CUDA - The Tool That Made the GPU Accessible to Everyone

GPUs existed long before we started using them for AI. But until 2006, if you wanted to write code that ran on a GPU - you had to know OpenGL or DirectX, think like a graphics processor, and deal with specialized and complex languages.

NVIDIA changed that when it launched CUDA - and from that launch, the world of AI changed.

What is CUDA?

CUDA (Compute Unified Device Architecture) is a platform that lets you write code in C/C++/Python, and run it on thousands of GPU cores - in parallel.

Instead of dealing with graphics, you can simply write a regular function that computes matrix multiplication, and CUDA handles the rest: distributing work among thousands of cores, synchronization, and memory management.

How Did This Change the AI World?

Before CUDA, training neural networks was slow and cumbersome. After CUDA - PyTorch, TensorFlow appeared, and these monsters started using GPUs directly.

This was the key: you no longer need to know CUDA itself - you can write regular Python code, and it automatically runs 100x faster on the GPU.

What Does CUDA Give You?

• Massive parallel computing - Thousands of cores working together on the same task.
• Built-in libraries - cuBLAS (linear algebra), cuDNN (deep networks), TensorRT (optimization).
• Native support - PyTorch, TensorFlow, JAX, MXNet - all built on CUDA.
• Profiling and debugging - Tools like NSight that let you see what’s happening inside.

How Does It Work in Practice?

Let’s say you wrote in PyTorch:

x = torch.randn(1000, 1000).cuda()
y = torch.randn(1000, 1000).cuda()
z = x @ y  # matrix multiplication

Behind the scenes:

1. PyTorch calls CUDA’s cuBLAS library.
2. Data is transferred to GPU memory.
3. The multiplication is computed in parallel across thousands of cores.
4. The result returns to you.

All in a fraction of a second, without you needing to understand all the internal details.

Are There Alternatives to CUDA?

• ROCm - AMD’s, supports their cards.
• OneAPI - Intel’s, trying to create an open standard.
• OpenCL - Open, but less efficient and less supported.

But there’s no doubt: CUDA is still the de facto standard. Almost every AI library was built first for CUDA, and the official support is particularly strong.

Summary

CUDA is the bridge between the GPU and the developer. Without it, most of what we know today about AI simply wouldn’t have happened. And thanks to it, even a developer who knows nothing about hardware - can train a model in record time.

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In the next post, we’ll discuss the central concept in all AI infrastructure - what exactly is an “Accelerator”, and why without it, models simply don’t move.