6 Best GPUs For Deep Learning in 2024 Reviews

By: Editorial Team

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.


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Runner Up
Best Budget
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Nvidia Tesla v100 16GB
EVGA GeForce RTX 3080 FTW3 Ultra Gaming, 10G-P5-3897-KL, 10GB GDDR6X, iCX3 Technology, ARGB LED, Metal Backplate, LHR
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
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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.

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Rating
Position
Runner Up
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EVGA GeForce RTX 3080 FTW3 Ultra Gaming, 10G-P5-3897-KL, 10GB GDDR6X, iCX3 Technology, ARGB LED, Metal Backplate, LHR
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
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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 2024-03-28 at 23:06 / Affiliate links / Images from Amazon Product Advertising API


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 the 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.


Here Are The Best Machine and Deep Learning GPUs


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.


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.


Pros

  • Excellent Performance
  • Attractive price tag

Cons

  • Large

Clock Speed: 1580 MHz | NVIDIA CUDA Cores: 6144 | Tensor Cores: 192 | VRAM: 8GB | Memory Bandwidth: 616GB/s

The NVIDIA RTX 3070 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 3070 Ti is the third-best GPU for AI, deep and machine learning.


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.


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.


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 budget.

But, still wants decent performance.


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.