{"id":3183,"library":"nvidia-cuda-runtime-cu11","title":"NVIDIA CUDA Runtime (cu11)","description":"The `nvidia-cuda-runtime-cu11` package provides the native CUDA Runtime libraries for Python applications. It acts as a foundational component, offering Cython/Python wrappers for CUDA driver and runtime APIs, enabling higher-level Python libraries to leverage NVIDIA GPUs. This is a low-level runtime dependency rather than a library with direct user-facing Python APIs. The current version is 11.8.89. It is actively maintained by NVIDIA.","status":"active","version":"11.8.89","language":"en","source_language":"en","source_url":"https://pypi.org/project/nvidia-cuda-runtime-cu11/","tags":["cuda","nvidia","gpu","runtime","machine-learning","deep-learning","accelerated-computing"],"install":[{"cmd":"pip install nvidia-pyindex && pip install nvidia-cuda-runtime-cu11","lang":"bash","label":"Install with pip"}],"dependencies":[{"reason":"Required for PyPI to fetch additional NVIDIA Python modules.","package":"nvidia-pyindex","optional":false}],"imports":[],"quickstart":{"code":"import os\n\n# This package primarily provides runtime libraries.\n# To verify successful installation and CUDA availability in a Python environment,\n# you typically check via a framework that utilizes CUDA, like PyTorch.\n# Ensure 'torch' is installed (e.g., pip install torch --index-url https://download.pytorch.org/whl/cu118)\n\ntry:\n    import torch\n    if torch.cuda.is_available():\n        print(f\"CUDA is available! Device name: {torch.cuda.get_device_name(0)}\")\n    else:\n        print(\"CUDA is not available according to PyTorch.\")\nexcept ImportError:\n    print(\"PyTorch not installed. Install it to verify CUDA availability:\")\n    print(\"pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\")\nexcept Exception as e:\n    print(f\"An error occurred while checking CUDA with PyTorch: {e}\")","lang":"python","description":"This package doesn't expose direct Python classes or functions for general use. Its presence enables other CUDA-aware Python libraries (like PyTorch or TensorFlow) to utilize the GPU. This quickstart demonstrates how to verify CUDA availability using PyTorch, a common library that depends on CUDA runtime libraries."},"warnings":[{"fix":"Do not attempt to 'import nvidia_cuda_runtime_cu11' for direct programming. Instead, rely on higher-level libraries built on CUDA Python.","message":"This package is a low-level runtime dependency. It does not provide direct Python APIs for coding. Its primary function is to make the CUDA Runtime libraries available for other Python frameworks (e.g., PyTorch, TensorFlow, Numba) that perform GPU-accelerated computing.","severity":"gotcha","affected_versions":"All"},{"fix":"Ensure `pip install nvidia-pyindex` is run before installing `nvidia-cuda-runtime-cu11`, or use `--extra-index-url https://pypi.ngc.nvidia.com` during installation.","message":"Installing `nvidia-cuda-runtime-cu11` (and other `nvidia-cuda-*` packages) from PyPI requires the `nvidia-pyindex` package to be installed first, or specifying the NVIDIA PyPI index URL. Without it, `pip` may fail to find the package.","severity":"breaking","affected_versions":"All"},{"fix":"Verify your system has a CUDA-capable NVIDIA GPU and the correct driver version is installed and up-to-date for your CUDA toolkit version.","message":"A compatible NVIDIA GPU and an appropriately installed NVIDIA driver are prerequisites. This Python package only provides the software runtime; it does not install the GPU driver or ensure hardware compatibility.","severity":"gotcha","affected_versions":"All"},{"fix":"For development workflows requiring CUDA compilers or other tools, install the full NVIDIA CUDA Toolkit from the official NVIDIA developer website alongside these runtime Python packages.","message":"These PyPI packages (`nvidia-cuda-runtime-cu11`, etc.) are intended for runtime use and do not include developer tools such as `nvcc` (the CUDA compiler). For CUDA development (e.g., compiling custom CUDA kernels), the full CUDA Toolkit installation is typically required.","severity":"gotcha","affected_versions":"All"},{"fix":"Aim for a consistent CUDA environment. If using PyPI wheels, be mindful of how they interact with existing system or Conda CUDA installations. Consider using virtual environments.","message":"Mixing CUDA versions from different installation methods (e.g., system-wide CUDA Toolkit, Conda, and PyPI wheels) can lead to environment conflicts and unexpected errors.","severity":"gotcha","affected_versions":"All"}],"env_vars":null,"last_verified":"2026-04-11T00:00:00.000Z","next_check":"2026-07-10T00:00:00.000Z"}