What is GPU and How it works

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Now one of the most important components of a system on a chip besides the CPU is the GPU, the Graphics Processing Unit. Now for some people, the GPU is shrouded in mystery how does it work, the difference between a GPU and CPU. Well, let me explain. 

Then lastly why use a GPU? and is it even important to use a GPU on the cloud?

So, let’s start first with, what is a GPU? GPU stands for ‘Graphics Processing Unit’. But, oftentimes people are more familiar with CPUs. So, CPUs are actually made up of just a few cores. You can think of these cores as the power, the ability of a CPU to do certain calculations or computations.

On the other hand, GPUs are made up of hundreds of cores. But what difference does it make? So, the thing with the CPU is that when it does a computation it does so in a serial form. So, it does one computation at a time.

But with the GPU, it does it in parallel. So, the importance of these two differences is that with a GPU, you are able to do computations all at once, and very intense computations at that.

So, oftentimes when you have app codes, a lot of it’s going to be going to the CPU. But then every now and then, you’re going to have an application that’s going to require quite a bit of compute-intensive support that the CPU just can’t do, so it’s going to be offloaded to the GPU.

So, you can think of a GPU as that extra muscle or that extra brainpower that the CPU just can’t do on its own.

So there are two main providers of GPUs in the industry: NVIDIA and AMD. Both providers manufacture GPUs that are optimized for certain use cases. So, let’s jump into that because a big question I get is: why do I even need a GPU? In what industries and in what use cases?

What is GPU and How it works
What is GPU and How it works

So, the first we’ll talk about is VDI. VDI stands for ‘Virtual Desktop Infrastructure’. So, GPUs are created to support high-intensive graphic applications. Think about if you are a construction worker, right?

And you’re out in the field, and you need to access a very high graphics-intensive 3D CAD program. So, rather than having the server right next to you or right in the field with you can have a server that’s a country away in a cloud data center and be able to view that 3D graphic as if that server was right with you.

And that’s going to be supported by the GPU because the GPU supports graphic-intensive applications. Another example of this would be movie animation or rendering. So in fact, GPUs actually first got their name mainly with the gaming industry. Oftentimes they were referred to as “gaming processing units” because of this ability to provide end-users with low-latency graphics.

But gaming is no longer the focus of the industry anymore. It’s a big piece of it, but now financial services and even healthcare are starting to get into it with artificial intelligence.

So artificial intelligence has two big pieces to it there’s machine learning and there’s deep learning. So now there are also GPUs that are optimized and created specifically for those applications.

There are some that are created for inferencing for machine learning purposes, and there are some that are created to help data scientists create and train neural networks. In other words, they are trying to create these algorithms that can think like a human brain. That’s something that a CPU can simply not do on its own, and it requires GPU capabilities.

And then, lastly, let’s talk about HPC. HPC is a buzzword that’s been going around, it stands for “High-Performance Computing”. While a GPU is not absolutely necessary for HPC, It is an important part of it.

So, high-performance computing is the company’s ability to spread out their compute-intensive workloads amongst multiple compute nodes (or in the case of cloud servers). Oftentimes, though, these applications are very compute-intensive it could include rendering, it could include AI and that’s where a GPU comes in.

You can add a GPU to these servers that are spread out amongst an HPC application and utilize those in that manner. So, this is a nice little segue into why should use GPUs on the cloud. If HPC is such a big piece of that, what else is important about the cloud?

So, the first part of that is you get high performance you need the cloud for that. The GPUs are great. but not on their own. So, back in the day(and even still today), there are companies that use a lot of no-perm infrastructures, and they utilize that infrastructure for any of their compute-intensive applications.

However, especially in the case of GPUs, the technology is ever-changing. In fact, there’s typically a new GPU coming out almost every single year. So, it is actually very expensive and nearly impractical for companies to keep up with the latest technology at this point.

Cloud providers actually have the ability to continually update their technology and provide GPUs to these companies to utilize them when they need them.

So on a more granular basis though, cloud technology can often be broken down from an infrastructure perspective between bare metal and virtual servers. So, let’s talk about the differences, There are advantages of using a GPU on both types of infrastructure.

If you utilize a GPU on a bare-metal infrastructure, the companies oftentimes have access to the entire server itself, and they can customize the configuration. So this is great for companies that are going to be really utilizing that server and that GPU intensive application on a pretty consistent basis.

But for companies that need a GPU maybe just on a burst workload scenario, the virtual server option might be even better. And the nice thing about virtual is that there are often different pricing models as well, including hourly. And the cool thing about the cloud is that you only pay for what you use.

So if a company is using on-prem technology or infrastructure but they are not utilizing it at the time, that technology is depreciating, and it’s essentially a waste of money for that company. When they offload to the cloud, they only pay for what they are using.

And so it just makes a lot more sense from a cost perspective; and then, because the GPU is so great at performance, it just makes sense from a performance perspective as well. So, companies are able to focus way more on output than they are on keeping up with the latest technology.

The List of Important NVIDIA GPUs

GPU BOOST GPU CLOCK(MHz) RAM TDP (WATT)
GeForce GTX 1050
1455
4 GB DDR5
75
GeForce GTX 1050Ti
1392
4 GB DDR5
75
GeForce GTX 1060
1708
6 GB DDR5
120
GeForce GTX 1070
1683
8 GB DDR5
150
GeForce GTX 1070 Ti
1900
8 GB DDR5
180
GeForce GTX 1080
1733
8 GB DDR5X
180
GeForce GTX 1080 Ti
1582
11 GB DDR5X
250
GTX 1650
1665
4 GB DDR5
75
GTX 1660
1785
6 GB DDR5
120
GTX 1660 Ti
1770
6 GB DDR6
120
RTX 2060
1680
6 GB DDR6
160
RTX 2060 SUPER
1650
8 GB DDR6
160
RTX 2070
1620
8 GB DDR6
175
RTX 2070 SUPER
1770
8 GB DDR6
215
RTX 2080
1710
8 GB DDR6
215
RTX 2080 SUPER
1815
8 GB DDR6
215
RTX 2080 Ti
1545
11 GB DDR6
250

The List of Important AMD’s GPU

GPU BOOST GPU CLOCK(MHz) RAM TDP (WATT)
Radeon RX 540
1219
2/4 GB DDR5
65
Radeon RX 550
1183
2/4 GB DDR5
50
Radeon RX 560
1275
2/4 GB DDR5
60
Radeon RX 570
1244
4 GB DDR5
150
Radeon RX 580
1340
4/8 GB DDR5
185
Radeon RX 590
1545
8 GB DDR5
185
Radeon RX Vega 56
1471
8 GB HBM2
210
Radeon RX Vega 64
1546
8 GB HBM2
295
Radeon RX Vega 64 Liquid
1890
8 GB HBM2
345
Radeon VII
1800
16 GB HBM2
300
Radeon RX 5700
1725
8 GB GDDR6
180
Radeon RX 5700 XT
1905
8 GB GDDR6
225

Before Making a purchase a graphics card make sure to read the Buying Guide.

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Abhishek
Hey, I am Abhishek Kumar and I am very passionate about electronics and gadgets and I love to explore and research more about them to keep updated myself and others.

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