5 Best GPU(s) for Deep Learning [Reviewed]

Deep Learning with GPU

5 Best GPU(s) for Deep Learning [Reviewed]

Best GPU(s) for Deep Learning

Are you looking for the best GPU for deep learning? , if Yes then you are at right place. We have reviewed the 5 best GPU for Deep Learning for you.

For deep learning or machine learning, it is very important to have a high-end computer system. High-end PCs allow one to gain practical experience rapidly. This practical experience is the key to be expertise which enables you to apply deep learning to new problems. A GPU with high processing speed can speed up the process.


You don’t have to have to buy the most expensive PC and PC’s parts. But Deep Learning can be done with a good computer with very high specifications is a must requirement because in DL you have to deal and handle a huge amount of data.

MSI Gaming GeForce GTX 1660 128-Bit HDMI

Surely, that there are a lot of features you should be looking for before buying a new GPU, and to save your time we have written blog which lists the best GPU for deep learning.

What is the GPU?

Just like motherboard, a Graphics Processing Unit (GPU) is a circuit board which include a processor and RAM. The circuit also has an Input and Output BIOS chip, which is the main source of computer’s setting, the chip stores the card’s settings. At the time of startup, the GPU performs a diagnostics on memory.

It isn’t much different between CPU and GPU. The GPU is specifically designed to solve and handle complex geometrical and mathematical calculations that are necessary for the rendering of graphics. GPU produces images and that images need to be stored somewhere and for this purpose card’s RAM is used, every part of the image, every colour, every pixel and its location is stored in the screen.

Different graphics card of different companies has different techniques to help GPU to allow changes to colours, patterns, shading and textures. Some of the faster GPUs have more transistors as compare to average CPUs. This is the reason why GPUs produces more heat and because they produce a lot of heat, they are placed under the fan or a heat sink.

Thanks to its fast processing power, it uses special language by which it analyses or user data. ATI and Nvidia are the two major companies that have been for a long time because they produce high-end GPUs in the market. There are two filters used to improve image quality.

Full scene anti-aliasing (FSAA): this helps in smoothing the edges of any 3D object.

Anisotropic filtering (AF): this helps in making the image crisper.

The Ram also works as Frame buffer, which holds the images until they are displayed. The video RAM operates at very high speed, and video RAM is dual-ported, which means that system can read and write the data at the same time. The Ram is connected to digital-to-analogue convertor (DAV), which translates the image into analogue signals which makes the image useable for a monitor. They are also called RAMDAC. Some GPUs have more than one RAMDAC which improve the performance. Multiple RAMDAV can support more than one monitor.

Deep Learning

Deep Learning is a technique to teach the computer what and how to do what is natural for people. We can easily detect Deep learning techniques in our daily life products like voice control on various devices such as televisions, phone, tablets and Bluetooth speakers.

Deep Learning got a lot of attention lately. Why? The reason behind its sudden attention is you can do what was not possible before. Today, One who has expertise in deep Learning can achieve those results which his ancestors ever dreamt of.

In deep Learning, a computer model is made to perform tasks by reading the images, text or sounds. The help of deep Learning can achieve cutting edge precision which is above human performance.

These computers models are trained under tagged data and neutral architectures with many layers. Learn by example: a driverless vehicle module is designed to operate and recognize the stop sign, and recognize between a lamppost and pedestrian.

Best GPU for deep Learning

Let’s dive in.

NVidia GeForce GTX 1660TiNVidia GeForce GTX 1660TiNVidia GeForce GTX 1660TiNVidia GeForce GTX 1660TiNVidia GeForce GTX 1660Ti

Deep Learning with GPU

It’s hard to beat NVidia GeForce GTX 1660Ti, as it is a budget GPU but doesn’t underestimate its power. Unlike other graphics cards who can’t even play the game at 1080p, this machine is far more than that.

In short, it rules over all in this budget category. Among the deep learners, this GPU is a good option, and it offers half-precision calculation in Floating Point 16 which increases the speed and sometimes by 40% – 50% in comparison of Floating-point 32 calculations.

To compete with AMD, NVidia released a series of GTX 1660Ti with tensor cores, and it came with 6GB of video memory when it was released. The latest model of GTX 1660 super is different from normal GTX 1660 but only in terms of memory which is GDDR6 instead of GDDR5. And because of this golden parameter, this GPU has become a record from the GTX series.

It provides the user with a bandwidth of 336 GB per second. In specs, this GPU is 75% higher than the previous model and 16% more reliable than the “titanium series”. In terms of RAM comparison, the GTX 1080 and RTX 2060 comes with a speed of 352 GB per second and 332 GB per second and stands out.

