CUDA Explained: Why NVIDIA Dominates AI

NVIDIA is currently one of the most valuable companies in the world. While their H100 and Blackwell GPUs are marvels of engineering, hardware is only half the story. The real secret to their monopoly is a piece of software released in 2006 called CUDA.

What is CUDA?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model. Put simply, it allows developers to use a GPU (Graphics Processing Unit) for general-purpose processing (GPGPU), not just for rendering video games.

Before CUDA, using a GPU for math was a nightmare. You had to trick the GPU into thinking your math problem was a “pixel shading” task. CUDA allowed developers to write C++ code that ran directly on the GPU’s thousands of cores.

The Moat: Why It Matters

1. The Network Effect

Because NVIDIA released CUDA early and made it free for students and researchers, the entire academic community adopted it.

• 2012: AlexNet (the big bang of Deep Learning) was written in CUDA.
• TensorFlow & PyTorch: The two biggest AI frameworks were built on top of CUDA primitives.

If you are an AI researcher today, you write PyTorch code. PyTorch talks to CUDA. CUDA talks to the NVIDIA GPU. If you want to use an AMD or Intel chip, you need a translation layer (like ROCm), which—historically—has been buggy or slower.

2. The Library Ecosystem

It’s not just the language; it’s the libraries built with it.

• cuBLAS: Highly optimized basic linear algebra subroutines (matrix math).
• cuDNN: The deep neural network library that powers almost every training run.
• TensorRT: Optimizes models for inference.

NVIDIA has spent 15 years hand-optimizing these libraries for every specific chip architecture they release. A competitor doesn’t just need to build a better chip; they need to rewrite 15 years of software optimization.

How CUDA Works (Simplified)

A CPU has a few powerful cores (like a Ferrari). A GPU has thousands of weaker cores (like a fleet of scooters). CUDA allows you to break a massive problem (like multiplying two giant matrices) into thousands of tiny pieces.

// Extremely simplified CUDA Kernel concept
__global__ void vectorAdd(float *A, float *B, float *C, int N) {
    int i = threadIdx.x; // Get the ID of the current thread
    if (i < N) {
        C[i] = A[i] + B[i]; // Each thread adds just ONE number
    }
}

Instead of a for loop running 10,000 times (CPU), CUDA launches 10,000 threads that all run this code at the exact same time.

Can the Moat be Crossed?

For the first time, cracks are appearing in the CUDA wall.

1. PyTorch 2.0 / Triton: OpenAI’s Triton language allows writing GPU code that compiles to both NVIDIA and AMD chips, bypassing CUDA.
2. AMD ROCm: AMD’s answer to CUDA is finally maturing, supporting major frameworks on the MI300X.
3. Mojo: A new programming language by Modular that aims to be faster than CUDA and hardware agnostic.

However, for now, if you want “it just works” compatibility, NVIDIA + CUDA is still the default.