cuVS on NVIDIA hardware can port workloads to AMD Instinct GPUs with minimal changes to existing code — a direct competitive advantage against the dominant CUDA ecosystem.

Demo: agentic RAG with query decomposition

Alongside the library, AMD released a demonstration of an agentic RAG system illustrating the practical use of hipVS. When a user submits a complex query, the system automatically decomposes it into 3 to 5 sub-queries that are forwarded to the vector database in parallel. The results are then deduplicated to remove overlapping documents, and the language model synthesizes a final answer with explicit source citations for each claim.

This architecture enables significantly better responses to complex multi-part questions than classic single-pass RAG queries. AMD positions hipVS as infrastructure for enterprise AI systems that need scalable vector search on their own hardware — an alternative to cloud vector databases like Pinecone or Weaviate.