GPU Guide · Part 1 of 3 | July 1, 2026

What is a GPU? From Arcade Games to Artificial Intelligence – A Simple History

So What Exactly is a GPU?

Let me explain this in the simplest way possible.

Your computer has a brain called the CPU (Central Processing Unit). The CPU is like a highly educated professor — he can solve very complex problems, but he can only do one or two things at a time.

The GPU (Graphics Processing Unit) is the opposite. It is like 1000 school students — each student is not as smart as the professor, but put them together and they can do 1000 simple calculations at the same time. That is why GPUs are called "massively parallel" processors.

In plain English: CPU is for general work. GPU is for doing the same kind of math thousands of times, very fast.

Originally, GPUs were made only for showing graphics on your screen — games, videos, Windows desktop. But today, they are used for everything from AI to video editing to mining Bitcoin.

A Short History: How We Got Here

1990s – The Beginning (2D and Early 3D)

In the early 90s, computers could only show basic 2D graphics. If you wanted to play a game, the CPU had to do all the work. The result? Slow, ugly graphics. Then companies like 3dfx and NVIDIA came out with "graphics accelerators" — separate cards that could handle graphics so the CPU could breathe.

The first big name was 3dfx Voodoo in 1996. If you were a gamer in those days, you knew the name Voodoo. It was the first card that made 3D games look good. But Voodoo only did 3D — you still needed a separate 2D card for the desktop.

NVIDIA released the RIVA 128 in 1997 and then the RIVA TNT in 1998. These cards could handle both 2D and 3D in one card. This was a big deal.

1999 – The Term "GPU" is Born

In 1999, NVIDIA released the GeForce 256. This was the first card they called a "Graphics Processing Unit" or GPU. Until then, cards were just "graphics accelerators." The GeForce 256 could do something called "hardware T&L" (Transform and Lighting) — basically, it could calculate 3D lighting effects on its own instead of asking the CPU. This was revolutionary.

Think of it like this: Before GeForce 256, the CPU had to tell the graphics card exactly where every light and shadow goes. After GeForce 256, the graphics card could figure it out on its own. Games became faster and more detailed overnight.

2000s – The Era of Gaming GPUs

The 2000s was the golden age of graphics cards. Every year, NVIDIA and ATI (which was later bought by AMD) released faster cards. Games like Doom 3, Half-Life 2, and Crysis pushed GPUs to their limits.

Key moments:

• 2000: NVIDIA GeForce 2 Ultra — first GPU to reach 1 Gigaflop (one billion calculations per second)
• 2002: ATI Radeon 9700 Pro — many people say this was the best card of its time, better than anything NVIDIA had
• 2006: NVIDIA GeForce 8800 GTX — a monster card that set the standard for high-end gaming for years
• 2007: AMD (now owning ATI) released the Radeon HD 2000 series

During this time, GPUs were mostly for gaming and professional graphics (like AutoCAD or 3ds Max for architects and engineers). Nobody imagined they would one day power artificial intelligence.

2007 – The Big Change: CUDA Changes Everything

In 2007, NVIDIA released something called CUDA (Compute Unified Device Architecture). This is one of the most important moments in tech history, and most people don't know about it.

What CUDA did: Before CUDA, GPUs could only render graphics. You could not use them for anything else. CUDA allowed programmers to use the GPU for general-purpose computing — meaning you could use the GPU for math, science, engineering, and any kind of calculation, not just games.

This was like suddenly discovering that your car engine could also power your house. The GPU was no longer just for gaming. It became a computing workhorse.

AMD responded with Stream and later OpenCL, but CUDA became the standard because it was easier for programmers to use. This decision — making CUDA free and easy for developers — is the main reason why NVIDIA dominates the AI market today.

2010s – GPUs Go to Work (AI, Mining, Video Editing)

Once CUDA opened the door, everyone wanted to use GPUs for everything:

• AI & Deep Learning: In 2012, a team used NVIDIA GPUs to build AlexNet, an AI that could recognize images better than any computer before. This was the moment AI researchers realized GPUs were the key to making smart machines. Today, every AI — from ChatGPT to Google Gemini to Midjourney — runs on GPUs.
• Bitcoin & Crypto Mining: Around 2011, people discovered that GPUs were great at mining cryptocurrencies. This caused GPU prices to explode. Gamers were angry because they couldn't find cards at normal prices.
• Video Editing: Software like Adobe Premiere Pro and DaVinci Resolve started using GPUs to render videos faster. A good GPU can cut video export time from hours to minutes.
• 3D Rendering: Architects, game developers, and VFX artists started using GPU-based renderers like Octane and Redshift because they were 10-50x faster than CPU rendering.

2018 – Ray Tracing Arrives

In 2018, NVIDIA released the RTX 2000 series with a new technology called Ray Tracing. This was a big deal for gaming. Ray Tracing simulates how light actually behaves in the real world — light bouncing off surfaces, shadows that are soft, reflections that look real.

Before RTX, games used "fake" lighting tricks that looked okay but not real. RTX made games look like Hollywood movies. The downside? It was very slow at first — only the most expensive cards could do it well.

Along with Ray Tracing, NVIDIA introduced DLSS (Deep Learning Super Sampling) — a technology that uses AI to make games run faster while looking just as good. DLSS was clever: instead of rendering the full 4K image (which is very hard for the GPU), the GPU renders a lower resolution image and AI fills in the missing details. The result? You get 4K quality with 1080p performance.

2020s – Intel Joins, AI Explodes

In 2022, Intel entered the GPU market with Arc Alchemist. For 25 years, it was just NVIDIA vs AMD. Now there are three players. Intel's first cards were not perfect — drivers had bugs, performance was inconsistent — but for the price, they offered good value.

Meanwhile, AI exploded. In 2022-2023, tools like ChatGPT, Stable Diffusion, Midjourney became mainstream. All of them run on GPUs — specifically NVIDIA GPUs with lots of VRAM. Suddenly, even normal people wanted powerful GPUs, not just gamers.

NVIDIA became one of the most valuable companies in the world. Their H100 and A100 data center GPUs cost $30,000 each and companies like Microsoft, Google, and Amazon buy them by the thousands to run AI services.

Today in 2026, we have the RTX 5000 series from NVIDIA, Radeon RX 8000 series from AMD, and Intel Arc Battlemage from Intel. GPUs are more powerful and more important than ever.

Why Does This Matter to You?

If you are reading this on AadhiPC's blog, you are probably thinking about building or buying a PC. Here is why understanding GPUs matters:

• For office/shop use: You don't need a separate GPU. The CPU's built-in graphics (called "integrated graphics") is enough for Tally, Excel, browsing, and billing.
• For gaming: You absolutely need a GPU. Modern games will not run without one.
• For video editing / 3D work: A good GPU will save you hours of waiting. A ₹15,000 GPU can make rendering 5x faster than CPU alone.
• For AI / Machine Learning: You need NVIDIA with lots of VRAM (at least 8GB, ideally 12GB+ for serious work). This is why AI researchers prefer NVIDIA over AMD.
• For normal home use: Integrated graphics is enough. Don't waste money on a GPU if you only browse, watch YouTube, and use MS Office.

Coming Up Next

Now that you understand what a GPU is and how we got here, Part 2 will explain the inside of a GPU — CUDA cores, Tensor cores, Ray Tracing cores, and the most confusing topic of all: VRAM (video memory). How much do you actually need?

Continue to Part 2 →