Everything You Want to Know About the Graphics Card for Deep Learning
The era of artificial intelligence is booming and with every passing day, we get to see something amazing. Artificial intelligence is massively impacting the world and it is an undeniable part that it adds more value to everything.
When it comes to Deep Learning models or machine learning, the Graphics Processing Unit is the soul and is a curial part of Artificial intelligence. A single chip is power-packed and used for both graphical as well as mathematical calculations. The best part is, it frees up the CPU cycle which eventually increases its productivity.
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Do we need GPU for Deep Learning?
The need depends on you but if you want to work on high-quality images and video, it is best to invest in a reliable GPU. GPU is capable enough to process the images at a faster rate and also efficiently as compared with the CPU. Without a GPU the work will be slow and you may end up being frustrated. The GPU in 2020 is any day a worthy investment.
What to Look In a GPU?
If you are a gaming lover, videographer, graphic designer, or want Deep Learning / machine learning, it is advisable to invest in a reliable GPU. Here are the top factors which you should be considering before investing in a Graphics Processing unit.
The first and foremost factor before investing in a GPU is to check for compatibility with your device. Double-check the physical space your case can accommodate, make sure to check for the power supply that includes the amp’s supply. While selecting the Video card for deep learning check the Display ports and connectors options.
The role of the system is curial while choosing the graphics card for Deep Learning 2020. Understand the limitations of the system and invest in the right graphic cards. If you have an older 1280 x 1024 resolution monitor, a mid-range graphic processing power unit will do the job for you.
Values of TDP
The graphic processing unit also heats up during the work and it is indicated by the TDP value. The TDP value reflects the power needed to keep GPU at a moderate temperature. If the GPU needs more power, it will lead to more heat. Hence, if you are planning to invest in a graphics card for deep learning, it is advisable to opt for GPU with lower TDP value or a water cooled GPU
Bigger is not always better, the same goes for graphic card memory bandwidth as well. If you have an ultra-high-resolution monitor, it is better to invest in a high-quality RAM else it does not make much difference. While looking for a graphics card for deep learning pay more attention to the bandwidth. It determines the performance and it is better to look for 1 GB for GPU.
CUDA Core or Stream Processors
CUDA cores is an ideal choice for gamers and it is equivalent to AMD’s stream processors. The GPU with a higher number of CUDA cores or Stream processors is better in performance and a perfect choice for a video card for deep learning.
List of 5 Best Graphics card for Deep Learning
For Deep Learning, it is better to have a high-end computer system, it will provide the best practical experience. Here is the list of 5 best video card for deep learning 2020.
1. RTX 2070 super
If you are planning to invest in a cheap and reliable GPU, RTX 2070 super is the best choice. It provides up to 1815 MHz core clock and offers incredible performance.
This GPU consists of 40 streaming processors with 8 tensor cores each. It supports real-time ray tracing and is totally a value for money option. Be it the configuration or the bandwidth or the speed, RTX 2070 super is the most cost-efficient choice. We feel its much better than titan rtx.
2. Nvidia GeForce RTX 2080Ti
Nvidia GeForce RTX 2080Ti is a steal deal if you want to invest in the best 4k GPU in 2020. This powerful graphic card offers 60 FPS in 4k, the silicon pieces are brilliantly engineered and the design is incredible.
Nvidia GeForce RTX 2080Ti is faster and 3 fan keeps the system cool and makes it perfect for this category. While using this GPU, you might require a high power supply but it supports up to 4 monitors in one go.
3. GTX 1660 Super
The GTX 1660 Super is another incredible choice for machine and deep learning and it does not pinch your pocket much. The GTX 1660 Super has a clock speed of the Vanilla GTX 1660 and is loaded with GDDR6 memory.
It is an entry-level GPU for Deep Leaning; it comes with 2 fans and does not support the ray-tracing model but it supports DirectX Ray-tracing. This GPU needs power support of 127.4 Watt but the overall architecture is brilliant and great for both entry and mid-level Learning.
4. RTX 2080 Super
RTX 2080 Super is another smart looking GPU and an ideal choice for Deep Learners. This GPU consists of 3 fans and comes with a super clocked system. This device supports DLSS and is a perfect choice for high-end gaming.
The RTX 2080 Super edition has also activated a 3072 shaders processor and it supports Raster Operations Pipeline (ROP) units. The speed of the clock is 1815 Mhz and great for regular use. The powerful GDDR6 memory 8GB and 250-watt TDP make this GPU a complete package. It is better than rtx 2080 ti
5. Nvidia GeForce GTX 1660Ti
The Nvidia comes as a budgeted GPU and it offers half-precision calculation, the speed is 16 times faster as compared to the Floating-point 32 calculations. This GPU comes with 6GB of video memory bandwidth and provides a bandwidth of 336 GB per second.
The Nvidia GeForce GTX 1660Ti comes with multiple port options and supports HDMI port and also 3 Display port 1.4. It is an ideal choice for all those who are looking for budget-friendly GPU only for Deep Learning.