NVIDIA cuSOLVER (CUDA 12)

Common errors

Warnings

Install compatibility stale last tested: 2026-05-12

Imports

• 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.

Quickstart stale last tested: 2026-04-24

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))