How GPU companies are integrating AI in their products.

 The requirements for AI in the market are at an all-time high with the recent surge of AI tools for almost everything. Naturally, GPU companies had to get their hands dirty with AI and create some mind-blowing tech. So, in this article, we are going to discover how AI is seamlessly integrated into modern GPUs.



Introduction

To understand how AI gets integrated into GPUs, we must first have a deep understanding of what GPUs are and what they do. 

What are GPUs and what do they do?

GPU is the abbreviation of Graphics Processing Unit. Unlike a CPU chip that processes a lot of data simultaneously, a GPU processes data in smaller bits to increase efficiency. A GPU was invented with the intention of rendering graphical designs, but it is now used for much more than that. GPUs are now mostly used in the gaming industry from commercial GPUs for everyday gamers to industrial GPUs for games devs. But what is the need for AI in modern GPUs when they are so powerful by themselves?

What is the need to integrate AI into GPUs?

With older GPUs, their maximum performance was limited by how good or advanced their stock hardware was. For example, if you are playing a game, your FPS is limited by how much VRAM your GPU has, or your graphical processing power and efficiency are also limited by the hardware. When AI started to be integrated into GPUs, the industry changed for the better. So, what did it change exactly?

The impact of AI integration on GPUs and the industry.

When AI was finally integrated into GPUs, many industries started using them for one major purpose- Machine Learning. With this new revolution, companies were able to create tech that was almost unthinkable like self-driving cars and generative AI tools like Dall E and ChatGPT. 

Gamers were also in luck because with all of that came some mind-blowing Upscaling tech. This was revolutionary for the gaming industry. Upscaling tech like Ray Tracing, DLSS, and FSR, with other generative AI tools came along with the new GPUs like Nvidia's 3000 and 4000 series GPUs and AMD's 6000 series GPUs.



2. Major Competitors in the Market

Nvidia

It is safe to say that Nvidia benefitted the most out of any other company (not to say that the others did not benefit at all). Being the first to create AI upscaling tech, it is no wonder they played a major role in laying the foundation for future improvements. Their upscaling tech is the most advanced and successful out of any other. They created DLSS, which stands for Deep Learning Super Sampling and improved upon Ray Tracing.

Now, what is DLSS and what does it do?

As I explained earlier, DLSS stands for Deep Learning Super Sampling. But what does all this fancy stuff do? It uses AI generation, complex calculations, machine learning and prediction methods to generate high-resolution frames in between existing frames to increase frame rates or FPS (Frames Per Second) without any deterioration in other factors. Basically, DLSS gives you more FPS. Nowadays many game studios are starting to integrate support for DLSS in their games right from launch day.

Why is it important?

Now that you know how it works, one might wonder, what is the importance of DLSS? You see according to a monthly hardware and software survey conducted by Steam, most of the player base is using an Nvidia RTX 3060 GPU, which is a good GPU. However, considering how graphically intense today's games are, the GPU struggles to output a desirable frame rate with DLSS turned off. So turning DLSS on basically improves performance and delivers desirable FPS.


AMD

AMD (Advanced Micro Devices) has been in the GPU market for Decades now and although they do not produce GPUs as powerful as Nvidia in terms of upscaling, they still make good budget GPUs to compete against Nvidia and also wins in terms of raw performance, since they have more VRAM for less the price of an Nvidia GPU. While it is not as good as Nvidia's, AMD still has its own upscaling tech; they call it FidelityFX Super Resolution, or FSR for short. It is open source, so now every game can support without any difficulties.

How does it work? 

Similar to Nvidia's DLSS, FSR uses complex calculations, machine learning and prediction methods to upscale the game resolution and increase FPS. 


3. Hardware used to make this possible

Now, this AI integration was not done by just installing an AI software or code into the GPU, it had to be done through some 'AI Specific' hardware; which is what we will discuss in this section.


Nvidia's Tensor Cores

Tensor Cores are specialised cores made specifically to perform mathematical operations on multidimensional layers. Tensor cores are key for AI tasks like Machine Learning. These hardware components were first introduced by Nvidia to help drive DLSS and other Generative Tools faster and more efficiently.

How do these cores work?

Unlike normal processor cores, which perform calculations at a time, these cores are designed to perform multiple calculations simultaneously. This method accelerates the time it takes to process data and increases calculation efficiency.


AMD's Architecture

Now AMD plays this AI 'game' differently. Instead of designating specific cores for AI, AMD has incorporated AI into its architecture. Let us learn more about this architecture.

AMD calls this new architecture RDNA3 which comprises 2 main parts:

  • RDNA
  • CDNA
What is RDNA?

The RDNA architecture was primarily designed for gaming which uses AI-powered tech like FidelityFX Super Resolution of FSR for short to upscale the game resolution and increase frame rates.

What is CDNA?

The primary purpose of the CDNA architecture is to handle AI and data center workloads. It was designed mostly for industrial purposes for heavy computing and machine learning for industries.



Conclusion

The advancements in AI and GPU technologies have left us all awe-struck. These advancements have laid the groundwork for future generations of technologies and innovations. These new GPUs are not as simple as they may look, even though they may be sold commercially and mostly used for gaming, they can do much more than that as we have discussed earlier. These GPUs can design AI models through machine learning to create technologies apart from gaming like self-driving cars. With new launches like Nvidia's 5000 series and Intel slowly gaining momentum in the GPU industries with the new Arc Battlemage series with the all-new Xe2 architecture and the new upscaling tech called XeSS which is similar to DLSS and FSR (about which I have already posted a blog), the future of gaming and AI seems to get brighter and brighter.


Well, that's it for this article, if there is any topic or news you would like me to cover leave it in the comments. This takes a lot of time and effort so consider following. I hope you enjoyed reading this article and have gained something knowledgeable and/or profitable.


Thank You.

Have a Great Day :)

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