If you are willing to explore deep learning and machine learning then you must have an idea that it involves computational requirements and the key factor that would determine your overall experience is the GPU you choose.
The training period costs you the most and to reduce this cost you must get a GPU that would enable you to run models with large data sets and parameters faster and more efficiently.
GPUs eliminate the bottlenecks by processing the task faster and are optimized to bring in better results. GPU efficiently deals with the complex operations that are involved in deep learning such as matrix manipulation, incomparable computing prerequisites, and computing power. Typically they have high memory bandwidth so that all the information becomes easily manageable.
Finding the best GPU for deep learning and machine learning could be a tedious job because you have to know the nitty-gritty details of one. To save you from all the hard work and help you choose one, we have done extensive research and have shortlisted the 8 best GPUs for deep learning that would proficiently cater to all your requirements.
Additionally, we have reviewed each one so that you get an insight that would help in making a better decision.
Follow these quick links to find the Best Graphics Card for Deep Learning.
If you are in a rush and cannot go over the entire article we recommend you to take a look at the Nvidia GeForce RTX 2080 Super Graphics Card which features a real boost clock of 1815MHz. and 8GB GDDR6 memory.
List of 8 Best GPU for Deep Learning and Machine Learning
If you are trying to find the best Graphics card for deep learning then you have landed at the right place. We have reviewed each GPU so that you can get the best of the best and has all the features that would cater to all your needs.
Do not forget to check our buying guide which is followed by some frequently asked questions as well. It is a run-down of our top picks that highlight the distinctive features of each Graphics Card.
So which one is the best GPU for deep learning? Let’s find out…
|1708 MHz, 8 GB 256-bit GDDR5 memory||Check Price|
|2.9 slot Design, 2 fans, 0dB Technology||Check Price |
|2GB GDDR5 64 bit memory, 4096 X 2160 resolution||Check Price |
|2 x HDMI, 1 x DVI-D |
1366 MHz Boost clock
|Check Price |
|4 GB 256-Bit GDDR5, 1250 MHz boost clock||Check Price|
|1815 real boost clock, 8192 MB GDDR6 memory||Check Price |
|AMD RX 580, 8GB DDR5 Memory, 1405Mhz||Check Price |
|4GB gddr5 memory, 7680 x 4320 maximum resolution||Check Price |
First up on our list is ZOTAC GeForce GTX 1070 Mini Graphic Card which is a small graphic card yet it packs a whole lot of features in it. It is 8 inches long so it will not fit in Mini ITX cases.
It runs on a GP104 chip and can clock as much as 1708 MHz and with this graphic card, you can play the games on 4K at 60fps. This graphic card requires a single eight-pin PCI power connector and it comes with 3 display port 1.4 connectors, 2 HDMI 2.0b port, and a dual-link DVI port.
ZOTAC GeForce GTX 1070 is equipped with 2 fans that are not bulky and surprisingly do not make much noise. It has a metal backplate which is most useful if your chassis has a window. You must get a case that is ventilated properly so that the heat generated by this graphic card escapes easily. This card can efficiently handle VR titles because it is immensely powerful.
- High performance for such a compact card
- Impressive overclock
- Fairly quiet
- Does not fit in many PCs
- Relatively pricey
The second one on our list of best graphics cards for deep learning is ASUS ROG Strix Radeon RX 570. It has a lead in the race because of its higher core count, better clock boosting technology, and faster memory. In addition, This card uses a Navi 14 GPU and has a size of 158 square mm.
clock speed is quite impressive with a 1737 MHz game clock and 1845 MHz boost clock. The RX 570 has 8 GB of GDDR6 memory that sits on a 128-bit bus and runs at 1750 MHz with a bandwidth of 224Gbps.
This graphic is in two variants: 4GB and 8GB, which can give you a gaming experience of 1080p. The total band power of this card is 130W — on a side note, the recommended PSU for this card would a 450W.
Also, you can have a look at our top 10 best PSUs if you’re unaware or confused…
Due to its large size, it would not fit in small form factor cases. It has 5 heat pipes at the start of the plate which does not let the important components get heated and the fan speeds are adjustable.
- Good value for money
- Fairly quiet
- Performance can be inconsistent
- Overclocking isn’t impressive
- Relatively pricey
Next up on our list is Gigabyte GeForce GT 710 Graphic Cards is known for its better clock boosting technique, faster memory, and higher core counts… and has a size of 158 square mm.
