---
id: pylibraft-cu12
version: "26.8.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# pylibraft-cu12 — RAFT: Reusable Algorithms Functions and other Tools

License: permissive · Maintenance: active · Downloads: 396.0K/mo

## What it is and what it does

pylibraft-cu12 is the Python interface to RAFT, a C++ header-only template library of CUDA-accelerated primitives for machine learning and data mining. It exposes runtime APIs that do not require a CUDA compiler, making GPU-accelerated algorithms accessible from Python without compilation overhead.

The package provides low-level building blocks—linear algebra, matrix operations, sparse and dense computations, solvers, and statistics—designed for application developers and data source providers building high-performance GPU workflows. It integrates with the RAPIDS ecosystem via RMM (memory management) and numpy, and accepts any object supporting the __cuda_array_interface__, enabling interoperability with other GPU libraries.

Use it for:

- Accelerate linear algebra operations (SVD, eigenvalue, factorization) on GPU-resident matrices.
- Implement sparse matrix operations and graph algorithms on GPU with centralized, optimized primitives.
- Develop high-performance machine learning applications that reuse vetted, maintained RAFT kernels.
- Build distributed GPU algorithms with multi-node multi-GPU infrastructure via raft-dask.
- Integrate GPU-accelerated computations into Python workflows with zero-copy interoperability.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

pylibraft-cu12 provides Python bindings to RAFT's CUDA-accelerated primitives for linear algebra, sparse and dense operations, statistics, and solvers, designed for GPU-accelerated algorithm development.

Yes, if you have a CUDA 12 GPU and need low-level accelerated primitives for algorithm development. The active maintenance, permissive Apache-2.0 license, and interoperability with numpy and other GPU libraries make it a solid foundation for GPU-accelerated workflows. Medium install friction is acceptable for the performance and code-reuse benefits. Not suitable if you lack GPU hardware or need high-level data science tools.

## Install

pip install pylibraft-cu12
uv add pylibraft-cu12
poetry add pylibraft-cu12

## Installing pylibraft-cu12

Before you install: Medium install friction due to CUDA 12 and GPU-specific dependencies (libraft-cu12, rmm-cu12, cuda-bindings). Requires Python 3.11+. Active maintenance with recent releases; repository shows 1037 stars and last commit on 2026-08-14.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.

Quickstart:

pip install pylibraft-cu12

import numpy
from pylibraft import device_ndarray

# Create GPU-resident arrays via numpy or __cuda_array_interface__ compatible objects
output = device_ndarray()

Requires NVIDIA GPU with CUDA 12 support and CUDA Toolkit installed; Python 3.11 or later.

Verify before relying:

- Specific CUDA compute capability requirements beyond CUDA 12 availability.
- Whether libraft-cu12 and rmm-cu12 are available for all target platforms or if installation may fail on unsupported architectures.
- Concrete example workflows demonstrating the package's primitives in isolation without external GPU libraries.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 396.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

cuda accelerated linear algebra python, gpu primitives machine learning, raft python bindings, cuda sparse dense operations, gpu accelerated algorithms, gpu-accelerated, cuda, linear-algebra

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