# GPU Accelerated

> Browse 4 items tagged with GPU Accelerated.

## Items

- [GPU-Accelerated Vector Indexing](https://vectordb.ever.works/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.
- [VAST AI OS](https://vectordb.ever.works/items/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.
- [VAST CNode-X](https://vectordb.ever.works/items/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.
- [NVIDIA cuDF](https://vectordb.ever.works/items/nvidia-cudf) — Open-source Python GPU DataFrame library that accelerates popular data engines like Apache Spark, pandas, and Polars on NVIDIA AI infrastructure. Built on Apache Arrow, it utilizes GPU parallelism and memory bandwidth to accelerate data processing and analytics workflows, serving as the data-processing foundation for the Sirius GPU-accelerated database project.

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