Published on

CUDA on Nvidia MX130 GPU


Welcome to this guide on how to enable CUDA on an Nvidia MX130 GPU for machine vision inference on laptops. CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs).

The Nvidia MX130 is a dedicated GPU that is commonly found in laptops, it is not as powerful as the RTX GPU families, but it can still run some machine vision tasks efficiently. In this guide, we will cover the installation of necessary drivers and software, as well as any configuration required to properly utilize the GPU for these types of computations using Pytorch. Keep in mind that the mileage may vary when using other frameworks.

At the end, I will demo the performance using SuperGlue's demo script.

Checking the GPU you have

Usually, laptop with NVIDIA GPU already preinstalled with NVIDIA drivers along with NVIDIA Control Panel. So, open that to see your GPU version.

Nvidia Control Panel Driver

I have MX130 drivers. From the specs page, it says it can support CUDA. If you have other drivers, you can still follow along this guide but your mileage may vary.

Optionally, you can verify the CUDA supports using Python (You need Python 3.9.x version, as PyTorch doesn't support the latest Python version yet).

from numba import cuda # run pip install numba


Found 1 CUDA devices
id 0    b'NVIDIA GeForce MX130'                 [SUPPORTED (DEPRECATED)]
                      Compute Capability: 5.0
                           PCI Device ID: 0
                              PCI Bus ID: 1
                                    UUID: GPU-7b092133-34da-571d-9506-9de68403ed55
                                Watchdog: Enabled
                            Compute Mode: WDDM
             FP32/FP64 Performance Ratio: 32
	1/1 devices are supported

Process finished with exit code 0

Checking CUDA availability using PyTorch

Even though the script above says that our GPU supports CUDA, the PyTorch still cannot 'see' the GPU yet.


I recommend you to create virtual environment to easily manage your packages and python version per project

Try running the script below:

import torch as torch # pip install torch

yes_cuda = torch.cuda.is_available()



So, we need to install some tools to make our CUDA visible to PyTorch.

Install Visual Studio

You may need to install Visual Studio 2022 to correctly install CUDA.

Not to be confused with Visual Studio Code

But for my case, I don't install the full VS due to storage constraints, but I have the VS Build Tools already installed with C++ development. I just downloaded Microsoft Visual C++ Redistributable for Visual Studio 2022 just in case. Visit Visual Studio download page for more info.

VS Redistributable

Setup CUDA

Download supported CUDA version

The latest CUDA version is 12.0. However (at the time of writing), PyTorch seems doesn't have support for it yet. So, you'll need an older version of CUDA (11.6 or 11.7). Go to Cuda Toolkit Archives to download version 11.7.1.

You may select Installer Type to local, and proceed with downloading.

CUDA Download

Install CUDA

Once the file downloaded, double click it to begin installation.

Click the Express Installation. It will install the CUDA toolkit, some other things, and your display driver.

CUDA Installation type express

When the installation finishes. I got notified about trouble installing Nsight.

CUDA Nsight not installed

As the description said, it may not be related to CUDA so just hit Next and complete the installation.

CUDA install complete

Verify the installation

Open Command Prompt or Powershell, run the following command:


Setup Pytorch (correctly)

Go to PyTorch's Get Started page. Select the setting accordingly, copy and run the command generated.

PyTorch download selector

You may need to uninstall the existing PyTorch installation (pip uninstall torch) before running the command below.


pip3 install torch torchvision torchaudio --extra-index-url

If you re-run the code previously. The output should be True indicating that PyTorch is now able to recognise the CUDA GPU.


I'm using the demo of the machine vision project SuperGlue Inference and Evaluation Demo Script without CUDA (using CPU) and with CUDA running on MX130.

Your result may vary

Without CUDA

Running inference on device "cpu"

Average FPS = 0.4


Running inference on device "cuda"

Average FPS = 1.0

The performance with CUDA has improved, but it's important to note that it may not achieve significantly higher FPS due to the GPU's limitations in handling intensive calculations.