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5 Best GPUs For Deep Learning (+ Machine): Reviews & Ratings

To truly evolve our ability to run deep learning and A.I protocols, we need mass adaption. We need machines that are affordable, yet powerful enough that students, enthusiasts, and professionals can work on A.I without needing to re-mortgage their house to buy computer parts.

Essentially, with deep learning, we are trying to replicate a human brain inside a computer, one that can learn and adapt without input. With the human brain processing nearly 450 billion bits of information a second, you can understand the challenge.

One of the most important components in your computing arsenal for deep learning is your GPU or graphics card.

Image

Model

Core Clock

Memory

Memory Clock

Power Connector

Power Draw

Outputs

Check Price

EVGA 11G-P4-2487-KR GeForce RTX 2080 Ti Ftw3...

1650 MHz

11GB

-

2 x 8 pin connectors

250 Watts

HDMI, DisplayPort

NVIDIA Titan Xp Star Wars Galactic Empire...

1450 MHz

12 GB

11.4 GB/second

1 x 8 pin

250 Watts

1 HDMI, 3 DisplayPort

NVIDIA Tesla P100 GPU computing processor -...

1450 MHz

16 GB

732 GB/second

1 x 8 pin

250 Watts

1 HDMI

Nvidia Tesla v100 16GB

7 Teraflops MHz

32 GB

900 GB/second

1 x 8 pin

250 Watts

1 HDMI

EVGA GeForce GTX 1080 Ti FTW3 Gaming, 11GB...

1683 MHz

11 GB

112 GB/second

1 x 8 pin

250 Watts

1 HDMI


Why Deep Learning Requires a Quality GPU

Deep learning requires some of the highest levels of computing power of any new age computer science. Even for small tasks and protocols, you are going to need top-level components.

For serious and in-depth deep learning, you are going to require even more firepower. Not many other computing tasks come close in terms of their spec requirements. This means that you don’t just need a good graphics card or high RAM; you need every aspect of your pc to be top-shelf grade.

In this article, though, we are going to focus solely on the GPU requirements of deep learning.


Buyer’s Guide: Choosing a Quality GPU for Deep Learning

Usage

The first thing you need to consider is your individual usage. A student learning how to do basic deep learning protocols is not going to need to buy a graphics card as capable or expensive as a full-time developer who wants to implement deep learning systems into his app.

If you have a clear idea of what level of user you are, you will find it much easier to decide what budget range to aim for when picking your GPU.

Balanced CPU & GPU Power

We already touched upon the fact the A.I and deep learning require every single component in your computer to work well together. In terms of your GPU, that means you need to ensure that any supporting components, like your RAM and CPU, need to be able to keep up.

In particular, if your CPU is too weak, your graphics card will do something called bottlenecking. This is where the GPU runs at 50-75% of its full power because your CPU has told it to slow down because it can't handle the requests you are sending it.

GPU Specs That Matter

Memory Bandwidth

As graphics cards have gained power and evolved over the last decade, they have become much more memory and power-hungry than their ancestors. This is why all modern graphics cards of a certain caliber, come with an onboard RAM.

Your RAM is where your graphics card stores all of its efficiency cheat sheets. This means that the more VRAM or RAM you have onboard your graphics card, the better, as it will access this before utilizing your motherboard RAM.

Processing Power/GPU Clock Speed

The processing power of your GPU tells you how many decisions or processes it can handle a second. Obviously, the higher the processing power, the better. When trying to emulate the human brain, which can make over 400 billion processes a second, you can see why this is a relevant spec.

Video RAM Size

The higher the VRAM on your graphics card, the less it will have to turn to your computer's main RAM for help. GPU VRAM is also designed better to work with just your graphics card. This means that VRAM is more efficient and effective than normal RAM when it comes to your GPU capabilities.

Brand (Nvidia vs. AMD)

The graphics card war has been waged between two brands for decades. There aren’t really any other contenders when it comes to GPUs. Nvidia makes better consumer cards in our opinion; that is, they are easy to install, user-friendly, and have the least problems.

AMD, on the other hand, makes cards that are cheaper and better for those with tech know-how. If you are looking into overclocking and other tinkering, AMD is probably the best way forward.

GPU Cores

Every GPU core is, in essence, its own brain. That means each core can make its own decisions. The more you have, the better. The quality of the core makes a big difference two, new age Cuda and Stream cores are much more efficient and powerful than the older GPU cores used in years past.