Additionally, the clock speed is quite impressive with a 954Mhz core clock. It has 2GB DDR5 memory that sits on a 64-bit bus that runs at 1750 MHz with a 224Gbps bandwidth.
This graphic is in two variants: 2GB and 4GB, which can give you a resolution of 4096 x 2160p. The total band power of this card is 130W and the recommended power supply is 450W.
This card is factory tweaked and has a 2.9 slot which has dimensions of 6.6 x 4.3 x 1 inch and can easily fit in small form factor cases. It has 5 heat pipes at the start of the plate which does not let the important components get heated. On top of that, you can set the fan speed to auto or manual as well.
- Excellent performance
- Compact size
- 4K resolution
- Trails AMD’s Radeon RX 550 in DX12
We will now shed light on the Sapphire Radeon Pulse RX 580 Graphic Card, which is a 9.5-inch long graphic card and is protected by an aluminum backplate that helps in cooling. It has 3 display ports and one HDMI port but lacks DVI-D and for the power supply, you will need an eight-pin power lead because this card requires 130W.
It has a boost clock of 1366 MHz and in the silent mode it has 1411 MHz, also the temperature, settings, performance, power, and acoustic measurements are all inclined with the latter figure.
The fan shrouds of this graphic card are of plastic but give a metallic look. With its Radeon chill software, less power is consumed because frame rates are limited, in case there is not enough movement on the screen which could be a result of many possible reasons. This is an opt-in which means you enable it before playing the game.
- Good value for money
- Much better than other competitors
- Lower playback power consumption
- Performance is inconsistent
- Overclocking not impressive
Next up on our list is the PowerColor VGA AXRX 570 Graphic Card which is a reference design of AMD with increased clock speed and memory speed. It uses a Navi 10 GPU and its die is manufactured with TSMC’s 7nm finFET process.
It has a total of 36 compute units for 2304 stream processors, 144 TMUs, and 64 ROPs. The 1250 MHz for the boost clock is quite impressive because it enhances the performance considerably.
For this graphic card, you will require a single 8 pin power connector because this card is rated at 180W. The dimensions of this card are 8.86 x 5.39 x 1.85 inches which need to be fit in a dual-slot and may not fit in a system with a small form factor.
PowerColor VGA AXRX 570 Graphic Card features a wind force 3 cooler that enhances heat transfer and prevents the critical components from getting heated.
The 3 fans make the airflow pretty strong and effective. Furthermore, this graphic card has 3 display ports 1.4, 1 HDMI port 2.0, and 1 DL-DVI-D port for output. It has additional memory and bandwidth to support 1440p gaming.
- Excellent for 1080 & 1440p
- Fairly quiet
- Outstanding cooling solution
- Too big for some SFF cases
- Fan speed fluctuations
Next, we will be reviewing the Nvidia GeForce RTX 2080 Super Graphic Card which is one of the top graphic cards because of its high performance. The real boost clock of this card is 1815 MHz with 8192 MB of GDDR6 memory for 448 Gbps of memory width.
It has 2944 programmable CUDA Cores, 368 Tensor cores, and 46 RT cores. Amazingly this graphic card has 8 Giga rays per second of bandwidth whereas the highest bandwidth of FP32 operations is 10 teraflops.
The processing of this graphic card is done with a 12nm processor and there are a total of 13.6 billion transistors. The TDP of this card is 225W and the size of this card is 388mm because it requires a bigger CPU that has 3 expansion slots.
This graphic card requires 6 pin and two 8 pin power connectors. You will have to make sure that your PC has the same slots as this graphic card. There are a total of three fans that are less noisy and they maximize airflow.
- Effective cooling
- Fairly quiet
- Factory overclocked
- Large size
- Single LED configuration zone
- Average overclocking software
Next on our list of best GPUs for machine learning is XFX Radeon RX 580 GTS Graphic Card which is a factory overclocked card with a boost speed of 1405 MHz and 8GB GDDR5 RAM.
The cooling system of this graphic card is outstanding and makes less noise as compared to other cards. It has a power rating of 185 watts and uses Polaris architecture. This is an opt-in which means you will have to enable it before playing the game.
