From Gaming to AI: The Evolution of GPUs

There was a time when today’s big names like Nvidia’s GeForce series/AMD Radeon series were terms unheard of by the general public. They were names only relevant to the audience in the video game industry; pure gaming geeks who’d be knowledgeable about GPUs because a certain benchmark was required for the game to perform as expected on their machines.

Fast forward to the AI era (present); just a few months ago, Nvidia’s stock price skyrocketed, and every other “techie” and even the general public became aware of that name and the term GPU. This leads to the 2 fundamental questions:

• Why did it take decades of buildup and hibernation to finally become an inseparable piece of equipment in every other industry, startup, or even household?

• Secondly, what extra do GPUs offer now in the AI era that brought them to the limelight in the tech and electronics industry?

The answer is quite simple, actually. Businesses and the world are now predominantly dependent on AI models. AI models are complex, state-of-the-art computational architectures built from layered algorithms that require a very high amount of "juice" to be trained. This "juice" can be divided into two primary sets:

1. Data sets: the information that is fed into the brain of AI.

2. Engine: the raw hardware piece that is important to train and perform enormous computation tasks to train these models, enabling the model to process the vast amounts of data collected globally.

The second part is where GPUs shine. Of course, apart from them, a lot of other hardware components are in play; but why GPUs? Weren’t they supposed to make video games enjoyable?

Here’s the catch:

Nvidia introduced CUDA (Compute Unified Device Architecture), a powerful parallel computing platform that unlocked the GPU's ability to perform general-purpose tasks — not just graphics — by giving developers access to its raw parallel computing power.

The turning point came around 2012, when a team led by Geoffrey Hinton used GPUs to train a neural network (AlexNet) that blew away traditional image recognition benchmarks — setting the stage for the deep learning revolution

The topic of CUDA and how it works is a completely different ball game, and I’d love to talk about it some other day.Think of modern GPUs like Autobots from Transformers — versatile machines that can shift from high-end gaming to hardcore scientific computation, all while keeping their raw power intact.

GPUs have become a core component of most AI-driven enterprises and are now increasingly present in consumer devices that use AI for image processing, gaming, or creative workloads.