Cooling System

You will never be able to escape the fact that high speed and powered components are always going to produce a lot of heat. This is where the design of the card's cooling system comes into play. A GPU with a good cooling system that is well-designed will be able to shift a lot more hot air away from your GPU, prolonging its life by a decent percentage.

Form Factor

When it comes to graphics cards, the design plays a massive part in your decision. If you like aesthetics, you will want to look for a card with RGB capabilities and sleek looking design. Even if you aren’t into aesthetics, you are still going to want to consider the expansion slots, slot location, and size of the card.

Price & Warranty

We look at the current price of our reviewed products and work out how well they perform per dollar. This enables us to pick lower-powered cards that come with a great price tag, even though they don’t perform as much as the cards that are double the price.

Another thing we look at is the warranty. Computer parts are notorious for breaking down at inappropriate moments. When it isn’t your fault, you want to know that a good warranty covers your card for as long as possible.


5 Best Graphics Card For Deep Learning Reviewed

1. EVGA GeForce RTX 2080 Ti

EVGA 11G-P4-2487-KR GeForce RTX 2080 Ti Ftw3...
  • Real Boost Clock: 1755 MegaHertz; Memory...
  • Triple HDB fans and all new cooler offers...
  • Adjustable RGB LED offers configuration...
  • Built for EVGA precision x1, EVGA all new...

Our Top Pick!

Core Clock

1650 MHz

Memory

11GB

Memory Clock

-

Power Connector

2 x 8 pin connectors

Power Draw

250 Watts

Outputs

HDMI, DisplayPort

Our top pick falls to one of the strongest graphics cards to have ever hit the consumer market. The RTX 2080TI sits at the top of Nvidia's flagship ray-trace capable card range. Nvidia pumped a lot of time, money, and effort into this card, and it shows. It is the cutting edge of graphics cards.

The RTX 2080TI is ideally suited for A.I. work; it even uses GPU-accelerated A.I, which enables it to learn and adapt, delivering better efficiency and more power the longer it runs.

Unfortunately, you have to pay the price for all this power, which is a steep one. The price tag on these cards puts them out of reach for your standard consumer or student. If you are a professional and can afford to invest in the best money can buy, this is the card for you.

The RTX 2080TI is a fantastic component. It is much faster then the previous top dog, the 1080TI, and has tangible ray-tracing capabilities. The card also has deep-learning capabilities and all new DLSS architecture. Even with all this power, it also remains very energy efficient. It does this thanks to the innovation in cooling tech, and by utilizing a smarter fan system then we have seen before.

This RTX 2080TI even comes overclocked out of the box, which provides even more power than a standard RTX 2080TI.

In terms of design, it isn’t as tall as other triple slots cards we have reviewed, and it is still a big card, however. You get HDMI and DisplayPort capabilities, capable of running 8k with ease.

There are some bad points to note, though, as there always is. Firstly, this is a very expensive card and doesn’t have windows seven capability. It is also much less energy efficient when powering multiple monitors.

If you can handle these small negative and have pockets deep enough to afford one, this is the card of most computer nerds dreams. EVGA has really knocked it out of the park with this Nvidia card.

Good
  • Best consumer card
  • Extremely high power
  • Efficient for its power band
  • A.I deep learning tech
  • Best for gaming
Bad
  • Expensive
  • Poor multi-screen power management

2. Nvidia Titan XP

NVIDIA Titan Xp Star Wars Galactic Empire...
  • GPU Architecture Pascal
  • Frame Buffer 12 GB GX5
  • Memory Speed 11.4 Gbps
  • Boost Clock 1582 MHz

Best cuda GPU for Deep Learning

Core Clock

1450 MHz

Memory

12 GB

Memory Clock

11.4 GB/second

Power Connector

1 x 8 pin

Power Draw

250 Watts

Outputs

1 HDMI, 3 DisplayPort

What was once the fastest consumer graphics card on the planet, the Nvidia Titan XP is still an amazing card, albeit a few years old. This means that you can buy a card that once cost an eye-watering amount, at a price that is much more agreeable to the average user. This is a card that came packing with power.

The cooler uses a blower-style layout, paired with the aluminum heatsink this allows the card to run full power, even with an overclock, while remaining cool under pressure. It is built on the 12nm silicone architecture from that year and comes with fully enabled GP102 tech. This was the first card other than the Quadro p6000 professional card to do so.

