Best Tools and Resources to Buy in January 2026
Glorto GeForce GT 730 4G Low Profile Graphics Card, 2X HDMI, DP, VGA, DDR3, PCI Express 2.0 x8, Entry Level GPU for PC, SFF and HTPC, Compatible with Windows 11
- EXPERIENCE SMOOTH MULTITASKING WITH 4-SCREEN NVIDIA SURROUND SUPPORT.
- EFFORTLESS COMPATIBILITY WITH WIN11 AND AUTOMATIC DRIVER UPDATES.
- IDEAL FOR COMPACT BUILDS: LOW PROFILE DESIGN FITS ANY PC SETUP.
GeForce GT 610 2G DDR3 Low Profile Graphics Card, PCI Express 1.1 x16, HDMI/VGA, Entry Level GPU for PC, SFF and HTPC, Compatible with Win11
- ENHANCED GRAPHICS PERFORMANCE WITH NVIDIA GEFORCE GT 610 CHIP.
- PLUG-AND-PLAY COMPATIBILITY WITH WINDOWS 11, NO DRIVER HASSLE.
- DUAL-OUTPUT: HDMI & VGA WITH STUNNING HIGH RESOLUTIONS.
Mllse GeForce GTX 750 Ti Graphics Card, 4GB GDDR5, 128-Bit, PCIe 3.0 x16, DirectX 12, 640 CUDA Cores, Low Power Single Fan GPU for Gaming & Work
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POWERFUL PERFORMANCE WITH LOW POWER CONSUMPTION FOR ENERGY SAVINGS!
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SMOOTH GRAPHICS AND FAST RENDERING FOR GAMING AND PROFESSIONAL USE.
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VERSATILE CONNECTIVITY SUPPORTS MULTI-DISPLAY SETUPS EFFORTLESSLY!
PNY NVIDIA T400 Professional Graphic Card 2GB GDDR6 PCI Express 3.0 x16, Single Slot, 3x Mini-DisplayPort, 8K Support, Ultra-quiet active fan
- POWERFUL NVIDIA T400 GPU WITH 384 CUDA CORES FOR OPTIMAL PERFORMANCE.
- 2GB GDDR6 MEMORY ENSURES FAST, SMOOTH GRAPHIC PROCESSING.
- EASY CONNECTIVITY WITH PCIE 3.0 AND 3 MINI-DISPLAYPORT ADAPTERS INCLUDED.
GeForce GT 610 2G DDR3 Low Profile Graphics Card, PCI Express 1.1 x16, HDMI/VGA/DVI, Entry Level GPU for PC, SFF and HTPC, Compatible with Win11
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SEAMLESS WINDOWS 11 COMPATIBILITY: NO MANUAL DRIVER SETUP REQUIRED!
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VERSATILE OUTPUT: 3 PORTS WITH STUNNING 2560X1600 RESOLUTION!
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IDEAL FIT: LOW PROFILE FOR ALL PC TOWERS & SMALL FORM FACTOR BUILDS!
BKFK 4K 120Hz HDMI 2.1-Compatible Dummy Plug, Headless Ghost Display Emulator Adapter for GPU, Remote Desktop, Servers, Cloud Gaming, VR Testing 3840x2160@120HZ,1440P@120HZ,1080P@120HZ(SDR)
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BOOST GPU PERFORMANCE: MAINTAIN FULL RESOLUTION FOR OPTIMAL COMPUTING TASKS.
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SEAMLESS REMOTE ACCESS: EXPERIENCE SHARP 4K@120HZ GAMING AND WORK REMOTELY.
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ECO-FRIENDLY SOLUTION: COST-EFFECTIVE, ENERGY-SAVING ALTERNATIVE TO MONITORS.
GeForce GT 740 4G DDR3 Low Profile Graphics Card, PCI Express 3.0 x16, HDMI/VGA/DVI, Entry Level GPU for PC, SFF and HTPC, Compatible with Win11
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POWERFUL NVIDIA GT 740 WITH 4GB DDR3 FOR ENHANCED GAMING PERFORMANCE.
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SUPPORTS MULTIPLE OUTPUTS: HDMI, VGA, DVI FOR VERSATILE CONNECTIVITY.
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COMPATIBLE WITH WIN11 FOR SEAMLESS INSTALLATION AND DRIVER UPDATES.
PNY NVIDIA T600
- DOUBLE PERFORMANCE BOOSTS FOR 3D GRAPHICS WORKFLOWS WITH T600.
