nvidia-cufft
CUFFT native runtime libraries
Decision gist · record as of 2026-08-14
Yes, if you are building or using a CUDA-based application that requires FFT operations on NVIDIA GPUs and your platform is Linux (aarch64 or x86_64) or Windows x86_64. No, if you do not have NVIDIA GPU hardware or are not working in a CUDA environment. Check license terms with NVIDIA first due to unclear license metadata.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA Toolkit and compatible GPU hardware; wheels are platform-specific (Linux aarch64, Linux x86_64, or Windows x86_64 only).
- Medium install friction due to platform-specific wheels (Linux aarch64, Linux x86_64, Windows x86_64) and a compiled runtime dependency on nvidia-nvjitlink.
- Released actively; last update 80 days ago.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms directly with NVIDIA before use in proprietary or restricted contexts.
last release 2026-05-26 (80 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 41,694,885 downloads/mo, #668 on PyPI
Alternatives
Verify before relying
pip install nvidia-cufft
import cufft # Requires nvidia-nvjitlink runtime dependency- Whether this package provides Python bindings or is a runtime-only library without direct Python API.
- Specific CUDA Toolkit version compatibility and minimum GPU compute capability requirements.
- Whether nvidia-nvjitlink is bundled or must be installed separately as a system dependency.
What it is and what it does
nvidia-cufft is a native runtime library package that exposes NVIDIA's CUFFT (CUDA Fast Fourier Transform) library for GPU-accelerated FFT computations. It is designed for developers working with CUDA on supported platforms (Linux aarch64, Linux x86_64, Windows x86_64) who need high-performance Fourier transforms on NVIDIA GPUs. The package depends on nvidia-nvjitlink and targets Python 3.5 and later, though it is classified as Beta status.
This is a low-level runtime distribution rather than a high-level Python wrapper—it provides the compiled CUFFT binaries that higher-level libraries (such as CuPy or other CUDA-aware scientific packages) may depend on or wrap. Installation requires a compatible NVIDIA GPU and CUDA environment; it is not useful on systems without NVIDIA hardware.
Use it for
- As a runtime dependency for scientific computing libraries that perform GPU-accelerated FFT operations on NVIDIA hardware.
- In deep learning pipelines that require fast Fourier transforms as part of signal processing or frequency-domain operations on GPU.
- For research or production systems performing real-time spectral analysis or signal processing on CUDA-capable GPUs.
- As part of a CUDA toolkit installation for applications that call CUFFT functions directly or indirectly through higher-level bindings.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or using a CUDA-based application that requires FFT operations on NVIDIA GPUs and your platform is Linux (aarch64 or x86_64) or Windows x86_64.
No, if you do not have NVIDIA GPU hardware or are not working in a CUDA environment. Check license terms with NVIDIA first due to unclear license metadata.
Install
nvidia-cufft on PyPI
Before you install
Medium install friction due to platform-specific wheels (Linux aarch64, Linux x86_64, Windows x86_64) and a compiled runtime dependency on nvidia-nvjitlink. Released actively; last update 80 days ago.
Requires NVIDIA CUDA Toolkit and compatible GPU hardware; wheels are platform-specific (Linux aarch64, Linux x86_64, or Windows x86_64 only).
License in practice
License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify licensing terms directly with NVIDIA before use in proprietary or restricted contexts.
Quickstart
pip install nvidia-cufft
import cufft # Requires nvidia-nvjitlink runtime dependencyVerify before relying
- Whether this package provides Python bindings or is a runtime-only library without direct Python API.
- Specific CUDA Toolkit version compatibility and minimum GPU compute capability requirements.
- Whether nvidia-nvjitlink is bundled or must be installed separately as a system dependency.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenvidia-nvjitlink |
| Maintenance | Actively maintained 80 days since the last release |
| First released | |
| Downloads | 41,694,885 / month, #668 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/ResearchNatural 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-12.3.0.29-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; nvidia_cufft-12.3.0.29-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; nvidia_cufft-12.3.0.29-py3-none-win_amd64.whl
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See also nvidia-cufft-cu12 · nvidia-cufft-cu11 · nvidia-cublas · nvidia-mathdx · hankel · nvidia-cusolver · nvidia-cuda-cupti · nvidia-curand · nvidia-cuda-runtime · nvidia-cusolver-cu11