While the NVidia GeForce offers you 336 GB per second. This device comes with 6GB of GDDR6 VRAM which is perfect for Deep Learning, plus it could give you core clock boost up to 1770 MHz, but the latest model will give you 1860 MHz

According to many experts, the bandwidth speed of the memory is the main key of importance in its construction of neutrals networks, not the computing power. The good thing is its comes with many varieties of port options, and it has 3 Display port 1.4 and also an HDMI port, in short, it is the best choice for those who are looking for the graphic beast in this budget.

RTX 2070 superRTX 2070 superRTX 2070 superRTX 2070 superRTX 2070 super

Deep Learning with GPU

A GPU that joins the ranks of best graphics card for Deep Learning. After the release of 5700XT which is 10% faster than RTX 2070 and actually cost 50$ less than RTX 2070 super, the NVidia improves the system, which results in the TU104 GPU with additional cores and performance.

The GTX super can give up-to 1815 MHz core clock speed. There is no change in the architecture, as it comes with the same Turing architecture as the previous RTX cards, but the additional CUDA cores and higher clock speed makes the GPU faster than previous RTX models.

The GTX 2070 super comes with 40 streaming processors and each SM contains 8 tensor cores, 1 RT core, 4 texture units and 64 CUDA cores. It comes with a speed of 448 GB per second and 8 GB of VRAM. In short, the GTX 2070 super is trimmed down to RTX 2080.

The speed of reference boost recorded is 1770MHz that is higher than overclocked 2070 t edition boost clock of 1710 MHz This includes the 4 additional SMs which means the performance is 22% faster than RTX 2070, theoretically.

Obviously, we can’t rely on just theories so, on observations we came to know that the memory bandwidth and configurations are same as RTX 2070 but in practice, there is results are closer to 10 – 15%. Note that, the sale of regular RTX 2070 reduces because of the arrival of RTX 2070 super, another reason of RTX 2070 fewer sales is that, with any improvement of the card, the prices increases while on the other side the 2070 Super comes with the base price of $499.

With real-time ray tracing enabled, it is good to steal with a price for machine learning. There is no doubt that this GPU will be the chief on the day of black Friday sale.

The configurations look exciting at this price because no one is expecting this at these prices. GPU is packed with an impressive transistor count and its CUDA processor make it 20% faster than GDDR6 video memory.

Unfortunately, the GPU doesn’t support pairing multiple GPUs through SLI. Plus, NVidia introduces this new feature of much higher bandwidth NV Link bridge connector with turing and this GPU doesn’t support this new connector too.

Talking about its gaming benchmark, they weren’t satisfying at all. Although it features impressive 4K gaming that wasn’t expected at this price. The unique features it offers are ray tracing and DLSS.

RTX 2080 SuperRTX 2080 SuperRTX 2080 SuperRTX 2080 SuperRTX 2080 Super

Deep Learning with GPU

Following the series of RTX here comes the RTX 2080 based on TU104 chip and with GDDR6 graphics memory. A cool looking memory card with black colour and three fans at the front and armed with the supercharged and super clocked system. From the gaming point of view, it is the device because it contains the DLSS (Tensor), the thing gamers were waiting for, and playing high-end games in a Ray-traced way.

Because of RT and tensor-less cards, people were directed towards this device. It has activated a 3072 shaders processor, which can only be seen in a super edition. It comes with 64 Raster Operations Pipeline (ROP) units and 192 texture units. Clocked with a speed of 1815 MHz in the founders’ version.

This GPU is also packed with faster 15.5 Gbps GDDR6 memory 8GB of it. The card can give 250-watt TDP. It offers one HDMI port, three display ports and a virtual link USB connector. Two auxiliary power connectors power the device. Both 8-pins and 6-pin power input are available there that are needed to connect to power the GPU.

The card also has USB Type-C port which is a standard port which can easily support next XR devices. No, the card’s specifications aren’t ended yet. Yes, it is fitted with 128 extra shaders in the processors. On comparison, the card this is 15 MHz more over the non-super version. The device’s colours bring a more metallic feels to the card. Its competent Radeon VII, RTX 2080 beats it by the killing specifications.

The gaming specs were only added to let you know about all of the GPU features, and we know you are here to find a device which is suitable for Deep Learning. So, for scientific calculations, this GPU is worth considering because of its tensor cores and 8GB of GDDR6 memory.

On test we can to know that this board is 4 times faster than the GTX series and 2 times faster than Tesla P100 Variant. If Tesla is expensive for you and you still want the specifications like the tensor flow and Ray tracing, then RTX 2080 is a great alternative for you. Note that you must have a powerful enough power supply and a well-ventilated case to support the card.

NVidia GeForce RTX 2080Ti

Deep Learning with GPU

A powerful graphics card that won’t disappoint you and will exceed 60 FPS in 4K while gaming. On the time of its release, it was the fastest GeForce gaming card on the planet. This piece of silicon was beautifully engineered in both design and turing of GPU. But unfortunately it was knocked when the Titan RTX was released.

