---
id: cupy-cuda13x
version: "14.1.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# cupy-cuda13x — CuPy: NumPy & SciPy for GPU

License: permissive · Maintenance: active · Downloads: 620.7K/mo

## What it is and what it does

CuPy is a GPU-accelerated array computing library that mirrors NumPy and SciPy's API, allowing you to run numerical computations on NVIDIA GPUs via CUDA 13.x. It is designed for scientists and engineers who want to leverage GPU parallelism without rewriting their numerical code from scratch. The package depends on numpy and cuda-pathfinder, and is distributed as precompiled wheels for Python 3.10 through 3.14 on Linux and Windows.

You install it either by ensuring CUDA Toolkit 13.x is already on your system, or by using the [ctk] tag to have pip bundle the necessary CUDA runtime components. Once installed, you can replace NumPy arrays with CuPy arrays and call the same functions, with computation happening on the GPU instead of the CPU.

Use it for:

- Accelerate large-scale matrix operations and linear algebra for machine learning or scientific simulations.
- Speed up image processing pipelines by running convolutions and transformations on GPU.
- Run iterative numerical algorithms such as optimization or PDE solvers that benefit from GPU parallelism.
- Prototype GPU-accelerated code in Python before moving to lower-level implementations.
- Process high-dimensional data arrays in applications where CPU throughput is a bottleneck.

## Worth the install?

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

CuPy provides a NumPy and SciPy-compatible array library that runs computations on NVIDIA GPUs via CUDA 13.x, allowing you to accelerate numerical code with minimal API changes.

Yes, if you have NVIDIA GPU hardware and CUDA 13.x available, and your workload involves large-scale numerical computation that would benefit from GPU acceleration. The package is production-stable, actively maintained, and has no known vulnerabilities. Install friction is moderate—you must have CUDA 13.x or use the [ctk] tag—but the API compatibility with NumPy makes adoption straightforward for existing code.

## Install

pip install cupy-cuda13x
uv add cupy-cuda13x
poetry add cupy-cuda13x

## Installing cupy-cuda13x

Before you install: Medium install friction due to compiled wheels and CUDA 13.x system requirement. The package is actively maintained with recent releases, and wheels are available for Python 3.10–3.14 on Linux and Windows; you must have CUDA Toolkit 13.x installed locally, or install with the [ctk] tag to bundle CUDA components.

License in practice: MIT license is permissive and imposes no restrictions on commercial or private use, modification, or redistribution.

Quickstart:

pip install cupy-cuda13x[ctk]

import cupy as cp
x_gpu = cp.array([1, 2, 3])
result = cp.sum(x_gpu)

CUDA Toolkit 13.x must be installed locally, or use [ctk] tag to bundle CUDA components from PyPI.

Verify before relying:

- Performance gains relative to CPU NumPy for specific workload types and array sizes.
- Compatibility of CUDA 13.x with your GPU hardware and driver versions.
- Whether the [ctk] installation option fully satisfies CUDA dependencies on all platforms.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 620.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

GPU array library NumPy compatible, CUDA accelerated computing Python, GPU scientific computing, NumPy on GPU, CUDA 13 array operations, GPU-accelerated numerical computing, SciPy GPU alternative, gpu-computing, cuda, array-library

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