raft
A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.
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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 1d
- Provenance
- Not a fork · Organization account
- As of 1d
- Security (OSV)
- No lockfile
- As of 1mo
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·1mo
- Employees
- 11,528·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
git clone https://github.com/NVIDIA/raftSimilar tools
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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 · Aug 23, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
In addition being a C++ library, RAFT also provides 2 Python libraries:Source link
Tags
README
RAFT: Reusable Accelerated Functions and Tools
RAFT: Reusable Accelerated Functions and Tools
Contents
- Useful Resources
- What is RAFT?
- Use cases
- Is RAFT right for me?
- Getting Started
- Installing RAFT
- Codebase structure and contents
- Contributing
- References
Useful Resources
- RAFT Reference Documentation: API Documentation.
- RAFT Getting Started: Getting started with RAFT.
- Build and Install RAFT: Instructions for installing and building RAFT.
- RAPIDS Community: Get help, contribute, and collaborate.
- GitHub repository: Download the RAFT source code.
- Issue tracker: Report issues or request features.
What is RAFT?
RAFT contains fundamental widely-used algorithms and primitives for machine learning and data mining. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
By taking a primitives-based approach to algorithm development, RAFT
- accelerates algorithm construction time
- reduces the maintenance burden by maximizing reuse across projects, and
- centralizes core reusable computations, allowing future optimizations to benefit all algorithms that use them.
While not exhaustive, the following general categories help summarize the accelerated functions in RAFT:
| Category | Accelerated Functions in RAFT |
|---|---|
| Data Formats | sparse & dense, conversions, data generation |
| Dense Operations | linear algebra, matrix and vector operations, reductions, slicing, norms, factorization, least squares, svd & eigenvalue problems |
| Sparse Operations | linear algebra, eigenvalue problems, slicing, norms, reductions, factorization, symmetrization, components & labeling |
| Solvers | combinatorial optimization, iterative solvers |
| Statistics | sampling, moments and summary statistics, metrics, model evaluation |
| Tools & Utilities | common tools and utilities for developing CUDA applications, multi-node multi-gpu infrastructure |
RAFT is a C++ header-only template library with an optional shared library that
- can speed up compile times for common template types, and
- provides host-accessible "runtime" APIs, which don't require a CUDA compiler to use
In addition being a C++ library, RAFT also provides 2 Python libraries:
pylibraft- lightweight Python wrappers around RAFT's host-accessible "runtime" APIs.raft-dask- multi-node multi-GPU communicator infrastructure for building distributed algorithms on the GPU with Dask.
Is RAFT right for me?
RAFT contains low-level primitives for accelerating applications and workflows. Data source providers and application developers may find specific tools very useful. RAFT is not intended to be used directly by data scientists for discovery and experimentation. For data science
For agents
This page has a .md twin and JSON over the API.