Article by Ayman Alheraki on January 11 2026 10:37 AM
In 1999, the first true Graphics Processing Unit (GPU) was born under the name GeForce 256 by NVIDIA, announced as the "world’s first GPU." But before this breakthrough, there was no concept of a standalone graphics processor. Graphics were handled by the CPU or simple display adapters that merely pushed pixels to the screen.
In the DOS era of the '80s and '90s:
The x86 CPU was responsible for pushing every pixel to Video RAM.
There were no real graphical computations—just direct memory writing.
3D graphics and shading were nearly impossible without heavy and slow software-based rendering.
Offloaded graphical processing from the CPU to a dedicated parallel processor.
Capable of executing thousands of similar operations on massive data sets in parallel.
Initially served gaming needs, but now it powers modern AI and machine learning.
With time, GPUs became integrated into processors (SoCs), offering decent graphical performance without requiring a dedicated GPU card.
Integrated in most modern Intel Core processors.
Performance is suitable for office tasks, media, and light gaming.
Newer Intel Iris Xe GPUs (starting from 11th Gen) offer a significant leap and can run basic 3D games.
Support for DirectX 12, OpenCL, and some machine learning capabilities through Intel’s OneAPI.
Does not support CUDA, but supports OpenCL.
Performance is far below NVIDIA and AMD discrete GPUs but perfect for mobile and low-power desktops.
Integrated into AMD Ryzen APUs like the Ryzen 5 5600G or Ryzen 7 8700G.
Offers the best integrated graphics performance in the market.
The Vega series (earlier generations) and newer RDNA 2 iGPUs offer excellent performance for general users and casual gaming.
Supports OpenCL, Vulkan, and AMD’s ROCm for compute tasks.
In many cases, AMD integrated GPUs can run modern games at 720p–1080p without a dedicated GPU.
| Feature | Integrated GPU (Intel / AMD) | Dedicated GPU (NVIDIA / AMD Radeon) |
|---|---|---|
| Graphics Performance | Moderate to low | Very high |
| Power Consumption | Low | Relatively high |
| Cooling Requirements | No extra cooling needed | Requires powerful cooling solutions |
| AI Support | Very limited or none | Advanced (CUDA, Tensor Cores) |
| Cost | Included with CPU | Expensive, separate hardware |
| Suitable Use Cases | Office, video, light gaming | AI, heavy games, rendering |
If a GPU vendor doesn’t provide a framework like NVIDIA’s CUDA, you can use open standards:
OpenCL: Cross-vendor GPU compute API (Intel, AMD, others).
Vulkan Compute and DirectX Compute: For general-purpose GPU computing.
OpenGL Compute Shaders: Available via standard graphics APIs.
Metal: Apple’s proprietary framework for Mac devices.
AI models depend heavily on matrix operations and repeated computations:
Matrix multiplication is the backbone of neural networks.
GPUs excel at performing millions of these calculations simultaneously.
For instance, training GPT-4 required thousands of NVIDIA A100 GPUs running for months.
Best for AI and machine learning (CUDA, TensorRT, cuDNN).
Excellent for high-end graphics and gaming.
Strong graphics performance.
Supports ROCm (an alternative to CUDA), but software support is still growing.
Great for budget and mobile systems.
Not suitable for heavy AI workloads but can support light compute tasks via OpenCL.
Apple Silicon features powerful integrated GPU and Neural Engine.
Supports CoreML and efficient machine learning on macOS and iOS.
Emergence of specialized processors (TPUs, NPUs) for AI.
Dedicated AI blocks inside GPUs (like NVIDIA’s Tensor Cores).
High-level language support (Python, Swift) for easier GPU programming.
GPU use is expanding to all fields: healthcare, autonomous vehicles, cybersecurity, finance, and more.
From early VGA display adapters to today’s powerful compute engines, the GPU has come a long way. It's no longer just a graphics device—it’s now the second brain of the computer.
If you want to enter the world of AI, machine learning, or modern graphics, understanding GPU architecture—whether integrated or dedicated—is essential.