nvidia-cufile
cuFile GPUDirect libraries
Decision gist · record as of 2026-08-14
Yes, if you are running GPU-accelerated workloads on Linux with NVIDIA hardware and need to optimize storage I/O. The package is actively maintained, has no known vulnerabilities, and addresses a real performance bottleneck in GPU computing. However, verify the unclear license terms before use in production, and confirm that your system has the required cuFile runtime libraries installed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA-capable GPU, Linux (aarch64 or x86_64), and NVIDIA cuFile runtime libraries installed on the system.
- Medium install friction due to platform-specific wheels (aarch64 and x86_64 Linux only); recently released (46 days old) with active maintenance status, though repository visibility is limited.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is provided, so legal terms and redistribution rights cannot be determined from the package metadata alone.
last release 2026-06-29 (46 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 38,976,413 downloads/mo, #703 on PyPI
Alternatives
Verify before relying
pip install nvidia-cufile==1.18.1.6
import cufile
# Use cuFile APIs for GPU-direct storage operations- What specific cuFile APIs and functionality are exposed by this Python binding—read, write, memory management, or all three?
- Does this package require a minimum CUDA Compute Capability or specific GPU architecture?
- What is the actual license governing this package and its redistribution terms?
What it is and what it does
nvidia-cufile is a Python wrapper around NVIDIA's cuFile GPUDirect storage libraries, which allow applications to perform I/O operations directly between GPU memory and storage devices, bypassing the CPU. This is useful for machine learning and high-performance computing workloads that move large volumes of data between persistent storage and GPU memory, as it can reduce latency and CPU overhead.
The package is in Beta status and supports Python 3.5 through 3.11 on Linux (both aarch64 and x86_64 architectures). It has no Python runtime dependencies, but requires a working NVIDIA CUDA environment and the cuFile runtime libraries to be installed on the host system. Installation is platform-specific via pre-built wheels.
Use it for
- Accelerate data loading in deep learning training pipelines by reading training data directly from disk to GPU memory.
- Optimize checkpoint save/restore operations in large-scale model training by writing directly from GPU memory to storage.
- Reduce CPU bottlenecks in data-intensive HPC simulations that require frequent GPU-storage transfers.
- Enable efficient batch processing of large datasets in machine learning workflows without staging through host memory.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are running GPU-accelerated workloads on Linux with NVIDIA hardware and need to optimize storage I/O.
The package is actively maintained, has no known vulnerabilities, and addresses a real performance bottleneck in GPU computing. However, verify the unclear license terms before use in production, and confirm that your system has the required cuFile runtime libraries installed.
Install
nvidia-cufile on PyPI
Before you install
Medium install friction due to platform-specific wheels (aarch64 and x86_64 Linux only); recently released (46 days old) with active maintenance status, though repository visibility is limited.
Requires NVIDIA CUDA-capable GPU, Linux (aarch64 or x86_64), and NVIDIA cuFile runtime libraries installed on the system.
License in practice
License status is unclear—no SPDX identifier or raw license text is provided, so legal terms and redistribution rights cannot be determined from the package metadata alone.
Quickstart
pip install nvidia-cufile==1.18.1.6
import cufile
# Use cuFile APIs for GPU-direct storage operationsVerify before relying
- What specific cuFile APIs and functionality are exposed by this Python binding—read, write, memory management, or all three?
- Does this package require a minimum CUDA Compute Capability or specific GPU architecture?
- What is the actual license governing this package and its redistribution terms?
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 46 days since the last release |
| First released | |
| Downloads | 38,976,413 / month, #703 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_cufile-1.18.1.6-py3-none-manylinux_2_27_aarch64.whl; nvidia_cufile-1.18.1.6-py3-none-manylinux_2_27_x86_64.whl
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See also cufile-python · libkvikio-cu12 · nvidia-cufile-cu12 · nvidia-cuda-runtime · nvidia-cuda-crt · nvidia-cuda-runtime-cu11 · nvidia-cublas · nvidia-cufft-cu12 · nvidia-cuda-runtime-cu12 · hip-python