# Gpu Support

> Browse 7 items tagged with Gpu Support.

## Items

- [NVIDIA cuVS](https://vectordb.ever.works/th/items/nvidia-cuvs) — NVIDIA cuVS is a GPU-accelerated approximate nearest neighbor search library utilizing CUDA for high-performance CAGRA, HNSW, IVF-PQ indexes on billion-scale datasets. Supports batch queries for high-throughput operations, ideal for large-scale similarity search and real-time recommendations. Delivers up to 12x faster index building and 8x lower query latency compared to CPU-only implementations like Milvus.
- [Faiss](https://vectordb.ever.works/th/items/faiss) — Faiss (Facebook AI Similarity Search) is a library for efficient similarity search/ clustering of dense vectors, supports GPU/CPU indexes like IVF, PQ, HNSW. Core for building custom VDBs; compares to Annoy by higher perf/scalability. Features: quantization, exact search.
- [Milvus Distributed](https://vectordb.ever.works/th/items/milvus-distributed) — Milvus Distributed is the cluster mode of the scalable open-source vector database for AI embeddings search, supporting HNSW, IVF, and NGT indexes in high-availability distributed setups. It provides GPU support, billion-scale capacity, real-time upsert/query capabilities, and multi-modal vector handling. Suited for RAG, recommendations, and image/video search at enterprise scale. Self-hosted unlike Pinecone's managed offering, and more ANN-centric than Weaviate.
- [Milvus WebUI](https://vectordb.ever.works/th/items/milvus-webui) — Milvus WebUI provides management for the scalable open-source vector DB ecosystem, enabling oversight of HNSW/IVF/NGT indexed collections in distributed/cluster/embedded Milvus modes. Supports monitoring of GPU-accelerated, billion-scale, real-time multi-modal operations. Facilitates RAG, recommendations, image/video search management; pairs with self-hosted Milvus vs Pinecone/Weaviate alternatives.
- [GPU-Accelerated Vector Indexing](https://vectordb.ever.works/th/items/gpu-accelerated-vector-indexing) — Open-source project demonstrating GPU-accelerated approximate nearest neighbor search using Inverted File (IVF) indexing on embeddings from a large Wikipedia dataset. It employs K-means clustering into 128 clusters and supports configurable CUDA kernels for coarse and fine search stages. Applicable for efficient vector querying in AI applications.
- [Hora](https://vectordb.ever.works/th/items/hora) — High-performance vector search library with product quantization.
- [Milvus](https://vectordb.ever.works/th/items/milvus) — Milvus is an open-source vector database designed for scalable similarity search on massive datasets, supporting billions of vectors with high performance. Key features include distributed architecture, support for multiple indexes like HNSW and IVF, hybrid search, and integrations with popular ML frameworks. Ideal for RAG pipelines, recommendation systems, and AI agents; self-hosted alternative to managed services like Pinecone with better cost control for large-scale deployments.

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_Canonical page: https://vectordb.ever.works/th/tags/gpu-support_