Best GPU(s) for Deep Learning in 2021
Ashwini Manjunath
Successful Digital Entrepreneur Coach at AIKidsProgramming.com Author, Speaker, trained 5000+ Students
WHAT IS GPU?
GPU (Graphics Processing Unit) : A programmable logic chip (processor) specialized for display functions. The GPU renders images, animations and video for the computer’s screen. GPUs are located on plug-in cards, in a chipset on the motherboard or in the same chip as the CPU.
The CPU (central processing unit) has often been called the brains of the PC. But increasingly, that brain is being enhanced by another part of the PC – the GPU (graphics processing unit), which is its soul.
To catch the nature of the data from scratch the neural net needs to process a great deal of information. There are two different ways to do so — with a CPU or a GPU.
Deep Learning is for the most part involved in operations like matrix multiplication.
Deep learning is a field with exceptional computational prerequisites and the choice of your GPU will in a general sense decide your Deep learning knowledge.
Having a fast GPU is an essential perspective when one starts to learn Deep learning as this considers fast gain in practical experience which is critical to building the skill with which you will have the capacity to apply deep learning to new issues.
Without this fast feedback, it just sets aside an excessive amount of opportunity to gain from one’s missteps and it very well may demoralize and disappointing to go ahead with Deep learning.
GPUs were created to deal with heaps of parallel computations utilizing a large number of cores. Additionally, they have an extensive memory bandwidth capacity to manage the information for these computations. This makes them the perfect product equipment to do DL on.
“If you want any real results you should be using a GPU“
The most critical purpose behind picking a powerful GPU is saving time while prototyping models. On the off chance that the networks prepare quicker the feedback time will be shorter.
Hence, it would be simpler for my understanding to come to an obvious conclusion regarding the presumptions I had for the model and its outcomes.
Picking a GPU for Deep Learning
There are fundamental qualities of a GPU identified with DL are:
Memory bandwidth — as examined over, the capacity of the GPU to deal with vast data. The most imperative execution metric.
Processing power —shows how quick your GPU can crunch information. It will process this as the quantity of CUDA cores increased by the clock speed of each core.
Video RAM size — the size of data we can have on the video card at any point. In the event that you will work with Computer Vision models etc, you need this to be as substantial as reasonable. Particularly, on the off chance that you need to do some competitions. Amount of VRAM isn’t so essential for Natural Language Processing (NLP) and working with clear cut information.
For a single card, any desktop processor and chipset like Intel Core i7-8700K and Asus TUF Z270 will use 16 lanes.
But, for two GPUs, you can go 8x/8x lanes or get a processor AND a motherboard that support 32 PCIe lanes. An Intel Xeon with a MSI — X99A SLI PLUS will do the job.
Extra Hardware
GPU needs a computer around it:
Hard Disk: First, we have to peruse the data off the disk. An SSD is prescribed here, however an HDD can also function.
CPU: That information must be decoded by the CPU . Any current processor will do fine.
Motherboard: The data passes through the motherboard to achieve the GPU. For a solitary video card, any chipset will work.
RAM: It is prescribed to have 2 GB of memory for each gigabyte of video card RAM. Having all the more absolutely helps in a few circumstances, similar to when you need to keep a whole dataset in memory.
Power supply: We should give enough capacity to the CPU and the GPUs, with an additional of 75 watts.
Titan XP – Fastest Single GPU Gaming Card
The king of the hill. When every GB of VRAM matters, this card has more than any other on the market. It’s only a recommended buy if you know why you want it.
For the cost of Titan X, you could get two GTX 1080s, which is a lot of power and 16 GBs of VRAM.
Specs
- GPU (Codename) – Pascal GP102
- Shader Units – 3840
- Base & Boost Clocks – 1405 MHz / 1582 MHz
- Memory Size & Type – 12 GB GDDRX5
- Memory Clock – 1426 MHz
- Memory Bandwidth – 547.6 GB/s
- Fans – (1) 62mm Centrifugal
- Ports – (3) DP, (1) HDMI 2.0
- Power Connectors – (1) 8-pin, (1) 6-pin
- Dimensions (LxHxD) – 26.9 x 10.5 x 3.5 cm
- Weight – 1072g
- Warranty – 3 Years
PROS
- Fastest single-GPU gaming card
- Great-looking card
CONS
- Cooler pushed as far as possible
- High price
- Look wise same as Titan X.
