NVIDIA - How a Graphics Card Company Became the Queen of AI

When talking about modern artificial intelligence, you can’t ignore NVIDIA. The company that dominates the graphics card market has become a powerhouse in the AI world - and not by accident: it built the strongest ecosystem in the industry.

Where Did It All Start?

NVIDIA was founded in 1993 as a company that produced graphics cards for computer games. The GPU (Graphics Processing Unit) it developed was designed to render images quickly - but it turned out that the same parallel processing capability used for pixels also works for massive mathematical computations - especially machine learning.

NVIDIA’s DNA: Hardware + Software

NVIDIA doesn’t just sell chips. It sells a complete system:

• Powerful GPUs (A100, H100, and more)
• CUDA - A programming platform that makes it easy to implement algorithms on the GPU
• cuDNN, TensorRT - Optimization libraries for models
• Full support for PyTorch, TensorFlow, JAX - Everyone working in AI already uses their tools

All these components create a closed and highly efficient ecosystem.

The H100 - The New Monster

Today, NVIDIA’s leading GPU for AI training is the H100 Tensor Core. Its capabilities:

• 60 trillion operations per second (in FP8 precision)
• Ultra-fast memory (HBM3)
• Support for Transformer Acceleration technologies - especially for LLMs

A server with 8× H100 can cost over a million dollars, but it can also train massive models in days instead of weeks.

The Monopoly and Criticism

NVIDIA currently controls over 90% of the AI accelerator market. Some argue that the lack of competition is harmful, but there’s no denying that their ecosystem - the tools, documentation, support - is still the most sophisticated.

Competitors Are Rising - But Slowly

• AMD - With the MI300 series, but the ecosystem is weaker.
• Intel - Trying with Gaudi, but still early.
• AWS Trainium / Inferentia - Amazon’s accelerators, but only within their cloud.
• Google TPU - Strong for training, but also closed to the organization.

Still, NVIDIA remains the de facto standard.

Why Does This Matter to You as a Developer?

If you work in AI - chances are you’re writing code that runs on NVIDIA GPUs. Even if you don’t know it - PyTorch translates your commands through CUDA, and your model runs on some NVIDIA card, in the cloud or on a dedicated server.

Summary

NVIDIA doesn’t just lead AI - it set the rules. Thanks to the general-purpose GPU it developed, it became a central player in a world where every computation matters. And right now, it’s hard to imagine AI without NVIDIA.

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In the next post, we’ll learn about the tool that made the difference - CUDA, the programming platform that made the GPU accessible to every developer.