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tech consumer guide

Last updated on September 1st, 2020 at 03:41 pm

Best GPUs For AI, Machine Learning and Deep Learning in 2020

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Last updated on September 18th, 2020 at 08:52 pm

Planning on building a desktop for artificial intelligence (AI), machine and deep learning? You will need a solid GPU because they greatly improve the completion speed of your models.

In this article, we list the best GPUs for artificial intelligence, machine and deep learning.


Position
First Place
Runner Up
Best Budget
Snapshot
Nvidia Tesla v100 16GB
EVGA 11G-P4-2487-KR GeForce RTX 2080 Ti Ftw3 Ultra, Overclocked, 2.75 Slot Extreme Cool Triple + iCX2, 65C Gaming, RGB, Metal Backplate, 11GB GDDR6
EVGA 06G-P4-2066-KR GeForce RTX 2060 KO Gaming, 6GB GDDR6, Dual Fans, Metal Backplate
What You Need To Know About The GPU

Optimised for AI, Machine and Deep learning. The NVIDIA Tesla V100 is the best GPU for such purposes.

The NVIDIA RTX 2080 Ti is a powerful card that will train your models quickly.

NVIDIA RTX 2060 is the best budget GPU for beginners and intermediates. Highly recommended.

Prime
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Rating
-
-
Position
First Place
Snapshot
Nvidia Tesla v100 16GB
What You Need To Know About The GPU

Optimised for AI, Machine and Deep learning. The NVIDIA Tesla V100 is the best GPU for such purposes.

Prime
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Rating
-
Position
Runner Up
Snapshot
EVGA 11G-P4-2487-KR GeForce RTX 2080 Ti Ftw3 Ultra, Overclocked, 2.75 Slot Extreme Cool Triple + iCX2, 65C Gaming, RGB, Metal Backplate, 11GB GDDR6
What You Need To Know About The GPU

The NVIDIA RTX 2080 Ti is a powerful card that will train your models quickly.

Prime
-
Rating
Position
Best Budget
Snapshot
EVGA 06G-P4-2066-KR GeForce RTX 2060 KO Gaming, 6GB GDDR6, Dual Fans, Metal Backplate
What You Need To Know About The GPU

NVIDIA RTX 2060 is the best budget GPU for beginners and intermediates. Highly recommended.

Prime
Rating
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Last update on 2020-11-26 at 02:35 / Affiliate links / Images from Amazon Product Advertising API


Preview
Name
Rating
Cores and VRAM
 
Nvidia Tesla v100 16GB

640 Tensor Cores and 16GB
EVGA 11G-P4-2487-KR GeForce RTX 2080 Ti Ftw3 Ultra, Overclocked, 2.75 Slot Extreme Cool Triple + iCX2, 65C Gaming, RGB, Metal Backplate, 11GB GDDR6

4352 NVIDIA CUDA Cores and 11GB
ASUS ROG-STRIX-GTX1080TI-O11G-GAMING GeForce 11GB OC Edition VR Ready 5K HD Gaming HDMI DisplayPort DVI Overclocked PC GDDR5X Graphics Card

3584 NVIDIA CUDA Cores and 11GB
ASUS ROG STRIX GeForce RTX 2070 SUPER Advanced Overclocked 8G GDDR6 HDMI DisplayPort USB Type-C Gaming Graphics Card (ROG-STRIX-RTX-2070S-A8G-GAMING)

2305 NVIDIA CUDA Cores and 8GB
XFX Radeon RX Vega 64 8 GB HBM2 3 x DP HDMI Graphics Card RX-VEGMTBFX6

4096 Stream Processor and 8GB
EVGA 06G-P4-2066-KR GeForce RTX 2060 KO Gaming, 6GB GDDR6, Dual Fans, Metal Backplate

1920 NVIDIA CUDA Cores and 6GB

Which GPU Is Best For Artificial Intelligence, Deep and Machine Learning

The best GPU for AI, deep and machine learning is the one that you can afford and train your models effectively. But, that takes some time to figure out.

