raft
A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.
Good fit when
- - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.
- - The project involves extensive linear algebra operations or vector similarity calculations that would benefit from GPU acceleration.
Avoid when
- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains.
- - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.
- Requirements:
- Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d 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/raftSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
RAFT offers a suite of GPU-accelerated primitives and algorithms crucial for developing high-speed ML models and retrieval systems, utilizing the power of NVIDIA GPUs through CUDA.
Capability facts
- Languages
- cuda, python
Source: github.language+pyproject.toml · Sep 20, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
In addition being a C++ library, RAFT also provides 2 Python libraries:Source link
Tags
README
<div align="left" <img src="https://rapids.ai/assets/images/rapids logo.png" width="90px"/ RAFT: Reusable Accelerated Functions and Tools</div <p align="center" <img src="https://raw.githubusercontent.com/rapidsai/raft/HEAD/img/raft tech stack.svg" alt="RAFT tech stack" wid...
For agents
This page has a .md twin and JSON over the API.