Which GPU is Best For Machine and Deep Learning? Expert Reviews

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 GPU for AI, machine and deep learning.


Position
First Place
Runner Up
Best Budget
Snapshot
Nvidia Tesla v100 16GB
MSI Gaming GeForce RTX 3080 10GB GDDR6X PCI Express 4.0 x16 ATX Video Card RTX 3080 Gaming Z Trio 10G
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 3080 is a powerful card that will train your models quickly.

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

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Rating
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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
Rating
Position
Runner Up
Snapshot
MSI Gaming GeForce RTX 3080 10GB GDDR6X PCI Express 4.0 x16 ATX Video Card RTX 3080 Gaming Z Trio 10G
What You Need To Know About The GPU

The NVIDIA RTX 3080 is a powerful card that will train your models quickly.

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Rating
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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.

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Rating

Last update on 2021-10-18 at 08:12 / Affiliate links / Images from Amazon Product Advertising API


Preview
Name
Rating
Cores and VRAM
 
Nvidia Tesla v100 16GB

5120 NVIDIA CUDA Cores, 640 Tensor Cores and 16GB
MSI Gaming GeForce RTX 3080 10GB GDDR6X PCI Express 4.0 x16 ATX Video Card RTX 3080 Gaming Z Trio 10G

8704 NVIDIA CUDA Cores, 272 Tensor Cores and 10GB
Gigabyte AORUS GeForce RTX 2080 Ti Xtreme 11G Graphics Card, 3X Stacked Windforce Fans, 11GB 352-bit GDDR6, Gv-N208TAORUS X -11GC Video Card

3584 NVIDIA CUDA Cores, 544 Tensor Cores and 11GB
2021 Newest EV GA GeForce RTX 3060 XC Gaming, 12G-P5-3657-KR, 12GB GDDR6, Dual-Fan, Metal Backplate +AllyFlex HDMI

3584 NVIDIA CUDA Cores, 28 Tensor Cores and 12GB
Sapphire VCX 11306-01-20T Nitro+ AMD Radeon RX 6700 XT Gaming OC 12GB GDDR6

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

Here Are The Best AI, Machine and Deep Learning GPUs


Best GPU For AI – NVIDIA Tesla V100

Nvidia Tesla v100 16GB

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Pros

  • AI Acceleration
  • Top-notch performance
  • Tensor Cores

Cons

  • Very Pricey

Clock Speed: 1246 MHz | NVIDIA CUDA Cores: 5120 | Tensor Cores: 640 | VRAM: 16GB | Memory Bandwidth: 900GB/s

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

The only 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 AI, machine and 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 and calculations.

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

The only downside is its expensive price tag.


Best GPU For Machine Learning – NVIDIA RTX 3080

MSI Gaming GeForce RTX 3080 10GB GDDR6X PCI Express 4.0 x16 ATX Video Card RTX 3080 Gaming Z Trio 10G

Where To Buy

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Pros

  • Fast memory bandwidth
  • Excellent Performance

Cons

  • Pricey

Clock Speed: 1440 MHz | NVIDIA CUDA Cores: 8704 | Tensor Cores: 272 | VRAM: 10GB | Memory Bandwidth: 912GB/s

After the NVIDIA Tesla V100. The NVIDIA RTX 3080 comes next in line. An expensive and powerful card.

The RTX 3080 is primarily used for 4K gaming. But, the insane performance and tensor cores makes it the best GPU for AI, machine and deep learning.

A solid option if you budget doesn’t cover the NVIDIA Tesla V100

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 3080 is the best graphics card for AI, deep and machine learning.


Best GPU For Deep Learning – NVIDIA GTX 2080 Ti

Gigabyte AORUS GeForce RTX 2080 Ti Xtreme 11G Graphics Card, 3X Stacked Windforce Fans, 11GB 352-bit GDDR6, Gv-N208TAORUS X -11GC Video Card

Where To Buy

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Pros

  • Excellent Performance
  • Attractive price tag

Cons

  • Large

Clock Speed: 1350 MHz | NVIDIA CUDA Cores: 4352 | Tensor Cores: 544 | VRAM: 11GB | Memory Bandwidth: 616GB/s

The NVIDIA RTX 2080 Ti is one of the best graphics card for AI. An expensive and powerful card.

The RTX 2080 Ti is primarily used for 1440p and 4K gaming. But, the insane performance makes it a top 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 third-best GPU for AI, deep and machine learning.


Best Value GPU For Machine & Deep Learning – NVIDIA RTX 3060

2021 Newest EV GA GeForce RTX 3060 XC Gaming, 12G-P5-3657-KR, 12GB GDDR6, Dual-Fan, Metal Backplate +AllyFlex HDMI

Where To Buy

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Pros

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

Cons

  • Very large projects will take longer

Clock Speed: 1320 MHz | NVIDIA CUDA Cores: 3584 | Tensor Cores: 28 | VRAM: 12GB | Memory Bandwidth: 360GB/s

The NVIDIA RTX 3060 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 3060 will struggle.

Still, it is one of the best GPU for AI, machine and deep learning models.


Best AMD GPU For Machine & Deep Learning – AMD Radeon RX 6700 XT

Sapphire VCX 11306-01-20T Nitro+ AMD Radeon RX 6700 XT Gaming OC 12GB GDDR6

Where To Buy

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Pros

  • Solid price-to-performance

Cons

  • Software is still immature

Clock Speed: 2321 MHz | Stream Processors: 2560 | VRAM: 12GB | Memory Bandwidth: 384GB/s

The AMD Radeon RX 6700 XT 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.


Best Budget GPU For Machine & Deep Learning – NVIDIA RTX 2060

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

Where To Buy

Check Today's Price

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.


Which GPU Is Best For AI, 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.

What To Look For When Getting The Best GPU For AI, Machine and Deep Learning

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 AI, 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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