For quick answers – the NVIDIA Tesla V100 is the best GPU for AI i.e. if you have the budget.

But, for those on a budget. The NVIDIA RTX 2060 is the best budget GPU for AI, machine and deep learning. It also has the best price-to-performance ratio when it comes to training models.


Here Are The Best AI, Machine and Deep Learning GPUs


Pros

  • AI Acceleration
  • Top-notch performance
  • Tensor Cores

Cons

  • Pricey

Clock Speed: 1246 MHz | Tensor Cores: 640 | VRAM: 16GB | Memory Bandwidth: 900GB/s

The NVIDIA Tesla V100 is a powerful card and one of the best GPUs for AI, machine and deep learning. Because it is packed with all the goodies and optimized for such purposes.

The only that might be a problem is the price tag. To be honest, a card with such performance is not going to be affordable. Plenty of VRAM, high memory bandwidth and tensor cores (specialized cores for deep learning) mean everything will go smoothly.

Furthermore, it comes equipped with AI acceleration. What it does is it basically speeds up artificial intelligence applications.

To sum it all up, the NVIDIA Tesla V100 was made for artificial intelligence, machine and deep learning. It provides the best performance for such purposes. That means less time in training models.


Pros

  • Fast memory bandwidth
  • Excellent Performance

Cons

  • Pricey

Clock Speed: 1350 MHz | NVIDIA CUDA Cores: 4352 | VRAM: 11GB | Memory Bandwidth: 616GB/s

After the NVIDIA Tesla V100. The NVIDIA RTX 2080 Ti comes next in line. An expensive and powerful card.

The RTX 2080 Ti is primarily used for 4K gaming. But, the insane performance makes it a solid choice for AI, machine and deep learning.

Because it has high and fast memory bandwidth, plenty of VRAM and CUDA cores. This makes it powerful enough to be used for large models.

However, the completion speed won’t be as fast as the NVIDIA Tesla V100. Overall, the RTX 2080 Ti is the second-best GPU for AI, deep and machine learning.


Pros

  • Excellent Performance
  • Attractive price tag

Cons

  • Large

Clock Speed: 1582 MHz | NVIDIA CUDA Cores: 3584 | VRAM: 11GB | Memory Bandwidth: 484GB/s

Back in the day, the NVIDIA GTX 1080 Ti was the king of gaming GPUs until the RTX 2080 Ti took that spot. Although it is not the fastest, it’s still one of the powerful cards on the market.

And the best part is the GPU is currently more affordable. This makes it a solid alternative to the RTX 2080 Ti for those who do not have the budget. Although there are newer cards, it is still comparable to the RTX 2080.

To be honest, when you look at the performance and price. You can’t go wrong. Because you are getting better performance albeit lower bandwidth than the RTX 2080.

Overall, a solid card if you want RTX 2080 performance on a budget.


Pros

  • Excellent Price-to-performance ratio
  • Great for intermediate and quite demanding models

Cons

  • Very large projects will take longer

Clock Speed: 1410 MHz | NVIDIA CUDA Cores: 2304 | VRAM: 8GB | Memory Bandwidth: 448GB/s

The NVIDIA RTX 2070 has an excellent price-to-performance ratio. Everything about it is well-balanced and the price tag makes it an attractive choice.

Powerful enough to take on large models or projects. Whiles not being too expensive. However, for exceptionally large projects and models. The NVIDIA RTX 2070 will struggle.

Still, it is a solid GPU for those who create medium size to large AI, machine and deep learning models.


Pros

  • Solid price-to-performance

Cons

  • Software is still immature

Clock Speed: 1247 MHz | Stream Processors: 4096 | VRAM: 8GB | Memory Bandwidth: 484GB/s

The AMD RX Vega 64 is for those who don’t take AI, machine and deep learning professionally.

Because the software is not matured for such purposes as compared to NVIDIA GPUs. However, the hardware is great making it a solid option up to a certain point.

