Best graphics cards for machine learning

Best graphics cards for machine learning in 2020

For machine learning, it is very very important to have a powerful system. But you obviously don’t have to go with super expensive PC parts, especially for machine learning. But, if you are engaging with deep learning, in that case, you are gonna need a very good computer with very high specifications simply because you have to work with a huge amount of data in that case.

Graphics cards, processors, RAM is the most important factors while we talk about AI, machine learning and deep learning. So, here I am gonna specifically discuss how to select a proper graphics card for machine learning and which are the best graphics cards for machine learning.

Now, I know some of you may be confused about machine learning and deep learning, AI and whether they are the same. No, they are actually not the same at all.

In lemon terms, deep learning is a part of machine learning. Don’t worry, I am gonna discuss this topic further below. But, now let’s some best graphics cards and how to choose a perfect GPU for machine learning for your computer.

Best graphics cards (GPUS) for machine learning:

Best budget GPUs:

You know the RTX series contains the best graphics card ever. But, the problem is that they are tremendously expensive. And if you are in machine learning (for beginners), it is always better to go with an average GPU than directly buying a very expensive one.

That’s why I love to go with the GTX series and even budget-friendly for those who want to buy multiple GPU.

#1. Nvidia GeForce GTX 1660Ti:

GTX 1660Ti is basically a mid-range graphics card but just don’t underestimate its power. It doesn’t like those cheaper graphics cards, who can’t even play games at 1080P settings.

Best graphics cards for machine learning

In the case of machine learning especially for beginners, I suggest buying this GPU because it is a perfect fit for this purpose. It is very good in terms of performance, VRAM, its architecture (chipset) and importantly it is not that expensive compared to Nvidias new GPUs with RTX branding.

This graphics card is also based on Nvidia’s Turing architecture. It is a very good choice for those who don’t want to wipe out all their money but want some serious output.

This Graphics card generally comes in 6GB of GDDR6 VRAM, which is perfect for machine learning. The base model of this GPU can give Core clock boost up to 1770 MHz but some of the latest ones can also give 1860MHz.

It also has too many ports available for connecting. Can you believe it has three DisplayPorts 1.4 and also HDMI port? Ya, that’s why you can connect up to 3 displays with this graphics card. 

The overclocking capacity of this graphics card is also very good. If the cooling system of your PC is good, believe me, you can outsource its true power by overclocking it.

Overall, for beginners in mechanical learning, GTX 1660Ti is the best and solid graphics card I have found in 7 days of research. And this card is also not that expensive.

if you are one of those who like multi-GPU, you can also buy two of these to enjoy the almost double performance.

Secret tip: Do you know a $100 GPU can’t give 2 times performance than a $50 GPU? That’s why getting two medium-priced GPUs can help you get way better results than one expensive graphics card.

Let’s discuss some of its specifications:

  • This GPU is based on Nvidia’s Turing architecture.
  • It can give around 1860MHz boost clock speed.
  • It uses a 192-bit memory interface.
  • It gebnerally co9mes with 6GB GDDR6 VRAM.
  • The recommended power supply for 1660Ti is 450W.

Price:

The price of GTX 1660Ti is around $280, check its latest price below:

Pros:

  • Way more performance than compared to other GPUs in this price range.
  • It comes with tons of great features.
  • Awesome and fits pretty well for machine learning.

Cons:

  • It doesn’t support real-time ray-tracing.

#2. GTX 1660 super:

Nvidia has recently launched new super cards and bingo! I have found a perfect one for you which can be really beneficial in your machine learning career. The GPU is GTX 1660 super.

Best graphics cards for machine learning

Nvidia just keeps on refining their GPU’s to get maximum results. In the case of this 1660 super, it is the best entry-level GPU available in the market till now.

And the most interesting thing is that it is even cheaper than 1660Ti. Then, why I have put the Ti one before it? Well, because this super card is undoubtedly best at a low price but can’t give performance like 1660Ti(since it is not an entry-level GPU). But, there is not a big difference between the performance. So, you can select any one for machine learning. 

Now, 1660 super comes with a super affordable price (under $250) and generally comes with 6GB GDDR6 VRAM. It is based on Nvidia’s TU116 chipset and can offer memory boost up tp 16GB/S

If you observe it carefully, you notice that it perfectly fits between GTX 1660 and GTX 1660Ti. That’s why if you just can’t afford GTX 1660ti for machine learning, directly check this GPU, because it has almost the same facilities, performance but cheaper in price.

This GPU needs up to 127.4W power in extreme cases. So, if you are planning to buy two GPU’s for machine learning, make sure you have a good power supply unit for your PC.

Let’s discuss some of its specifications:

  • It is based on Nvidia’s Turing architecture.
  • GTX 1660 super generally comes with 6GB GDDR6 VARM.
  • It has 3 display port1.4 and HDMI 2.0B.
  • You can connect up to three monitors with this GPU.

Price:

The price of GTX 1660 super is around $240, check its latest price below:

Pros:

  • Awesome in this budget.
  • Very good performance.
  • Good for mechanical learning.

Cons:

  • It doesn’t have sufficient ports except for the display ports.

Actually powerful for machine learning:

If you have a good budget to spend on the graphics card for your computer for machine learning, you should check out these ones, because they are the true power.

Before going further, you must ensure that you are using at least 16GB RAM (32 recommended), a good power supply, CPU, cooling fans because to get ultimate results, every part of your system should be able to work properly with one another. And don’t create bottleneck with these GPUs.

