NVIDIA cuFFT for CUDA 12

Common errors

Warnings

Install

Install compatibility stale last tested: 2026-05-12

Imports

• cuFFT functionality

  This package is a low-level runtime library and is not directly imported in Python. High-level libraries like `nvmath-python`, `torch`, or `tensorflow` provide Python interfaces that utilize `nvidia-cufft-cu12` under the hood.

  nvidia-cufft-cu12 primarily provides the underlying C/C++ binaries for GPU-accelerated FFTs. Python users typically interact with cuFFT through frameworks like PyTorch, TensorFlow, or dedicated Python wrappers like `nvmath-python`.

• nvmath.fft

  from nvmath.fft import fft, ifft

  This is the recommended way to directly access cuFFT functionalities in Python via NVIDIA's `nvmath-python` library, which depends on `nvidia-cufft-cu12`.

Quickstart stale last tested: 2026-04-24

This quickstart demonstrates how to perform a 1D complex-to-complex FFT and inverse FFT using `nvmath-python`, which leverages the `nvidia-cufft-cu12` runtime library. Ensure `cupy` is also installed (it's a dependency of `nvmath-python[cu12]`) for GPU array operations.

import os
import nvmath.fft as nvfft
import cupy as cp

# Ensure CUDA is available and nvmath-python is correctly set up
# (e.g., pip install nvmath-python[cu12] and appropriate CUDA Toolkit installation)

# Example: Perform a 1D complex-to-complex FFT using nvmath-python
size = 1024
x = cp.arange(size, dtype=cp.complex64)

# Perform forward FFT
y = nvfft.fft(x)

# Perform inverse FFT
z = nvfft.ifft(y)

print(f"Original data (first 5 elements): {x[:5].tolist()}")
print(f"FFT result (first 5 elements): {y[:5].tolist()}")
print(f"Inverse FFT result (first 5 elements): {z[:5].tolist()}")
print(f"Difference from original (max abs error): {cp.max(cp.abs(x - z))}")