A tutorial-style introduction to NVIDIA — the company, the hardware, and the ideas that turned a graphics card maker into the backbone of the AI industry. Built for readers new to GPUs, CUDA, or AI infrastructure.

By the end of this post, you’ll understand where NVIDIA came from, what a GPU actually is and why it’s built for parallel work, how that compares to a CPU, and the three specific reasons NVIDIA — not a competitor — became the AI industry’s default hardware.

NVIDIA was founded in 1993 by Jensen Huang and his co-founders. In its early decades, the company had one focus: graphics cards (GPUs) for video games.

Here’s the timeline of NVIDIA’s foundational hardware moments, from founding through its early gaming-era peak:

Jensen Huang himself became a recognizable figure in tech — known for his signature leather jacket at product keynotes, much like how Steve Jobs became associated with a black turtleneck.

The stock move that put NVIDIA in the headlines

Here’s the number everyone talks about: NVIDIA’s stock rose 1,100% over a five-year period. That wasn’t driven by gaming — it was driven by the AI boom, and specifically by demand for GPUs to train and run large language models (LLMs). A company once measured by frame rates is now measured by how many AI data centers it powers.

Growth through acquisition

NVIDIA didn’t get here organically alone — a string of acquisitions expanded its capabilities well beyond graphics rendering. Here’s the acquisition trail visualized, split by era:

A Graphics Processing Unit (GPU) is a processor built to render high-resolution images and video — quickly, and concurrently across many pixels and calculations at once.

That “many things at once” design is the entire reason GPUs became useful outside of gaming. GPUs excel at parallel operations across multiple sets of data, which is exactly what’s needed for machine learning and scientific computation.

Sequential vs. parallel processing, visualized

Before comparing chip architecture, it helps to see the core idea of parallelism in action — completing tasks one after another versus completing many at once:

This single idea — doing many small things at the same time instead of one thing very fast — is the foundation for everything else in this post.

The core-count difference, visualized

Now apply that idea to real chip design. The easiest way to understand the CPU-vs-GPU difference is to look at how many processing cores each one packs, and how those cores are designed:

What GPUs are best at

Because of this core design, GPUs excel at repetitive, highly parallel workloads such as:

• Rendering graphics
• Cryptocurrency mining
• Deep learning and machine learning

Why the architecture is different under the hood

It’s not just “more cores” — the underlying chip design philosophy is different:

• CPUs reserve significant die area for large caches, control units, and other overhead. Their job is to minimize latency for each individual thread — meaning a CPU wants to finish any single task as fast as possible.
• GPUs dedicate the overwhelming majority of their transistors to raw data processing. Rather than minimizing latency for one thread, GPUs hide instruction and memory latency by running enormous numbers of threads simultaneously — so while some threads wait, others are actively computing.

Think of it like this: a CPU is a small team of highly skilled generalists, each working quickly on their own complex task. A GPU is an enormous team of simple specialists, each doing one small repetitive job — but there are thousands of them working at the same moment.

Plenty of companies have made GPUs over the years (AMD being the most obvious competitor). So why did NVIDIA specifically become the dominant force in AI computing? It comes down to three compounding factors:

Software platform → ecosystem adoption → purpose-built hardware. That chain is what gave NVIDIA a multi-year head start that competitors are still trying to close.