You get four separate display outputs, which include three display ports and an HDMI. With 12GB onboard VRAM installed from the factory, this is a card that offers blistering performance, and thanks to the card's age, it comes with a much more palatable price tag.

Nvidia took their time to tweak this card so that it operates to its full potential. The Titan XP was touted as the best possible choice for extreme users. At the time, it was one of those cards that offered best in class performance while still being out of reach for the majority of users.

Since the introduction of the RTX line of cards and the new GTX offerings, this older card has been slashed in price across the board. This means that it is now an excellent choice for those that want to do deep learning as it comes with some of the highest cuda cores of any card available.

If you are planning to do deep learning and have a good understanding of GPU architecture, this card is a surprisingly good choice.

Good
  • Killer performance
  • Great overclock potential
  • Power-efficient
  • Quiet fans
Bad
  • More power-hungry then GTX 1080
  • Limited availability

3. NVIDIA Tesla P100

Best GPU for Machine Learning

Core Clock

1450 MHz

Memory

16 GB

Memory Clock

732 GB/second

Power Connector

1 x 8 pin

Power Draw

250 Watts

Outputs

1 HDMI

Our next card and winner of the best GPU for machine learning category goes to the Nvidia Tesla P100. This professional card is aimed at those who need a ridiculously strong GPU, that prioritize performance over gaming ability. This card comes with a whopping 16GB of VRAM and is powered by 3584 cores across its two slots.

This huge VRAM makes this card one of the best options for machine learning; in fact, it has more RAM than a lot of computers and laptops that we have reviewed, which is impressive. Pair it with 16GB DDR4 RAM, and your system will have a combined RAM of nearly 32GB, which should be enough to handle most machine learning protocols.

This card uses the newer pascal architecture, which allows the P100 to deliver extreme performance and hyperscaled workloads. With over 20 teraflops of fp16 performance, this is a card that doesn’t break a sweat under strain.

The cooling tech in this card is unbelievably good, too, running nearly 30 degrees cooler than a GTX 1080 during testing. It is worth bearing in mind, though, that this is a card designed for work, not for play, it doesn’t come with the stylish look we associate with GTX cards, and cannot power multiple monitors.

As this is a professional aimed card, you get a level of efficiency that you do not see from the GTX or RTX range. This card is literally made for machine learning, so if you are going to be spending most of your time doing deep and machine learning tasks and little time gaming, this would be our choice.

It is built to last longer under full strain and is made to be resistant and hardy to the errors that gaming cards can throw up during deep learning systems.

Good
  • Professional card
  • Ridiculous level of VRAM
  • Power-efficient
  • Designed for machine learning
Bad
  • Lack of functionality for everyday users
  • Limited availability

4. Nvidia Tesla v100

Top of the Range

Core Clock

7 Teraflops MHz

Memory

32 GB

Memory Clock

900 GB/second

Power Connector

1 x 8 pin

Power Draw

250 Watts

Outputs

1 HDMI

As A.I. technology has become more accessible to computer scientists worldwide, we have seen the birth of several cards designed to tackle machine and deep learning. The P100 was the first iteration of this and opened the door for innovation.

The successor to the P100, the Tesla V100, is one of the most powerful and sophisticated data center cards to have ever been created. With the new Volta architecture that it uses and with over 5000 cuda cores and an option for a blistering 32GB of VRAM, this is the golden standard for deep learning GPU’s.

This is a card that has been designed for A.I workflows and has immense power to do so. The Tesla V100 is prepared for hyperscaling too. When you're A.I system has been trained to your standards; it can then provide the maximum performance currently possible to hyper-scale server racks.

With over 640 tensor cores, this card was the world's first GPU that was capable of breaking the 100 teraflop barrier of machine learning performance. If you intend to do deep learning on a massive scale, you can even use NVLINK to link multiple v100 cards together, giving you deep learning capabilities at the forefront of computing power and A.I science.

If you are serious about the machine and deep learning and want to invest in the best that money can buy, the Tesla V100 is one of the most impressive cards we have ever seen.

