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 computing limitations by processing the task faster and are optimized to perform target tasks. 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 well-informed 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 1815MHz and 8192 MB GDDR6 memory.
List of 8 Best GPU for Deep Learning and Machine Learning
If you are trying to distinguish the best Graphics card for deep learning then you have landed at the right place. We have reviewed each GPU so that you can make a piece of informed information in regards to all the features that would capably cater to all your needs. Do not forget to check our buying guide towards the end which is followed by some frequently asked questions. To give you an insight into the article following 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…
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 fix in Mini ITX cases. This runs on a GP104 chip and can clock as much as 1708 MHz and with this graphic card, you can play the games at 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 Mini Graphic Card 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 is exhausted. This card can efficiently handle VR titles because it is immensely powerful.
Second on our list of best graphics card for deep learning is ASUS ROG Strix Radeon RX 570 Graphic Card has a lead in the race from many of its competitors and is a product from a well-reputed firm because of its higher boost clock, higher core count, fast memory. 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. ASUS ROG Strix Radeon RX 570 Graphic Card has 8 GB of GDDR6 memory that sits on a 128-bit bus that runs at 1750 MHz and the bandwidth is 224 Gbps.
This graphic is in two variants of 4 GB and 8 GB which can give you a gaming experience of 1080p. The total band power of this card is 130W at which the recommended power supply is 450W. This card is factory tweaked and has a 2.9 slot which has dimensions of 11 x 5 2.3 inches which are largely owing to large PCB and heat sink because of which 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. The speed of the fan of this graphic card can be set to auto or manual.
Next up on our list is Gigabyte GeForce GT 710 Graphic Cards which has a lead from many of its competitors and is a product from a well-reputed firm because of its higher boost clock, higher core count, fast memory. This card has a size of 158 square mm. clock speed is quite impressive with a core 954Mhz clock. Gigabyte GeForce GT 710 Graphic Cards 2 GB of DDR5 memory that sits on a 64-bit bus which runs at 1750 MHz and the bandwidth is 224 Gbps.
This graphic is in two variants of 2 GB and 4 GB which can give you a gaming experience of 4096 x 2160p. The total band power of this card is 130W at which 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 inches which are small owing to PCB and heat sink because of which it would 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. The speed of the fan of this graphic card can be set to auto or manual.
We will now shed light on Sapphire Radeon Pulse RX 580 Graphic Card 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. Sapphire Radeon Pulse RX 580 Graphic Card has a boost clock of 1366 MHz and on 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 much movement in the graphic being rendered 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.
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 but prevents the critical components from getting heated. Since there are 3 fans because of which the airflow is effective. 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.
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. The Nvidia GeForce RTX 2080 Super Graphic Card 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 process and there are a total of 13.6 billion transistors. The TDP of this card is 225W. The size of this card is 388mm because of which you will require a CPU with a bigger form factor because it will require 3 expansion slots. This graphic card requires a 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.
Next on our list of best GPU 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 GDDR5 RAM from 8.0 to 8.1 GHz. The cooling system of this graphic card is outstanding and makes minimal noise. This graphic card has a power rating of 185 watts. XFX Radeon RX 580 GTS Graphic Card uses Polaris architecture. This is an opt-in which means you enable it before playing the game.
With its Radeon chill software, less power is consumed because frame rates are limited in case there is not much movement in the graphic being rendered on the screen which could be a result of many possible reasons. This card has a dual-slot cooler with two fans and the backplate is made of metal. The size of the card is larger than the other cards providing similar performance which is not impressive. Installing and removing this card can be troublesome. This graphic card has 3 display ports, 2 HDMI ports, DVI-D port.
Lastly, we will discuss MSI Gaming GeForce GT 710 Graphic Card as the best on the entire list that we have shortlisted and we have the reasons why. 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. MSI Gaming GeForce GT 710 Graphic Card has a single fan and because of its small size, it can also be fit in a small factor form.
The maximum resolution you can get from this card is 4069 x 2160 p which is quite impressive from a graphic card of this size. This graphic card 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 the chipset used in this is NVIDIA GeForce GTX 1050 It. This is a high-end high performing graphic card.
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 texture packs require more memory. Games with 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 more than enough and won’t even overclock.
More efficient graphic cards require more power than a standard graphic card that 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 a long term remedy.
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.
This will help you determine the level of performance of your graphic card. You should compare core counts with the same architecture as comparing AMD and Nvidia would be of no use.
Trillions of Floating Point Operations per Second
Like the previous point, this determines the level of performance but theoretically. It is calculated by multiplying core count with clock speed with two, which should also be compared in the same architecture otherwise it is useless. This 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. Our experts have also recommended you the best graphics card for deep learning to save you from all the time and effort of finding one or going through the entire article. So without any further ado buy the best GPU for deep learning now.
Moving on to the Frequently Asked Questions
How much should I spend on a GPU?
It is advisable to spend at least 200 which would get you a mid-range mainstream graphic card but for an excellent high-performing one you need to spend 800.
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 that you are exploring deep learning in your spare time then you must get at least 6 GB GPU or more.
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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