How do I check my CUDA version?

To check your CUDA version, the most direct and common method is to use the nvcc command in your terminal or command prompt.

How Do I Check My CUDA Version?

The most straightforward way to determine your installed CUDA Toolkit version is by executing a simple command in your command-line interface.

Method 1: Using `nvcc --version` (CUDA Toolkit Version)

This command provides the exact version of the CUDA Toolkit installed on your system. The CUDA Toolkit is essential for compiling CUDA-accelerated applications and is often what deep learning frameworks like TensorFlow and PyTorch rely on.

Steps:

Open your command-line interface:
- Windows: Open Command Prompt, PowerShell, or Anaconda Prompt.
- Linux/macOS: Open Terminal.
Execute the command: Type the following command and press Enter:
```
nvcc --version
```
If CUDA is correctly installed and its path is configured in your system's environment variables, you will see output similar to this:
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Wed_Nov_22_10:17:15_PST_2023
Cuda compilation tools, release 12.3, V12.3.107
Build cuda_12.3.r12.3/compiler.33567101_0
```
The line release 12.3, V12.3.107 indicates that your CUDA Toolkit version is 12.3.

What if nvcc is not found?

If you receive an error like "nvcc is not recognized as an internal or external command," it typically means:

CUDA Toolkit is not installed.
CUDA Toolkit is installed, but its binary directory (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\bin on Windows) is not added to your system's PATH environment variable.

Method 2: Using `nvidia-smi` (CUDA Driver Version)

While nvcc --version shows the CUDA Toolkit version, nvidia-smi (NVIDIA System Management Interface) provides information about your NVIDIA GPU driver and the maximum CUDA API version supported by that driver. This is crucial for ensuring compatibility between your driver and the CUDA Toolkit.

Steps:

Open your command-line interface.

Execute the command: Type nvidia-smi and press Enter.

You'll see a table with GPU information. Look for the CUDA Version: field in the header.

+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.154.05             Driver Version: 535.154.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3090         On | 00000000:01:00.0 Off |                  N/A |
| N/A   47C    P2    40W / 350W |    106MiB / 24576MiB |      0%      Default |
|                               |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

In this example, the CUDA Version: 12.2 indicates that your NVIDIA driver supports up to CUDA API version 12.2.

Important Distinction:

nvcc --version: Shows the version of the CUDA Toolkit installed on your system. This is what you compile your code with.
nvidia-smi: Shows the maximum CUDA API version supported by your GPU driver. Your CUDA Toolkit version should generally be less than or equal to the driver's supported CUDA version for optimal compatibility.

Why is Knowing Your CUDA Version Important?

Software Compatibility: Deep learning frameworks (like PyTorch, TensorFlow) and other GPU-accelerated applications often require specific CUDA Toolkit versions. Mismatched versions can lead to performance issues or errors.
Driver Compatibility: Ensuring your NVIDIA driver supports the CUDA Toolkit version you intend to use is critical. Older drivers might not support newer CUDA Toolkits.
Troubleshooting: When encountering GPU-related errors, knowing your exact CUDA setup is usually the first step in diagnosis.

Summary of Commands

Command	What it checks	Output Example (relevant part)
`nvcc --version`	CUDA Toolkit version (for compilation)	`release 12.3`
`nvidia-smi`	CUDA API version supported by GPU driver	`CUDA Version: 12.2`

By using these commands, you can accurately determine both your installed CUDA Toolkit version and the CUDA version supported by your GPU driver, ensuring your development environment is correctly configured. For the latest CUDA Toolkit downloads and documentation, visit the NVIDIA CUDA Toolkit website.

How do I check my CUDA version?