But the good thing is it isn’t fallen from second place. It is pixel picker card that justifies itself for deep learning or machine learning purposes. But for NVidia GeForce, you must have cash in your pocket. Dropping more than $1000 won’t be an issue because the device is worth the money after all was the world fastest device at once.

It is a three fan device in black matte colour which sounds perfect for building a PC for deep learning purposes. Almost people but it for heavy resources-oriented works that are Deep Learning or machine learning. You will surely be needing a monster power supply for running RTX 2080Ti with a great motherboard that can handle extreme pressures.

You can name this board “A giant” and there would be objections because it comes with 11 GB of GDDR6 VRAM that can give up to 1,635 MHz boost clock.

The best thing is it can easily support up to 4 monitors which are awesome for because some people love to practice and perform deep Learning or machine learning in multiple screens. Thanks to its three fans support which is a great source for cooling. The TU102 GPU is the heart of the device. It comes with 68 SMs and 4,352 CUDA cores. With its 12nm transistor fitted into its 754mm² footprint, it is ruling the GPU world.

The monster is equal to twice size of GTX 1080’s GPU. With 11GB of GDDR6 memory that runs at the speed of 14Gb per second across a dedicated 352-bit aggregated memory bus. This enables the device to run at 616 GB/s of memory bandwidth which is twice of 1080 GPU.

As an RTX card, it contains RTX silicon inside and RTX 2080Ti has 68 RT cores resent in it. The device contains 544 Artificial intelligence-focused tenser cores, which makes the device the best choice for deep Learning. It’s is a high-end spec machine with ray-tracing and awesome potential to handle AI works easily.

The performance is unmatched to any other device. Surely you will be touching the sky with this piece of art. There is no doubt in that, and one must have a lot of cash in their bank account to buy this card, on the other side It’s impossible to find a better graphics card for deep Learning with the same specifications in this price budget.

GTX 1660 Super

Deep Learning with GPU

You know, the super series started with this model and went on. It is a device that is perfect for you to start your machine learning career. If you are on starting your machine learning career than this GPU suits you and your pocket very well.

The good thing is that it is lower in prices than GTX 1660Ti, but it won’t be able to give you a performance like 1660Ti. It is an entry-level graphic card for deep learners. There is not much difference between these both devices, and you can select any one of them for deep Learning.

A board with basic processors and two fans at the front within everyone reach isn’t a bad deal. Among the series of 1660Ti, 1650, GTX 1660, this board ranks in the best graphics card. Unfortunately, we can’t get ray tracing in this model, but it makes sense in this price range. Nope, I am not saying that the card is bad by any means, it has GPU core counts and clock speeds of the vanilla GTX 1660.

The improvement that was made is that it comes with GDDR6 memory which is the major difference among the other 16 series. Here is something more interesting about the devices is that it is overclocked at 14Gbps unlike 12 Gbps of GTX 1660 Ti. Isn’t it weird that the lower version gets both better and lower specs than the existing higher tier.

Note that you have a good power supply because this GPU needs the power of 127.4W in extreme cases. We almost get the same GPU as 1660, and it has same 22 SM units and 1408 CUDA cores which are clocked at 1530 MHz and the same speed of GPU boost clock of 1785 MHz, and the same texture units. In other words, it is same as in 1660. The RAM has been upgraded to 14 GB/s with GDDR6, which results in a massive change of 75 % in memory bandwidth.

There is the unavailability of ray tracing feature but theirs is driver support for DirectX Ray-tracing (DXR) but you won’t get above 30 fps even at 1080p. In short, the new GTX 1660 super look goof from both point of view that is from gaming and for entry-level deep Learning.

It based on the same architecture as other NVidia GPU’s and that is turning architecture. It comes with several connectivity options, and it has 3 display port 1.4 and HDMI 2.0B. The 1660 super in this price knocks out the GTX 1060 series with RX 580 and 590.

Difference between machine learning and deep Learning

Don’t get yourself confused by thinking that machine learning and deep Learning are the same things. No, they aren’t. In the field of machine learning, the computer is trained to perform a specific task.

For example: consider a machine designed to recognize the car won’t recognize motorbikes because it is not designed for it.

While, in deep Learning, a machine is not only prepared to perform a task but also it is taught to give its input.

For example, Google assistant.

Wrapping up

In this blog, we have mentioned all the best of GPU from all price range. So It won’t be difficult for you to choose which GPU suits you and your pocket. All the above GPU are perfect for Machine learning and gaming purposes too. Keep in mind that GPU’s having higher VRAM will result in better performance because they use larger batch sizes which help saturate the CUDA cores.

so, those GPU which comes with higher VRAM will have larger batch sizes. Here is some back of the envelope calculations that result as GPU who have 24 GB of VRAM can fix a 3x larger batch as compare to GPUs that have 8Gb of VRAM. So that’s all folks.

If you need any help or any guidance about GPU’s feel free to leave a comment we will get back to shortly. See you in the next article.