GTX 1080 Ti – Very Good Value
Key Features
- GP-102 GPU
- 3854 CUDA cores
- 1.6GHz boost clock
- 11GB GDDR5 X memory at 11GHz
- 3x DisplayPort, 1x HDMI (DP-DVI adapter in box)
- Founders Edition
- TDP: 250W
PROS
- Good 4K performance
- overclockable
- Fastest consumer card.
CONS
- Power hog
- Price is more attractive
Note: This card has a great high-end option, with lots of RAM and high throughput. Very good value.
I recommend this GPU if you can afford it. It works great for Computer Vision or any competitions.
GTX 1080 – Most Powerful Graphics Card
Specs
- VRAM: 8 GB
- Memory bandwidth: 320 GBs/second
- Processing power: 2560 cores @ 1733 MHz (~ 4,44 M CUDA Core Clocks)
- Crazy-powerful and frosty-cool, the Nvidia GTX 1080 Ti is the most powerful graphics card yet – and hits a pricing sweet spot.
PROS
- Titan X-equivalent gaming performance
- Pushes the limits of Pascal
- Best Improved cooling
CONS
- A costly investment for most.
GTX 1070 Ti – Newest Card in Nvidia LineUp
The newest card in Nvidia’s lineup. This will get you the same amount of VRAM (8 GB) and also, 80% of the performance for 80% of the price. Very sweet deal.
Specs
- CUDA Cores 2432
- Texture Units 152
- ROPs 64
- Core Clock 1607+MHz
- Boost Clock 1683+MHz
- Memory Clock 8Gbps GDDR5
- Memory Bus Width 256-bit
- VRAM 8GB
- TDP 180W
- Cooling Dual fan open air
PROS
- Masters 1440p gaming
- Overall performance improved.
CONS
- Mostly higher energy draw
- Gets very hotter than the GTX 1070
GTX 1070 – Best Power Efficient
Specs
- VRAM: 8 GB
- Memory bandwidth: 256 GBs/second
- Processing power: 1920 cores @ 1683 MHz
Key Features
- 1920 CUDA cores
- Base clock speed: 1506MHz
- Boost clock speed: 1683MHz
- 8GB GDDR5 memory
PROS
- Best 1440p performance
- Superb overclockable
- Best power efficient
- Very silent while running
CONS
- Limited SLI capabilities.
GTX 1060
Specs
- VRAM: 6 GB
- Memory bandwidth: 216 GBs/second
- Processing power: 1280 cores @ 1708 MHz
Key Features
- 1,280 CUDA cores
- 6GB GDDR5
- 150W TDP
- 3x DisplayPort, 1x HDMI, 1x DVI
- Third-party cards from ￡239
- VR-ready
PROS
- Best FHD and 1440p Gaming performance
- Silent running
- Very low power consumption.
CONS
- Little expensive than others.
GTX 1050 Ti – Low Power Consumption
Specs
- VRAM: 4 GB
- Memory bandwidth: 112 GBs/second
- Processing power: 768 cores @ 1392 MHz
Key Features
- 768 CUDA cores
- 4GB GDDR5 memory
- Base clock speed: 1,290MHz
- Boost clock speed: 1,392MHz
- DisplayPort 1.4, HDMI 2.0, DVI
- 75W TDP (Powered through PCI-E slot)
PROS
- Handles all games at Full HD
- Very low power consumption
- Very Good design
CONS
- Few games need to be changed often to Medium settings to play.
FINAL WORDS
Finally, below are my GPU suggestions depending upon your financial plan:
The best GPU for Deep learning is the 1080 Ti. It has a similar number of CUDA cores as the Titan X Pascal but is timed quicker.
It’s altogether more financially savvy than the highest point of-the-line Titan XP. The 1080Ti’s single accuracy execution is 11.3 TFlops.
In the event that you have over $1000 -> Get as many GTX 1080 Ti or GTX 1080 as you can.
If you have $700 to $900-> GTX 1080 Ti is very prescribed. Utilize 2 1080 Ti rather than a TITAN XP since GTX 1080 Ti is unquestionably less expensive.
If you have $400 to $700-> Get the GTX 1080 or GTX 1070 Ti.
If you have $300 to $400-> GTX 1060 will kick you off.
If you have under $300-> Get GTX 1050 Ti
Deep Learning has the incredible guarantee of changing numerous aspects of our life. Sadly, figuring out how to employ this ground-breaking apparatus, requires great equipment.
Ideally, I’ve given you some lucidity on where to begin in this journey.
Read More:
10 Best Laptop for Machine Learning Programming