Good
  • Best money can buy
  • Ridiculous level of VRAM
  • Groundbreaking machine learning tech
  • NVLINK capable
Bad
  • Extremely expensive
  • Requires a high level of tech knowledge

5. EVGA GeForce GTX 1080 Ti

EVGA GeForce GTX 1080 Ti FTW3 Gaming, 11GB...
  • Real Base Clock: 1569 MHz/Real Boost Clock:...
  • EVGA iCX Technology - 9 additional temp...
  • GPU/Memory/PWM Thermal Status Indicator RGB...
  • New vented heatsink fin design and pin fins...

Best Budget GPU for Deep Learning

Core Clock

1683 MHz

Memory

11 GB

Memory Clock

112 GB/second

Power Connector

1 x 8 pin

Power Draw

250 Watts

Outputs

1 HDMI

For those that want to dip their toes in the water of A.I and deep learning, but don’t want to spend thousands on a professional card, the GTX 1080 TI is our winner for best budget GPU for deep learning.

The GTX 1080ti was, for a while, the best consumer card on the market. Topping Nvidias GTX range, this powerful card comes with 11GB onboard VRAM and a super-strong cooling system.

For this reason, it was the holy grail for gamers. Luckily, it also does extremely well in deep learning and A.I. protocols. At the time, the GTX 1080TI made massive waves since it performed better than the Titan X, which was meant to be at the top of the pyramid.

Recently, with the addition of the Titan, and RTX range, the 1080ti has been demoted. Don’t let that fool you, though, thanks to the demotion, and you can now pick a GTX 1080ti up for a fraction of the cost that it used to RRP at.

Although you do not get the ray-tracing capabilities that you see in the new RTX range, this card is still an absolute cannon and destroys most tasks with blistering performance. There is a reason this card was the top dog for a long time, and it isn’t ready to roll over just yet.

For students or those on a budget, there isn’t any other card that is suitable for machine learning that has as strong a price to performance ratio. This makes it our best budget GPU pick.

Good
  • Great price tag
  • Good level of VRAM
  • Ice cool under strain
  • Beats the titan X
Bad
  • Old technology
  • Lacks ray-tracing

Deep Learning Software & Platforms Requiring GPUs

The reason that you need such a strong GPU for deep learning is the programming used. Requirements for these programs are constantly on the change, meaning you need to keep up in terms of computing power. All of the following programs require a good GPU:

  • Neural Designer
  • H20
  • DeepLearningKit
  • Keras
  • Genism
  • Deeplearning4J

One program, in particular, requires a high spec, and for a good reason. One of the most popular deep learning programs on the planet, TensorFlow. TensorFlow is an open-source end-end machine learning platform designed for everyone.

It comes with a deep library of community resources as well as some innovative and effective tools. Although there is no specified spec requirement, to work with TensorFlow, you are going to need a card that is at least as powerful as a GTX 1080TI.


Comparing Integrated vs. Dedicated Graphics

Integrated graphics cards are the cards supplied in most low-end laptops and computers. They are capable when it comes to web browsing and watching videos, but they do not hold up in gaming or deep learning scenarios. You absolutely must have a dedicated card to even consider working in the deep learning space.


People Also Ask (FAQs)

Why is GPU better than CPU for deep learning?

A strong GPU can process the specific data used for deep learning, much more effectively than a CPU. This means that your GPU is the driving force behind deep learning processes, not your CPU.

How much GPU memory is required for deep learning?

We highly recommend at least 8GB VRAM as a minimum, with 10+ being optimum.

Is it possible to rent out a GPU for deep learning? Are there any free online resources?

It is possible, although doing so would require you to find a local business who offer such services. It can be hard, as graphics cards, like cars, acquire mileage. This means most people will not want you to rent their GPU and run the miles up.

How can I use GPU instead of integrated graphics?

If your system is prioritizing your integrated graphics, then you will need to change the driver settings. If you are using an Nvidia card, you can do this through their Geforce program.

Will TensorFlow automatically use GPU? How can I tell if TensorFlow is using my GPU?

If TensorFlow detects the need to engage your GPU, it will do so automatically. If you want to know if it is currently using your GPU, you should use a program designed to monitor your GPU’s usage.


Conclusion

I hope this article has helped you out if you are just setting off on your journey into deep learning. Graphics cards can be confusing at the best of time, and it only gets more confusing when you are dealing with deep learning or machine learning.

If you are still unsure what card is best for you, we recommend going with our top pick, the RTX 2080TI. This is the best card for the majority of users.