Install & Compatibility
Where this runs
tested against v11.7.5.82 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 29.4s · import 0.000s · 2150.4MB
2150MB installed
● package 2150MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
N/A
✓ This package provides native shared libraries (e.g., .so, .dll) for cuSOLVER.
The `nvidia-cusolver-cu12` package is not designed for direct Python import of symbols. Instead, it serves as a runtime dependency, providing the underlying CUDA binaries that other Python libraries (like CuPy, PyTorch, or TensorFlow) dynamically load and link against to offer CUDA-accelerated functionalities.
This quickstart demonstrates how a higher-level library, CuPy, leverages the underlying cuSOLVER libraries provided by `nvidia-cusolver-cu12`. When installed, `cupy.linalg.solve` and other linear algebra functions can utilize cuSOLVER for GPU-accelerated computations. Ensure CuPy is also installed (`pip install cupy-cuda12x`).
import cupy as cp
# This example uses CuPy, which dynamically loads cuSOLVER routines
# via packages like nvidia-cusolver-cu12 if available.
# Create a random positive definite matrix A and a vector b on the GPU
A = cp.random.rand(5, 5, dtype=cp.float64)
A = A @ A.T + cp.identity(5) # Make it symmetric positive definite
b = cp.random.rand(5, dtype=cp.float64)
# Solve the linear system Ax = b using CuPy's linalg.solve
# CuPy internally dispatches to cuSOLVER routines for this operation.
x = cp.linalg.solve(A, b)
print("Matrix A:\n", A)
print("Vector b:\n", b)
print("Solution x:\n", x)
# Verify the solution (A @ x - b should be close to zero)
print("Verification (A @ x - b):\n", A @ x - b)
print("Norm of residual (should be close to zero):", cp.linalg.norm(A @ x - b))
Debug
Known issues
gotchaThe `nvidia-cusolver-cu12` package primarily provides native shared libraries and is not intended for direct Python import and use of symbols. Its functionality is exposed indirectly through other high-level Python libraries like CuPy, PyTorch, or TensorFlow.fixUse a Python library that integrates with cuSOLVER (e.g., CuPy, PyTorch) and ensure `nvidia-cusolver-cu12` is installed in the environment.
affects: All versions
breakingThe `cu12` suffix in the package name signifies compatibility with CUDA Toolkit 12.x. Installing this package with an incompatible CUDA Toolkit, GPU driver, or older GPU architecture can lead to runtime errors or performance issues.fixEnsure your NVIDIA GPU, driver, CUDA Toolkit installation, and all `nvidia-*` Python packages (e.g., `nvidia-cublas-cu12`, `nvidia-cuda-runtime-cu12`) are consistent with the `cu12` version suffix. Major CUDA version changes (e.g., `cu11` to `cu12`) often require a complete reinstallation of all CUDA-related components and potentially recompilation of CUDA-dependent code.
affects: All versions
gotchaPip's dependency resolver can struggle with the complex interdependencies among `nvidia-*` packages, leading to slow resolution times or conflicts.fixWhen installing multiple `nvidia-*` packages or libraries that depend on them, explicitly pin exact versions in your `requirements.txt` or install order. For example, `pip install 'nvidia-cusolver-cu12==11.7.5.82'`.
affects: All versions
deprecatedCertain cuSOLVER APIs, particularly within the `cuSOLVERMg` (multi-GPU) and some `cuSOLVERSp` (sparse) modules, have been deprecated in recent CUDA Toolkit versions.fixRefer to the NVIDIA CUDA Toolkit documentation for your specific CUDA version to identify deprecated APIs and their recommended replacements. Ensure your application code is updated to use current cuSOLVER APIs.
affects: Specific APIs in CUDA Toolkit 12.x and newer
breakingMany `nvidia-*` Python packages are hosted on the NVIDIA Python Package Index, not directly on PyPI.org. Attempting to install them without configuring pip to use the NVIDIA index will result in a `RuntimeError` indicating the package is a placeholder.fixBefore installing `nvidia-*` packages, install `nvidia-pyindex` to configure pip to use the NVIDIA Python Package Index. The installation command should typically look like: `pip install nvidia-pyindex && pip install <your-nvidia-package>`.
affects: All versions of `nvidia-*` packages distributed via NVIDIA Python Package Index.
Upgrade
Version history
11.7.5.82latest on PyPI · released Jun 5, 2025
Audit
Dependencies
nvidia-cublas-cu12requiredcuSOLVER functionality often relies on cuBLAS for basic linear algebra operations.
nvidia-cusparse-cu12requiredcuSOLVER includes sparse solvers, which may depend on cuSPARSE.
nvidia-nvjitlink-cu12requiredProvides NVIDIA JIT LTO (Link-Time Optimization) library, a common dependency for CUDA runtime components.
NVIDIA GPU and driverrequiredNative CUDA libraries require a compatible NVIDIA GPU and an installed NVIDIA driver.
CUDA ToolkitoptionalWhile this package provides runtime libraries, a full CUDA Toolkit installation is often required for development or for specific dependent libraries.