- EFFORTLESSLY HANDLE LARGER MODELS FOR ENHANCED DESIGN INTERACTIVITY.
- SUPPORTS 4X 5K DISPLAYS, ENABLING IMMERSIVE MULTI-MONITOR SETUPS.
BKFK 4K 120Hz HDMI 2.1-Compatible Dummy Plug, Headless Ghost Display Emulator Adapter for GPU, Remote Desktop, Servers, Cloud Gaming, VR Testing 3840x2160@120HZ,1440P@120HZ,1080P@120HZ(SDR)
- ENABLE 4K@120HZ GAMING & REMOTE WORK WITHOUT A PHYSICAL MONITOR!
- KEEP YOUR GPU PERFORMING AT ITS BEST-NO LOW-RESOLUTION DROPS!
- COST-EFFECTIVE, ECO-FRIENDLY SOLUTION FOR SERVERS AND PCS ALIKE!
To install TensorFlow and CUDA drivers, first ensure that your GPU is compatible with TensorFlow and CUDA. Next, download and install the appropriate version of CUDA drivers for your system. After installing CUDA drivers, download and install TensorFlow using pip or anaconda. Make sure to install the GPU version of TensorFlow to take advantage of your GPU for accelerated computation. Finally, test your installation by running a simple TensorFlow program that utilizes your GPU.
How to set up a virtual environment for TensorFlow?
To set up a virtual environment for TensorFlow, you can use tools like virtualenv or Conda. Here's a step-by-step guide using virtualenv:
- First, make sure that you have Python installed on your system. You can check this by running the command python --version in your terminal.
- Install virtualenv if you don't already have it by running the command pip install virtualenv.
- Create a new virtual environment by running the command virtualenv tensorflow_env.
- Activate the virtual environment by running the command source tensorflow_env/bin/activate on Linux or tensorflow_env\Scripts\activate on Windows.
- Install TensorFlow in the virtual environment by running the command pip install tensorflow.
- You can now start coding with TensorFlow in your virtual environment. When you're done working with TensorFlow, you can deactivate the virtual environment by running the command deactivate.
By setting up a virtual environment for TensorFlow, you can isolate your TensorFlow project's dependencies and ensure that they don't interfere with other Python projects on your system. This helps keep your development environment clean and organized.
How to check if TensorFlow is using the GPU?
To check if TensorFlow is using the GPU, you can follow these steps:
- Import TensorFlow and check the available devices:
import tensorflow as tf
devices = tf.config.list_physical_devices('GPU') print("Available GPUs:", devices)
This will print the list of available GPUs that TensorFlow can use.
- Check if TensorFlow is currently using the GPU:
print("Is TensorFlow using GPU:", tf.test.is_built_with_cuda() and tf.test.is_gpu_available())
This will print whether TensorFlow is currently using the GPU or not.
If both of the above steps indicate that TensorFlow is using the GPU, then it is configured to use the GPU for computations.
What is the easiest way to install TensorFlow on a Mac?
The easiest way to install TensorFlow on a Mac is using pip, the Python package manager. Here are the steps to install TensorFlow using pip:
- Make sure you have Python installed on your Mac. You can download and install the latest version of Python from the official Python website.
- Open a terminal window on your Mac.
- Install TensorFlow by running the following command:
pip install tensorflow
- Once the installation is complete, you can verify the installation by importing TensorFlow in a Python script or a Jupyter notebook and running some sample code.
That's it! You have successfully installed TensorFlow on your Mac using pip.
How to install CUDA drivers on Windows?
Here are the steps to install CUDA drivers on Windows:
- Go to the NVIDIA CUDA download page: https://developer.nvidia.com/cuda-downloads and select your operating system (in this case, Windows).
- Under "Select Target Platform", choose your version of Windows (e.g. Windows 10).
- Under "Select the version" select the version of CUDA Toolkit you want to download. Once selected, click on the "Download" button to start the download.
- Once the download is complete, double click on the downloaded file to start the installation process.
- Follow the on-screen instructions to complete the installation.
- After the installation is complete, restart your computer to apply the changes.
- To verify that the CUDA drivers have been successfully installed, you can go to the NVIDIA Control Panel on your computer. Under the "Help" menu, click on "System Information" and you should see information about the CUDA driver version installed on your system.
That's it! You have successfully installed CUDA drivers on your Windows computer.