It's time to peer into the heart of computer hardware to understand an essential component: the GPU. Don't worry; this won't get too technical, but understanding this term could be instrumental for your tech investments.
What is a GPU?
GPU stands for "graphics processing unit." This computer chip is the brain behind every visual element that your computer displays. From the colorful graphs in your stock analysis software to the latest blockbuster movie streaming on your 4k screen, the GPU is hard at work turning digital data into visible pixels.
You'll occasionally see the terms GPU and graphics card used as metonyms for one another. That's a useful shorthand, but not exactly right. The two pieces of hardware are closely related, and you can't make a graphics card without a GPU. So when the two terms are conflated into a single concept, it sort of makes sense. Still, they are not the same thing, technically speaking.
The GPU is the most important chip on a fully realized graphics card. Besides the GPU, this card also contains memory chips, a cooler, and other components. You'll also typically find them equipped with a few HDMI or DisplayPort connectors, from which the right cable can transfer the generated graphics data to a monitor (or three). Think of the graphics card as a fully functional car with the GPU being the engine powering it.
Related investing topics
The GPU in action: Training the ChatGPT system
To truly illustrate the importance of GPUs, let's turn to the realm of artificial intelligence. In particular, we'll look at OpenAI's large language models, GPT-3 and GPT-4. Both are powered by a large-scale machine learning model trained on a wealth of internet text, and the resulting large language models (LLMs) power the popular ChatGPT application.
But what does this have to do with GPUs?
It's simple. The training of these colossal models involves processing an astronomical amount of data, a task for which GPUs are uniquely qualified. The right chips can perform thousands of calculations simultaneously, making them crucial in creating these language models in a reasonable timeframe.
One of the key players in the GPU market, Nvidia, has been particularly instrumental in this development. The Nvidia A100, V100, and H100 Tensor Core GPUs, for instance, offer incredible acceleration at every scale for AI training and inference. Thanks to the AI training performed on these high-powered GPUs, the GPT-4 AI system can analyze and understand text on a near-human level.
AMD has its Radeon Instinct accelerators designed specifically for machine learning and AI workloads. Their GPUs are also widely used across the tech industry, powering advancements in AI and machine learning. With just a handful of exceptions to the rule, almost all of the world's most powerful supercomputers were built around either Nvidia or AMD AI-oriented GPUs.
The deployment of these GPUs isn't limited to a shadowy server farm either. They are hard at work in everyday applications like ChatGPT, where users interact with the AI models in real time. This practical application of GPU technology shows how integral they are to advancements in AI, affecting both businesses and consumers.
So understanding the role of GPUs isn't just a tech concern. This understanding directly relates to evaluating companies that manufacture GPUs, like Nvidia and AMD, and businesses that heavily use them, such as OpenAI. Knowing the basic facts about GPUs will help you make more informed predictions about these companies' performance and their impact on the tech industry.
About the Author