Plenty of VRAM, fast memory bandwidth and a lot of stream processors make the AMD RX Vega 64 hard to ignore.

Overall, it is a great card that can be used for AI. Just keep in mind that, for professional applications switch over to NVIDIA because of their matured software.


Pros

  • Affordable
  • Great price-to-performance ratio

Cons

  • Large models will take longer to complete

Clock Speed: 1365 MHz | NVIDIA CUDA Cores: 1920 | VRAM: 6GB | Memory Bandwidth: 336GB/s

The NVIDIA RTX 2060 is the best budget GPU for AI, machine and deep learning. The specs make it great for starting at the same time decent enough for reasonably large-sized projects.

This makes it a solid GPU when it comes to testing the waters. Highly-recommended if you are a beginner, student or intermediate who doesn’t have a lot of budgets.

But, still wants decent performance.


What To Look For When Getting The Best Artificial Intelligence (AI), Machine and Deep Learning GPU

So you want to build a rig for AI. But, don’t know which GPU to get? Here are a few things to consider when looking for the best GPU for AI, machine and deep learning.

Clock Speed

This metric is pretty straightforward and provided on the spec sheet. The higher the clock speed the more powerful the GPU. However, clock speed is not the only determining factor in GPU performance.

For example, it is well known that the RTX 2060 is more powerful than the GTX 1650. But, when you take a look at their clock speeds (MHz). They are almost similar. So clock speed shouldn’t be the only thing that you should use to gauge a GPU’s performance.

VRAM

Just like how having a lot of RAM helps in system performance and multitasking. VRAM is RAM specifically used by the GPU. That means the more VRAM a GPU has the more it can handle graphical loads.

Graphic cards with a high amount of VRAM are more often powerful cards. For example, the RTX 2060 has 6GB of VRAM whiles the GTX 1650 has 4GB VRAM. The RTX 2080 Ti has a whopping 11GB RAM.

Cores

Just like how CPUs have their own cores. GPUs also have cores except that GPU cores are more numerous than CPU cores.

Also, NVIDIA calls their cores NVIDIA CUDA cores whiles AMD refer to theirs as Stream processors. Depending on the complexity of the project having a GPU with a lot of cores helps a lot.

Memory Bandwidth

Memory bandwidth is the speed of the video RAM. The higher the memory bandwidth the better. Furthermore, GPUs have a higher memory bandwidth than CPUs.

One of the reasons why GPUs are important in AI, machine and deep learning. Because they can transfer large amounts of data in a second.

This is normally written as GB/s. Also, the higher the memory bandwidth the more powerful the GPU.

Your Level Of Expertise

To know which GPU is right for you. You should know your level of expertise. For example, if you are a beginner and occasionally do some projects.

Then there is no reason to get an NVIDIA RTX 2080 Ti. Unless you have sufficient experience or it’s your full-time job. You can use this as a guide below.

  • Beginner – GTX 1650 or below
  • Intermediate – GTX 1660 Ti or RTX cards
  • Experience – RTX cards and Tesla cards

Budget

Finally, the budget. GPUs are expensive especially the powerful ones. Your budget has to be enough to get a card that is in line with your level of expertise and if it has the performance to handle projects.

NVIDIA Vs AMD GPUs For Artificial Intelligence, Deep and Machine Learning

Currently, NVIDIA GPUs. Because they have more support than AMD. You can use AMD GPUs up to a certain point though. But, for best results and performance. NVIDIA GPUs are the way to go for AI, deep and machine learning.

Cloud Services For AI, Deep and Machine Learning

Cloud services allow you to take advantage of the massive computing power using the internet. This means that you can scale up your projects very quickly.

Google Cloud and Amazon Web Service are examples of such cloud services. However, things can get really expensive with these cloud services.

Final Thoughts

Depending on your budget and expertise. Choosing the best GPU for artificial intelligence, machine and deep learning is very important. Because they add a lot of computational power in training your models.

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