libcuvs-cu12
cuVS: Vector Search on the GPU (C++)
Decision gist · record as of 2026-08-14
Yes, if you have NVIDIA GPU hardware and need fast vector search or clustering. The library is actively maintained, has no known vulnerabilities, and the Apache-2.0 license is permissive. Install friction is moderate due to CUDA and GPU library dependencies, but these are expected for GPU-accelerated workloads. Not suitable for CPU-only environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU with CUDA 12 support and CUDA toolkit installed; Python >=3.11
- Medium install friction due to CUDA toolkit and multiple NVIDIA GPU libraries (libraft-cu12, librmm-cu12, nvidia-nccl-cu12, nvidia-nvjitlink-cu12) as runtime dependencies.
- Active maintenance with recent release and steady repository activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for integration into proprietary applications.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 833 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 114,530 downloads/mo, #12,291 on PyPI
Alternatives
Verify before relying
pip install libcuvs-cu12
from libcuvs-cu12 import neighbors
# Build approximate nearest neighbor index on GPU-resident data
index_params = neighbors.IndexParams()
index = neighbors.build(index_params, dataset)- Whether pre-built wheels cover all target architectures beyond aarch64 and x86_64
- Specific CUDA version compatibility requirements beyond the cu12 variant designation
- Performance characteristics or throughput benchmarks for different dataset sizes
- Whether the Python API is available in this package or requires separate installation
What it is and what it does
libcuvs-cu12 is a GPU-accelerated library for vector search and clustering built on NVIDIA CUDA. It implements state-of-the-art approximate nearest neighbor algorithms and clustering primitives designed to run on NVIDIA GPUs, with the goal of accelerating similarity search on dense vector embeddings. The library is part of the RAPIDS ecosystem and provides APIs for multiple languages.
The package is used for semantic search tasks—including retrieval-augmented generation, recommendation systems, and image/text search—as well as data mining operations like clustering, visualization, and k-NN graph construction. It requires CUDA 12 and depends on libraft-cu12, librmm-cu12, nvidia-nccl-cu12, and nvidia-nvjitlink-cu12, meaning it is only usable on systems with compatible NVIDIA GPUs and the CUDA toolkit installed.
Use it for
- Build fast approximate nearest neighbor indexes for semantic search in retrieval-augmented generation and generative AI applications
- Accelerate recommendation systems by computing similarity between user embeddings and item vectors on GPU
- Perform large-scale clustering and visualization tasks on GPU-resident data
- Construct k-NN graphs from dense vectors for graph analysis and downstream machine learning
- Enable low-latency, high-throughput vector similarity queries in production retrieval systems
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have NVIDIA GPU hardware and need fast vector search or clustering.
The library is actively maintained, has no known vulnerabilities, and the Apache-2.0 license is permissive. Install friction is moderate due to CUDA and GPU library dependencies, but these are expected for GPU-accelerated workloads. Not suitable for CPU-only environments.
Install
libcuvs-cu12 on PyPI
Before you install
Medium install friction due to CUDA toolkit and multiple NVIDIA GPU libraries (libraft-cu12, librmm-cu12, nvidia-nccl-cu12, nvidia-nvjitlink-cu12) as runtime dependencies. Active maintenance with recent release and steady repository activity.
Requires NVIDIA GPU with CUDA 12 support and CUDA toolkit installed; Python >=3.11
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for integration into proprietary applications.
Quickstart
pip install libcuvs-cu12
from libcuvs-cu12 import neighbors
# Build approximate nearest neighbor index on GPU-resident data
index_params = neighbors.IndexParams()
index = neighbors.build(index_params, dataset)Verify before relying
- Whether pre-built wheels cover all target architectures beyond aarch64 and x86_64
- Specific CUDA version compatibility requirements beyond the cu12 variant designation
- Performance characteristics or throughput benchmarks for different dataset sizes
- Whether the Python API is available in this package or requires separate installation
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagescuda-toolkitlibraft-cu12librmm-cu12nvidia-nccl-cu12nvidia-nvjitlink-cu12 |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 114,530 / month, #12,291 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Developers |
Evidence: libcuvs_cu12-26.8.1-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; libcuvs_cu12-26.8.1-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “approximate nearest neighbors gpu”
- libcuvs-cu12GPU-accelerated vector search and clustering library providing…
- cuvs-cu12Provides GPU-accelerated approximate nearest neighbor search and…
- faiss-gpuFaiss provides GPU-accelerated similarity search and clustering for…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also cuvs-cu12 · nmslib · faiss-gpu · fastcluster · cuml-cu12 · annoy · scann · libcuml-cu12 · libraft-cu12 · pynndescent