GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering.
Good fit when
- - When you need high-performance vector operations leveraging the parallel processing power of GPUs, specifically with CUDA.
- - If your application requires heavy lifting in similarity searches or neighborhood methods using dense vectors where GPU acceleration is beneficial.
Avoid when
- - For environments where GPU resources are limited or unavailable because cuVS heavily relies on CUDA's capabilities for performance gains.
- - When you prioritize portability across different hardware, as cuVS being tied to CUDA means it may not be optimal on non-NVIDIA GPUs or CPU-only systems.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Nvidia. Display-only - separate from trust and ranking.
- Company
- NVIDIA Corporation·GitHub org profile·Jul 11, 2026
- Employees
- 11,528·Wikidata (P1128 employees)·Jul 11, 2026
- Commercial model
- Pure OSS·GitHub org profile (public repos)·Jul 11, 2026
Install
git clone https://github.com/NVIDIA/cuvsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
cuVS is a CUDA-based library designed to perform vector search and clustering operations efficiently on GPUs.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Sep 20, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Sep 20, 2026
- Languages
- cuda, python
Source: github.language+pyproject.toml · Sep 20, 2026
Categories
Graph entities
Tags
README
Standalone C library (Docker) To build the standalone C library tarball ( ) for use in your own C/C++ projects, see Building the standalone C library with Docker . Getting Started The following code snippets train an approximate nearest neighbors index for the CAGRA algorithm in...
For agents
This page has a .md twin and JSON over the API.