Although a GPU is not required for machine and deep learning. Owning a GPU as your neural networks and models get larger becomes important.
The more powerful your GPU the faster your models will be trained. But, do you need a good GPU for machine learning? Here’s why you may or may not need a GPU.
How Much GPU Do I Need For Machine Learning?
Do you plan to put GPUs in an array or running a single GPU? If you plan on creating an array of GPUs for machine learning.
Your budget is all that matters. But for a single GPU then you have to start looking at your budget and specifications.
The more money you have the better GPU you can get. A top-tier NVIDIA RTX GPU costs on average USD900-1500.
Next is the VRAM, the higher the VRAM the more graphics data it can hold and process. Think of the VRAM as the RAM of the GPU used by it only. Highly recommended you get GPUs that have 4GB or higher VRAM.
Finally, clock speed. Just like with CPUs higher clock speeds translate into better performance and faster completion of tasks.
So how much GPU you need for machine learning depends on your budget, skill in machine learning, and the size of your models or neural networks.
Which GPU Is Best For Machine Learning?
GPUs that have high clock speeds, core count, and VRAM are the best for machine learning. For a list of such GPUs read this guide for the best GPUs for machine learning.
Why Is GPU Good For Machine Learning?
GPUs are not required for machine learning. However, the larger your datasets and neural networks become the more computing power your PC or laptop requires.
And if your hardware isn’t that powerful it’s going to take a long time to train them. This is where GPUs come in.
In addition to having powerful processors for machine learning, GPUs greatly reduce the training time of neural networks and models.
These are hardware-intensive tasks and the several thousands of cores in a GPU complete the tasks faster than a CPU. This saves you time because you don’t have to wait for several hours for the models to be completed.
Do You Need GPU For Artificial Intelligence?
You don’t need a GPU for AI if you have small datasets, training small models, or neural networks.
A GPU becomes necessary when the amount of information and tasks grows so much it takes a lot of time to finish them.
A decent or powerful GPU greatly shortens the time because they excel at specialized tasks compared to CPUs.
That’s why powerful GPU cards such as NVIDIA RTX are mostly used for training complex neural networks and models.
They have high clock speeds, cores, and VRAM that helps immensely in providing the necessary performance in finishing the training in a short duration.
Is GTX 1060 Good For Machine Learning?
A few years ago, GTX 1060 was good for machine learning. Now? They are not so great anymore.
Newer and more powerful cards have been released making them better options than the GTX 1060.
Plus, they are either cheaper or the same price as the GTX 1060 if in the same category. High-end cards are more expensive and come with better performance.
Is Tensorflow GPU Faster?
Tensorflow GPU is always going to be faster than CPU. It’s a complete and robust framework that offers fantastic features and capabilities for machine learning.
However, this greatly depends on how you train your models and neural network.
Is AMD GPU Good For Machine Learning?
Yes, AMD GPU is good for machine learning. Contrary to what other people or NVIDIA have led you to believe that only NVIDIA GPUs are great for machine learning.
AMD GPUs also offer similar performance to NVIDIA GPUs when training models and neural networks. Plus, you don’t even need a GPU for machine learning.
The only important factors in determining the performance of a GPU are the clock speed, cores, VRAM, and GPU architecture.
Another thing to note is that the GPU cores in AMD are called stream processors and in NVIDIA are CUDA cores.
Both are GPU cores but with different names. You can read more about NVIDIA CUDA cores vs stream processors if you want to understand them better.
In short, AMD GPUs are fantastic options for machine learning.