# Gpu Acceleration > Browse 16 items tagged with Gpu Acceleration. ## Items - [NVIDIA cuVS](https://vectordb.ever.works/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. - [cuVS](https://vectordb.ever.works/items/cuvs) — NVIDIA RAPIDS cuVS is a GPU-accelerated library for vector search and clustering with CUDA-optimized HNSW, IVF, CAGRA, and PQ implementations. Supports batch queries for high QPS, suited for large-scale similarity search in GenAI apps. Achieves up to 12x faster indexing and lower latency vs CPU-only alternatives like FAISS CPU. - [PilotANN](https://vectordb.ever.works/items/pilotann) — Memory-bounded GPU-accelerated framework for graph-based ANN vector search using CUDA and LibTorch, optimized for large-scale workloads beyond GPU memory. Features batch processing for high efficiency; outperforms CPU-only ANN in speed for similarity search in vector databases. - [RUMMY](https://vectordb.ever.works/items/rummy-gpu-vector-search) — GPU-accelerated vector query processing system using CUDA to handle datasets larger than GPU memory via reordered pipelining and cluster-based retrofitting. Supports batch queries with up to 135x speedup over traditional GPU methods and 23x vs CPU-only for large-scale similarity search and MIPS. - [FusionANNS](https://vectordb.ever.works/items/fusionanns-cpu-gpu-search) — An efficient CPU/GPU cooperative processing architecture for billion-scale approximate nearest neighbor search. FusionANNS achieves up to 13.1× higher QPS compared to SPANN and can handle billion-vector datasets with over 12,000 QPS while maintaining 15ms latency using only one entry-level GPU. - [RAFT](https://vectordb.ever.works/items/raft) — RAFT is a suite of GPU-accelerated libraries for data science, including support for vector search and similarity operations, often used in vector database scenarios. - [Juno — Optimizing ANNS with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping](https://vectordb.ever.works/items/juno-optimizing-anns-with-sparsity-aware-algorithm-and-ray-tracing-core-mapping) — ASPLOS 2024 paper introducing Juno, a system that accelerates high-dimensional approximate nearest neighbor search using sparsity-aware algorithms and GPU ray-tracing (RT) core mapping for hardware-level computation acceleration. - [vLLM](https://vectordb.ever.works/items/vllm) — High-throughput and memory-efficient open-source LLM inference engine with PagedAttention, continuous batching, and support for embedding model serving. Widely adopted for production-scale AI inference. - [Breaking the Storage-Compute Bottleneck in Billion-Scale ANNS](https://vectordb.ever.works/items/breaking-storage-compute-bottleneck) — A 2025 research paper presenting a GPU-driven asynchronous I/O framework for billion-scale approximate nearest neighbor search. The system addresses the fundamental bottleneck of data movement between storage and compute in large-scale vector search. - [Infinity](https://vectordb.ever.works/items/infinity-embedding-server) — High-throughput, low-latency serving engine for text embeddings, reranking models, CLIP, CLAP and ColPali with GPU acceleration support for local deployment and production use. - [RAPIDS cuVS](https://vectordb.ever.works/items/rapids-cuvs) — GPU-accelerated vector search library from NVIDIA providing approximate nearest neighbors and clustering algorithms with up to 12x faster index builds and 4.7x lower search latency through GPU parallelization. - [GTS](https://vectordb.ever.works/items/gts) — GTS is a GPU-based tree index for fast similarity search over high-dimensional vector data, providing an efficient ANN index structure that can be integrated into or used to build high-performance vector database systems. - [RTNN](https://vectordb.ever.works/items/rtnn) — RTNN is a research prototype system and codebase that accelerates high-dimensional nearest neighbor search using hardware ray tracing units on modern GPUs. It targets vector similarity search workloads common in AI applications, exploring ray-tracing hardware as an alternative acceleration path to traditional CPU- or CUDA-based ANN indexes. - [BANG](https://vectordb.ever.works/items/bang) — BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale. - [Cagra](https://vectordb.ever.works/items/cagra) — Cagra provides highly parallel graph construction and approximate nearest neighbor search for GPUs, supporting large-scale vector database operations and efficient similarity search. - [NVIDIA CAGRA](https://vectordb.ever.works/items/nvidia-cagra) — NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval. --- _Canonical page: https://vectordb.ever.works/tags/gpu-acceleration_