nvidia-cufft-cu12
CUFFT native runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are developing GPU-accelerated applications targeting NVIDIA CUDA 12 hardware and need FFT functionality. The package has no known vulnerabilities and is widely downloaded. However, verify NVIDIA's proprietary license terms for your use case, and ensure your system has compatible NVIDIA hardware and drivers installed before proceeding.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an NVIDIA GPU with CUDA 12 support and the corresponding NVIDIA driver installed on the system.
- Medium install friction due to platform-specific wheel distributions (Windows, Linux x86_64, Linux aarch64).
- Maintenance status is aging—last release was 435 days ago—though the package remains part of NVIDIA's active CUDA ecosystem.
License · maintenance · safety
LicenseRef-NVIDIA-Proprietary (unclear) — Licensed under NVIDIA's proprietary license (LicenseRef-NVIDIA-Proprietary). License treatment is unclear; you should review NVIDIA's licensing terms before deploying in commercial or restricted environments.
last release 2025-06-05 (435 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 26,552,961 downloads/mo, #878 on PyPI
Alternatives
Verify before relying
pip install nvidia-cufft-cu12
import cufft
# Use CUFFT functions for GPU-accelerated FFT operations- Whether this package can be redistributed or used in closed-source commercial applications under NVIDIA's proprietary license terms
- Compatibility guarantees with specific CUDA driver versions beyond the cu12 designation
- Real-world performance characteristics and typical use patterns in production environments
What it is and what it does
nvidia-cufft-cu12 is a runtime library package that wraps NVIDIA's CUFFT (CUDA Fast Fourier Transform) library for CUDA 12. It provides the native compiled binaries needed to perform GPU-accelerated FFT computations, which are essential for signal processing, image analysis, and numerical computing workloads that benefit from parallel GPU execution.
The package is a low-level runtime dependency typically installed as part of a larger CUDA ecosystem (it depends on nvidia-nvjitlink-cu12). It is designed for developers building machine learning, scientific computing, or signal processing applications that require high-performance FFT operations on NVIDIA GPUs. The package is in Beta status and supports Python 3.5 through 3.11 on Windows and Linux platforms.
Use it for
- Accelerate FFT computations in deep learning frameworks running on NVIDIA GPUs
- Build GPU-accelerated signal processing pipelines for audio, radar, or seismic data analysis
- Optimize numerical simulations in scientific computing that rely on Fourier transforms
- Enable real-time frequency-domain processing in computer vision or image filtering applications
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing GPU-accelerated applications targeting NVIDIA CUDA 12 hardware and need FFT functionality.
The package has no known vulnerabilities and is widely downloaded. However, verify NVIDIA's proprietary license terms for your use case, and ensure your system has compatible NVIDIA hardware and drivers installed before proceeding.
Install
nvidia-cufft-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheel distributions (Windows, Linux x86_64, Linux aarch64). Maintenance status is aging—last release was 435 days ago—though the package remains part of NVIDIA's active CUDA ecosystem.
Requires an NVIDIA GPU with CUDA 12 support and the corresponding NVIDIA driver installed on the system.
License in practice
Licensed under NVIDIA's proprietary license (LicenseRef-NVIDIA-Proprietary). License treatment is unclear; you should review NVIDIA's licensing terms before deploying in commercial or restricted environments.
Quickstart
pip install nvidia-cufft-cu12
import cufft
# Use CUFFT functions for GPU-accelerated FFT operationsVerify before relying
- Whether this package can be redistributed or used in closed-source commercial applications under NVIDIA's proprietary license terms
- Compatibility guarantees with specific CUDA driver versions beyond the cu12 designation
- Real-world performance characteristics and typical use patterns in production environments
Package facts
| License | LicenseRef-NVIDIA-Proprietary unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenvidia-nvjitlink-cu12 |
| Maintenance | Aging 435 days since the last release |
| First released | |
| Downloads | 26,552,961 / month, #878 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseNatural Language :: EnglishOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: Libraries |
Evidence: nvidia_cufft_cu12-11.4.1.4-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; nvidia_cufft_cu12-11.4.1.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; nvidia_cufft_cu12-11.4.1.4-py3-none-win_amd64.whl
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See also nvidia-cufft · nvidia-cufft-cu11 · nvidia-cuda-runtime-cu12 · cupy-cuda12x · nvidia-cublas-cu11 · nvidia-cublas-cu12 · nvidia-cufile-cu12 · nvidia-cuda-runtime-cu11 · nvidia-mathdx · julius