Why is GPU rather than CPU used in Artificial Intelligence Training?

Artificial intelligence If you are looking at the issue from outside; Visual production tools such as Stable Diffusion and Midjourney use what we know as a graphics card. GPU and that assistants like ChatGPT use processors, which we know as processors. from CPU You may think it’s getting power. Although this is what is commonly thought of by everyday definitions, GPU is ahead on both sides regardless of the situation.

Well, it enables us to easily edit images and videos, play games with solid graphics and create 3D designs. GPUshow come it seems quite irrelevant voice and text assistants Can it give life?

First of all, the issue we need to solve is the distinction between CPU and GPU.

When it comes to CPU in general i3, i7, Ryzen 5 Series like these are meant. When it comes to GPU GTX, RTX, 6600, 7700 There are video card series such as. But in order to understand this issue, we need to go behind the scenes a little.

Both the CPU and GPU are essentially processing units. CPU The processing unit we know as performs sequential operations. CPUs that do this quite quickly cannot deal with very large data sets, even if they are fast. However, these units, whose main duties are to ensure the operation of the computer and to perform complex operations, are available in small numbers. to strong cores they have.

on the other hand GPU unitsIt hosts smaller and more cores than CPUs. These units, produced for graphics operations, also perform parallel calculations. Of course, don’t get confused here. We just said that CPUs perform fast operations sequentially. GPUs can perform a large number of operations simultaneously with the benefit of a higher core count. This includes calculations.

Additionally, there are APUs located in the middle of the two sides, containing both the CPU and GPU.

After making this distinction, let’s come to our main question: Why are GPUs preferred over CPUs in artificial intelligence training?

The ability to train artificial intelligence tools depends on how long it takes to process very complex transactions. In order for this to be done as quickly as possible, many complex transactions must be solved at the same time. GPUs compared to CPUs more cores We said we had it. This allows these units to perform many operations at the same time.

Band width This is also a point where GPUs have an advantage. Thanks to their high bandwidth, these units perform many transactions simultaneously and process the data of these transactions at high speed. An average CPU has a bandwidth of 50 to 100 GB/s, while a high-end GPU can reach over 500 GB/s.

In addition, it was developed just to make these operations faster. AI-focused GPUs There is also. These models, which have more robust bandwidth and clock speed, are the ones we are used to. opening GTA 5 Although they are not like cards, they make AI learning more efficient. NVIDIA’s H100 GPU is one of them.

For those who do not understand what has happened so far, there is a very good example that will clarify this issue.

Let’s imagine that what we need to do is to get these people across the street.

This is our CPU:

This is our GPU:

Although Our CPU Even if it is quite advanced, it will take a long time to get all of these people across. Of course, this is not because it is not powerful enough, but because it only has the option to perform operations sequentially.

on the other hand our GPUSince it has the capacity to transfer all of these people (data) at the same time, it allows us to save both time and expense.

When data is entered into artificial intelligence models, both GPU and CPU play critical roles. So don’t let thoughts like one can happen without the other come to your mind. Because even in areas where the CPU is ahead, the GPU can remain on the sidelines.