The best thing about the Radeon Chill software is that if there are no movements on the screen, its frame rates are limited and extra energy is used… This card has a dual-slot cooler with two fans and the backplate is made of metal.
The size of the card is larger as compared to other cards with similar performance, which is a tradeoff for most of us because the size makes installing and removing difficult and it has 3 display ports, 2 HDMI ports, and a DVI-D port.
- VR ready
- Much better performance than its competitors
- Slow for the size of the card
- Large size
- Tricky 8 pin connector
MSI Gaming GeForce GT 710 is the best on the entire list that we have shortlisted. This card has 2 GB of DDR3 memory on a 128-bit interface.
This offers 768 Cuda cores and the card occupies 2 expansion slots because of its size and its installation is easier. It has a single fan and because of its small size and it can fit in a small factor form.
The maximum resolution you can get from this card is 4069 x 2160p which is quite impressive from a graphic card of this size. It has 9 sensors to monitor memory and VRM. The dimensions of this card are 5.75 x 0.75 x 2.72 inches and have a display port, one HDMI port, and a DVI-D port as well.
The memory clock of this graphic card is 1600 MHz and it is a high-end high performing graphic card.
- Excellent value for money
- High resolution
- No PCIe connector required
- Average overclocking
Since now you have thoroughly gone through the entire review you must have made your choice but it is always advisable to have a complete know-how of the product you intend to buy.
To give you a complete understanding of which factors to consider prior to buying a graphic card we have designed this buying guide very insightfully. It demonstrates all the key elements that would impact your buying decision.
If you want to experience gaming at 1080p then you must get a card having 4-6GB of memory because high-resolution textures require more memory. Similarly, if you are aiming to play games at 4K resolution would require memory of 8GB.
It is one of the most important factors and you need to be sure that the case must have enough space for your graphic card. Check for the dimensions of the graphic card that you intend to buy because they come in different sizes including single, dual, and triple slot flavors.
Gaming cards designed specifically for gaming are typically occupying two expansion slots. Such a graphic card would be blocking adjacent slots especially the ones with a bigger heatsink or fan shroud.
It is preferred that if you have a mini motherboard then you should purchase a mini card or depending on the dimensions and specifications.
Thermal Design Power
It is one of the major elements to consider. As its name suggests, this unit measures heat dissipation and roughly estimates the watts required to run your graphic card. You need to look for a power supply unit that goes well with the TDP of your graphic card, typically a 600W PSU is capable of handling almost all graphic cards but an 800W PSU will be better but consider a PSU that matches well with your GPU.
More efficient graphic cards require more power than a standard card with a PCIe slot provides. Such graphic cards are provided additional power through supplementary PCI connectors.
A card can have a different number of connectors on it ranging from 2-8 pin ports. In case supplemental connectors are not available then up-gradation is required. SATA and Molex connectors are not the ideal solutions because they cannot serve as long-term remedies.
If you want to save money from buying extra adapters then make sure that you buy the graphic card that has the ports that you intend to connect the monitor with. Older versions had only a DVI port whereas newer versions of monitors have HDMI and DP.
Although it is not as important a factor as others, clock speed would affect the rate at which the processor goes through or completes an entire processing cycle. Higher clocked graphic cards have different frame rates.
Trillions of Floating Point Operations per Second
This determines the level of performance but theoretically. It is calculated by multiplying the core count with clock speed by 2. And it determines which one is faster.
This is exactly like clocks, with a higher clock speed you get faster performance, likewise with a greater memory speed you will get fast performance. Increased memory bandwidth tends to make the graphic card much faster.
We hope you find this article helpful in regards to choosing the best GPU for deep learning. With all the information and unbiased reviews, you must be able to make a smart choice.
According to our experts, here are some of the best GPUs for Deep Learning:
If you come across any issue or confusion, you’re more than welcome to comment and our experts will assist instantly.
Moving on to the Frequently Asked Questions
Should I buy a GPU for deep learning?
It is recommended to get a GPU for deep learning because training a model will involve large sets of data and a larger amount of memory will be required to handle the large computational operations.
Is a 2GB GPU enough for deep learning?
The answer is a clear NO! It is always recommended to get a 6GB one because you won’t want your models to be trained in years.
Are GPUs faster than CPUs?
These are designed for different goals and serve different purposes. CPUs are generally faster than GPUs. The ultimate goal of a GPU is to process the graphics faster.
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