CUDA is the parallel computing platform and programming model from NVIDIA to run tasks on compatible GPUs. It brings together tools, interfaces and libraries that help a program distribute calculations across many GPU cores. NVIDIA's official documentation describes CUDA as a platform to accelerate compute intensive applications.
How it works
In a CUDA program, the CPU usually coordinates the application and the GPU runs parts of the work that can be split into many similar operations. These parallel operations are written in functions called kernels. The programmer defines how many instances of the kernel will run and how they access the data.
A kernel can, for example, add each pair of numbers from two vectors. Instead of processing one pair at a time, the application distributes several pairs across thousands of GPU threads. Threads are lightweight execution units; they are organized in groups to share work and coordinate operations.
CUDA is not a video card nor an artificial intelligence model. It is a software layer that lets you take advantage of compatible hardware resources. The ecosystem also includes a compiler, libraries and diagnostic tools. Numerical libraries let you use optimized operations without writing each kernel by hand.
Data transfer between the computer's main memory and GPU memory also matters. Sending too much data or making repeated small transfers can reduce the expected gain. For that reason, speeding up an operation depends as much on parallelization as on data flow.
Practical example
Imagine a service that needs to multiply large matrices during model inference. A matrix organizes values in rows and columns, and multiplication requires many numeric operations. On a GPU, independent parts of that calculation can run at the same time.
In practice, someone using a deep learning framework may not write CUDA code directly. The framework can call low level libraries that use CUDA when they detect a compatible GPU. A team that creates a specialized operation, on the other hand, can write a kernel to reduce steps or adapt the calculation to the application's data.
The same principle appears in image processing, scientific simulation and numerical analysis. The example does not mean every task will be faster on the GPU: small, sequential activities or those limited by input and output may not benefit from parallelism.
What it's for
CUDA is for developing and running applications that leverage NVIDIA GPUs for compute intensive tasks. It is common in deep learning, model training and inference, scientific computing and processing large volumes of data.
For AI software developers, understanding the role of CUDA helps investigate why a library requires a specific GPU, why an operation uses a lot of memory or why GPU usage does not automatically improve execution time. It also helps separate three layers: the AI model, the framework that implements its calculations and the hardware that runs them.
CUDA is especially relevant when you need to control processing on the GPU. For many projects, however, it is enough to install a compatible library and use its high level operations. The level of control should match the need: starting with the framework is usually simpler; writing kernels makes sense when there is a specific operation that needs to be optimized or studied.