nvidia-nvjitlink-cu12
Nvidia JIT LTO Library
Decision gist · record as of 2026-08-14
Yes, if you are building or running GPU-accelerated Python applications that depend on CUDA 12 JIT compilation. Install it as a dependency of higher-level libraries rather than directly. The proprietary license is unclear, so verify NVIDIA's terms for your use case. No known vulnerabilities. Medium install friction is typical for platform-specific NVIDIA packages.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12 runtime environment and a compatible NVIDIA GPU; only available for Linux (x86_64, aarch64) and Windows (x86_64).
- Medium install friction due to platform-specific wheel distributions (x86_64 Linux, aarch64 Linux, Windows).
- Package shows aging maintenance status with no recent commits tracked, though it receives regular version updates from NVIDIA.
License · maintenance · safety
LicenseRef-NVIDIA-Proprietary (unclear) — Licensed under NVIDIA's proprietary license (LicenseRef-NVIDIA-Proprietary). License treatment is unclear, so review NVIDIA's terms before integrating into commercial or redistributed projects.
last release 2025-06-05 (435 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 26,736,623 downloads/mo, #873 on PyPI
Alternatives
Verify before relying
pip install nvidia-nvjitlink-cu12
import nvidia.nvjitlink
# Use within CUDA-enabled GPU applications that require JIT compilation- Whether this package is intended for direct use or only as a transitive dependency of higher-level CUDA libraries.
- Specific use cases and API surface beyond JIT LTO functionality.
- Compatibility matrix with different CUDA toolkit versions and GPU architectures.
What it is and what it does
nvidia-nvjitlink-cu12 is NVIDIA's compiler library for just-in-time and link-time optimization in GPU code. It is a low-level runtime component typically used indirectly by higher-level CUDA frameworks and machine learning libraries rather than directly by application code. The package provides precompiled binaries for Linux and Windows platforms.
This is a foundational piece of the CUDA ecosystem, supporting dynamic compilation workflows where GPU kernels are compiled at runtime rather than ahead of time. It has no Python runtime dependencies and integrates with NVIDIA's broader CUDA 12 toolchain. The package is in beta status and shows aging maintenance, though NVIDIA continues to release updates.
Use it for
- As a transitive dependency of PyTorch, TensorFlow, or other frameworks that perform JIT compilation of GPU kernels.
- In custom CUDA applications that require runtime compilation and optimization of GPU code.
- Supporting dynamic kernel generation in machine learning frameworks that adapt computation graphs at runtime.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or running GPU-accelerated Python applications that depend on CUDA 12 JIT compilation.
Install it as a dependency of higher-level libraries rather than directly. The proprietary license is unclear, so verify NVIDIA's terms for your use case. No known vulnerabilities. Medium install friction is typical for platform-specific NVIDIA packages.
Install
nvidia-nvjitlink-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheel distributions (x86_64 Linux, aarch64 Linux, Windows). Package shows aging maintenance status with no recent commits tracked, though it receives regular version updates from NVIDIA.
Requires CUDA 12 runtime environment and a compatible NVIDIA GPU; only available for Linux (x86_64, aarch64) and Windows (x86_64).
License in practice
Licensed under NVIDIA's proprietary license (LicenseRef-NVIDIA-Proprietary). License treatment is unclear, so review NVIDIA's terms before integrating into commercial or redistributed projects.
Quickstart
pip install nvidia-nvjitlink-cu12
import nvidia.nvjitlink
# Use within CUDA-enabled GPU applications that require JIT compilationVerify before relying
- Whether this package is intended for direct use or only as a transitive dependency of higher-level CUDA libraries.
- Specific use cases and API surface beyond JIT LTO functionality.
- Compatibility matrix with different CUDA toolkit versions and GPU architectures.
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 | 26,736,623 / month, #873 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_nvjitlink_cu12-12.9.86-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl; nvidia_nvjitlink_cu12-12.9.86-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; nvidia_nvjitlink_cu12-12.9.86-py3-none-win_amd64.whl
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See also drjit · nvidia-nvjitlink · nvidia-cuda-nvrtc-cu12 · numba · nvidia-cuda-nvcc-cu12 · nvidia-nvvm · nvidia-cuda-nvrtc-cu11 · nvidia-nvfatbin · nvidia-cuda-nvcc · torch-c-dlpack-ext