cupy-cuda12x
CuPy: NumPy & SciPy for GPU
Decision gist · record as of 2026-08-14
Yes, if you have CUDA 12.x hardware and want GPU-accelerated NumPy-like operations. The package is actively maintained, permissively licensed, and widely used in production. Install friction is moderate—you need CUDA Toolkit 12.x or must use the [ctk] extra—but that's a one-time setup cost. No known security vulnerabilities. Not worth installing if you don't have NVIDIA GPU hardware or are locked into a different CUDA version.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- CUDA Toolkit 12.x must be installed locally on your system, or you must use the [ctk] extra to install CUDA components alongside the package.
- Medium install friction: requires CUDA Toolkit 12.x to be installed locally, or you can use the [ctk] extra to bundle CUDA components from PyPI.
- The package is actively maintained with recent releases and supports Python 3.10–3.14 across Linux, Windows, and ARM platforms.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-06-01 (74 days) · last repo commit 2026-08-14 · 12,244 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,215,490 downloads/mo, #2,698 on PyPI
Alternatives
Verify before relying
pip install cupy-cuda12x
# or with bundled CUDA:
# pip install cupy-cuda12x[ctk]
import cupy as cp
x_gpu = cp.array([1, 2, 3])
result = cp.sum(x_gpu)- Performance gains relative to NumPy on typical workloads and hardware configurations.
- Memory overhead or compatibility gotchas when mixing CuPy and NumPy arrays in the same codebase.
- Support status for older CUDA 12.x minor versions (e.g., 12.0 vs. 12.6).
What it is and what it does
CuPy is a GPU-accelerated array library that mirrors NumPy and SciPy's API, allowing you to write numerical code that runs on NVIDIA GPUs instead of CPUs. It's built on CUDA 12.x and lets you leverage GPU parallelism for matrix operations, linear algebra, Fourier transforms, and other scientific computing tasks without rewriting your code from scratch. The package is actively maintained, supports modern Python versions (3.10–3.14), and is available as precompiled wheels for Linux (x86_64 and aarch64) and Windows.
You can install it standalone if you already have CUDA Toolkit 12.x on your system, or use the [ctk] extra to have pip bundle the necessary CUDA runtime components. It depends on NumPy and cuda-pathfinder for runtime operation. The library is in production-stable status and widely used in scientific and machine-learning workflows where GPU acceleration is needed.
Use it for
- Accelerate large matrix operations and linear algebra computations by running them on GPU instead of CPU.
- Port existing NumPy code to GPU with minimal changes, using CuPy's drop-in compatible API.
- Build GPU-accelerated machine learning pipelines that need fast numerical array operations.
- Perform large-scale scientific simulations and data transformations that benefit from GPU parallelism.
- Combine CuPy with deep learning frameworks to handle numerical preprocessing on the same GPU device.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have CUDA 12.x hardware and want GPU-accelerated NumPy-like operations.
The package is actively maintained, permissively licensed, and widely used in production. Install friction is moderate—you need CUDA Toolkit 12.x or must use the [ctk] extra—but that's a one-time setup cost. No known security vulnerabilities. Not worth installing if you don't have NVIDIA GPU hardware or are locked into a different CUDA version.
Install
cupy-cuda12x on PyPI
Before you install
Medium install friction: requires CUDA Toolkit 12.x to be installed locally, or you can use the [ctk] extra to bundle CUDA components from PyPI. The package is actively maintained with recent releases and supports Python 3.10–3.14 across Linux, Windows, and ARM platforms.
CUDA Toolkit 12.x must be installed locally on your system, or you must use the [ctk] extra to install CUDA components alongside the package.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install cupy-cuda12x
# or with bundled CUDA:
# pip install cupy-cuda12x[ctk]
import cupy as cp
x_gpu = cp.array([1, 2, 3])
result = cp.sum(x_gpu)Verify before relying
- Performance gains relative to NumPy on typical workloads and hardware configurations.
- Memory overhead or compatibility gotchas when mixing CuPy and NumPy arrays in the same codebase.
- Support status for older CUDA 12.x minor versions (e.g., 12.0 vs. 12.6).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpycuda-pathfinder |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,215,490 / month, #2,698 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: CythonProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: cupy_cuda12x-14.1.1-cp310-cp310-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp310-cp310-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp310-cp310-win_amd64.whl; cupy_cuda12x-14.1.1-cp311-cp311-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp311-cp311-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp311-cp311-win_amd64.whl; cupy_cuda12x-14.1.1-cp312-cp312-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp312-cp312-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp312-cp312-win_amd64.whl; cupy_cuda12x-14.1.1-cp313-cp313-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp313-cp313-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp313-cp313-win_amd64.whl; cupy_cuda12x-14.1.1-cp314-cp314-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp314-cp314-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp314-cp314t-manylinux2014_aarch64.whl; cupy_cuda12x-14.1.1-cp314-cp314t-manylinux2014_x86_64.whl; cupy_cuda12x-14.1.1-cp314-cp314-win_amd64.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 › “gpu array computing”
- cupy-cuda12xCuPy provides a NumPy- and SciPy-compatible array library that runs…
- cupy-cuda13xCuPy provides a NumPy and SciPy-compatible array library that runs…
- jax-cuda13-pjrtProvides NVIDIA GPU acceleration for JAX numerical computing via the…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also cupy-cuda13x · nvidia-cufft-cu12 · nvidia-cuda-cccl-cu12 · nvidia-cuda-runtime-cu12 · array-api-compat · nvidia-cufft-cu11 · coffea · nvidia-cublas-cu12 · nvidia-cublas-cu11 · nvidia-cuda-runtime-cu11