Why GPUs and NVIDIA are the Powerhouses of AI?
Tags: machinelearning, deeplearning, news, watercooler
Published: 2023-05-31 21:05:16

Ever wondered what’s behind NVIDIA’s recent ascent to becoming a trillion-dollar company? Or why the Graphics Processing Unit (GPU) is so crucial to the realm of artificial intelligence (AI)? Let’s dive into the fascinating world of GPUs, explore the role they play in AI, and understand why NVIDIA is leading the pack.

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## What is a GPU?
The first step is understanding what a GPU is and what it does. Standing for Graphics Processing Unit, a GPU’s primary role, as the name suggests, is to handle the graphical operations in a computer. Whether it’s rendering videos, images, or enabling your video games to run smoothly, the GPU is the powerhouse behind it all.

However, GPUs have another superpower: they’re incredibly useful in the field of artificial intelligence. This is where companies like NVIDIA come into the picture. As the leading producer of computer chips that power the current AI revolution, NVIDIA’s GPUs are pivotal in the generative AI boom.

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## The Magic of Parallel Processing
To understand why GPUs are so important for AI, let’s consider the concept of parallel processing. Imagine you’re in a supermarket with a shopping list of 100 items. You could go through the list one by one, picking up each item in turn. This would be akin to serial processing, where a computer completes one task after another in sequence, much like how CPUs (Central Processing Units) generally work.

But what if you had 100 friends, each assigned to pick up one item from your shopping list? Your shopping would be done a lot faster because many tasks are being done at the same time. This is known as parallel processing, and GPUs excel at it. With many more cores (think of these as your 100 friends in the supermarket) than CPUs, GPUs can handle many tasks simultaneously.

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## Why AI Loves GPUs
When we talk about AI, specifically machine learning and deep learning, we’re often dealing with huge amounts of data. For instance, if you’re training a neural network to recognize images, you might have millions of images to process. Processing these one by one would be as slow and tedious as picking up one item at a time from your shopping list.

![GPU](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0zcuuplp4v0ueqmg6lj6.jpeg)


But if you can process many images at once, you can speed up the entire process. That’s where GPUs, with their ability to perform parallel processing, become so important in AI. NVIDIA, in particular, has demonstrated this with their supercomputer powered by 256 of their “superchips,” designed for training ultra-large language models and other complex tasks.

![Supercomputer](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ifysnp47lmis9eoujyb8.jpeg)

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## The NVIDIA Advantage
NVIDIA has become the market leader in GPUs, playing a significant role in the AI revolution. But the company isn’t just producing top-notch hardware; it’s also providing fully-trained AI foundation models and even expanding into networking capabilities to increase the efficiency of its data centre GPUs.

![Foundational Model](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/22bf2goa1qf13s3g8zlk.jpeg)

In essence, a GPU is important in AI because it allows us to process large amounts of data simultaneously, significantly speeding up the time it takes to train a machine-learning model. With its continuous innovation and commitment to excellence, NVIDIA has secured its position as a key player in this process, driving the AI revolution forward.

In conclusion, GPUs are the lifeblood of AI, and NVIDIA is the rockstar of this show. The next time you hear about advancements in AI, remember the humble GPU doing the heavy lifting behind the scenes and salute NVIDIA for its game-changing role in shaping the future of AI.

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## Before we go…
If you’ve come this far, thanks a lot for reading. Let’s chat on top of it, you can reach me on [LinkedIn](https://www.linkedin.com/in/yatendra2001/) or [Twitter](https://twitter.com/yatendrakumar27).

Ciao 👋