#3. RTX 2070 super:

With real-time ray tracing, this RTX 2070 super is not only for gaming, but also it can give extreme performance in hard-core PC uses. That’s why it is good for machine learning and I also suggest it for deep learning too. This GPU is based on Nvidia’s Turing family and can give up to 1815MHz Core clock speed.

Best graphics cards for machine learning

It generally comes with 8GB GDDR6 VRAM which can be clocked up to 14GB/S. The power consumption of this GPU is 215 Watts. You get 3 display ports 1.4, HDMI 2.0b port and you can connect three monitors with this GPU, which makes machine learning job much earlier.

Now, why you should go with this GPU. The first reason is, its performance, I know there are better GPUs like 2080Ti which give way more good performance than it, but for medium budget, this is a very good GPU especially for Video editing, gaming, and machine learning.

Let’s discuss some of its specifications:

  • It is based on Nvidia’s Turing family.
  • It can give a Core clock speed of 1815 MHz.
  • It uses 8GB GDDR6 VRAM.
  • It has 256-bit memory interface.
  • You get three DisplayPorts 1.4 and one HDMI 2.0b port.

Price:

The price of RTX 2070 super is around $530, check its latest price below:

Pros:

  • It can give 1440P gaming with real-time ray tracing.
  • Awesome for machine learning.

Cons:

  • A little bit expensive.

#4. RTX 2080 super:

Nvidia launched RTX 2080 super which is capable of high performance comparing its base model that is RTX 2080. It one of the latest graphics cards from Nvidia and that’s why it can perform more smoothly than some other more pricy GPUs.

Best graphics cards for machine learning

This Graphics card is also based on Nvidia’s Turing family and supports real-time ray tracing. It generally comes with 8GB GDDR6 VRAM and with 256-bit memory bus.

Not only that, but it also has three display ports, one HDMI port, and Type-C port. So, you can connect up to three screens with it. 

The Core clock speed of this Graphics card is 1845MHz and also supports real-time ray tracing. It is a very good one for 4k gaming. Before, in this budget, you get 4K gaming, but can’t give very smooth gameplay. But, in this case, you not only get 4K but also with a good frame rate.

Not only for gamers, but this graphics card is also very beneficial for machine learning. So, go with this GPU if you like it.

Let’s discuss some specifications of this graphics card:

  • This graphics card is based on Nvidia’s Turing family.
  • It generally comes with 8GB GDDR6 VRAM.
  • It uses 256-bit memory interface.
  • The Core clock speed of this GPU is around 1845MHz.

Price:

The price of RTX 2080 super is around $770, check its latest price below:

Pros:

  • Good performance (can give 4K performance).
  • Awesome for overclocking.

Cons:

  • Expensive.

The giant:

If you are an extreme user and don’t care money for getting maximum performance in your machine learning process, simply go with RTX 2080Ti

#5. Nvidia GeForce RTX 2080Ti:

This GPU from Nvidia is just the next level. It is the most powerful GPU till now in 2020 and I don’t think this fact will change until Nvidia launches a new monster GPU.

Best graphics cards for machine learning

This GPU can easily give you 4K gaming in 60fps with real-time ray tracing. For machine learning, deep learning and AI this is the best GPU you can get for your PC. Some people also buy two 2080Ti for these types of heavy resource-oriented works. 

Well, these completely dependent up to you. But the thing is that you are gonna need a monster power supply for running two RTX 2080Ti with a great motherboard which can handle the extreme pressure.

This graphics card generally comes with 11GB GDDR6 VRAM and can give up to 1,635MHz boost clock. It supports up to 4 monitors which is simply awesome for machine learning because you may have to deal with multiple screens.

This Graphics card is also good fire overclocking, you can unlock the true power of this GPU by overclocking it. Not only that, it comes with triple fans better cooling system.

But, this graphics card is too expensive and that’s why everyone can’t afford it. If you do, go ahead and unlock the true power in machine learning.

Specifications of RTX 2080Ti:

  • It uses Nvidia’s Turing architecture.
  • It can give 4K gaming in 60fps gaming.
  • It generally comes with 11GB GDDR6 VRAM.
  • It supports real-time ray tracing.

Price:

The price of RTX 2080 super is around $1224, check its latest price below:

Pros:

  • Awesome for overclocking.
  • Its performance is amazing.

Cons:

  • Very expensive.

What is machine learning and how it is different from AI and deep learning?

Now the question is what is machine learning and how it is different from deep learning and AI?

Best graphics cards for machine learning

Machine learning is basically a process in which a machine is trained for any specific topic.

For example, if a machine is trained to recognize different breeds of dogs and tons of dog breeds were scans in its data. But, this process fails when you bring a cat in front of the machine, it simply can’t recognize it.

But, in AI learning, a machine is not only taught and mimic but also it can give its own input. 

For example- if you ask google assistant, who is Bill Gates? It gives you an answer. But after that, if you ask the assistant how old is he? Alexa shows information about Bill gates, not someone Bob’s information. That’s a type of AI. 

Now, deep learning is a part of machine learning and it is also applied in specific areas. Like the YouTube video recommendations are the perfect example of deep learning. Do you ever realize how you always see ads for the products where you are interested? Yes, this is because of the deep learning process.

Overall, deep learning is a part of machine learning and machine learning is a part of AI.

Wrapping up:

I hope after reading this post, you can choose the perfect graphics card for machine learning. I have tried to explain the best ones according to different categories.

If you have any questions in your mind, don’t hesitate to ask me in the comments or in my email id.

If you buy from our links we get some commissions because of affiliate partnership. That’s it for today, see you in the next one, tada:)

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