nvidia-cufile-cu12
cuFile GPUDirect libraries
Decision gist · record as of 2026-08-14
Yes, if you have a CUDA 12 GPU workload on Linux that is I/O-bound and you need GPUDirect Storage acceleration. No, if you are on Windows, do not have CUDA 12, or your workload is not storage-intensive enough to justify the added complexity. Verify NVIDIA's proprietary license terms and check that your system has the required cuFile runtime before installing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA 12 and compatible GPU; only x86_64 and aarch64 Linux wheels are available despite Windows classifier listing.
- Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only).
- Package is aging—last release was 435 days ago—with no visible repository or commit history in the fact sheet, making it difficult to assess ongoing maintenance.
License · maintenance · safety
LicenseRef-NVIDIA-Proprietary (unclear) — Licensed under a proprietary NVIDIA license (LicenseRef-NVIDIA-Proprietary) with unclear treatment. Verify NVIDIA's terms before use in commercial or redistributed projects.
last release 2025-06-05 (435 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 14,508,752 downloads/mo, #1,227 on PyPI
Alternatives
Verify before relying
pip install nvidia-cufile-cu12
import cufile
# Use cuFile APIs for GPU-direct storage operations- Whether this package requires a local NVIDIA cuFile installation or runtime library on the system
- Compatibility with specific CUDA 12.x versions and whether it works across all CUDA 12 minor releases
- Whether Windows support (listed in classifiers) is actually available given only Linux wheels in friction evidence
What it is and what it does
nvidia-cufile-cu12 is a Python interface to NVIDIA's cuFile library, which implements GPUDirect Storage—a technology that allows GPUs to read and write data directly to storage devices without routing through the CPU. This eliminates a major bottleneck in data-intensive workloads like machine learning training and scientific computing.
The package is designed for CUDA 12 environments and targets developers working with high-performance GPU applications that need fast I/O. It has no Python runtime dependencies, but requires a compatible NVIDIA GPU, CUDA 12 installation, and a Linux system (x86_64 or aarch64). The package is classified as Beta and has not been updated in over a year, suggesting it may not receive active maintenance.
Use it for
- Accelerate training loops in deep learning frameworks by enabling direct GPU reads from large datasets stored on disk or network storage.
- Speed up scientific simulations that process massive arrays by avoiding CPU memory copies during I/O operations.
- Optimize data pipelines in machine learning inference servers that need to load and process batches with minimal latency.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have a CUDA 12 GPU workload on Linux that is I/O-bound and you need GPUDirect Storage acceleration.
No, if you are on Windows, do not have CUDA 12, or your workload is not storage-intensive enough to justify the added complexity. Verify NVIDIA's proprietary license terms and check that your system has the required cuFile runtime before installing.
Install
nvidia-cufile-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only). Package is aging—last release was 435 days ago—with no visible repository or commit history in the fact sheet, making it difficult to assess ongoing maintenance.
Requires NVIDIA CUDA 12 and compatible GPU; only x86_64 and aarch64 Linux wheels are available despite Windows classifier listing.
License in practice
Licensed under a proprietary NVIDIA license (LicenseRef-NVIDIA-Proprietary) with unclear treatment. Verify NVIDIA's terms before use in commercial or redistributed projects.
Quickstart
pip install nvidia-cufile-cu12
import cufile
# Use cuFile APIs for GPU-direct storage operationsVerify before relying
- Whether this package requires a local NVIDIA cuFile installation or runtime library on the system
- Compatibility with specific CUDA 12.x versions and whether it works across all CUDA 12 minor releases
- Whether Windows support (listed in classifiers) is actually available given only Linux wheels in friction evidence
Package facts
| License | LicenseRef-NVIDIA-Proprietary unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Aging 435 days since the last release |
| First released | |
| Downloads | 14,508,752 / month, #1,227 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_cufile_cu12-1.14.1.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; nvidia_cufile_cu12-1.14.1.1-py3-none-manylinux_2_27_aarch64.whl
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See also cufile-python · libkvikio-cu12 · nvidia-cufile · nvidia-cuda-runtime-cu12 · nvidia-cufft-cu12 · nvidia-cuda-cccl-cu12 · nvidia-cuda-runtime-cu11 · nvidia-cublas-cu12 · nvidia-cudnn-cu11 · nvidia-curand-cu12