Best GPUs For Deep Learning in 2022

What are the best GPUs for deep learning in 2022? Deep Learning is an area of machine learning research, which has led to breakthroughs in speech recognition, visual object recognition, and natural language processing. One field that can benefit from these advances in computer vision, which had seen a significant increase in use cases due to recent improvements in image-recognition algorithms.

Consequently, there’s been an equal rise in demand for GPU hardware that supports this type of computation. So what should one look for when shopping around? Let’s look at a few different types of hardware and the pros and cons of each.

Best GPUs For Deep Learning
Source: Google

First things first – it’s essential to be clear about what we mean by “deep learning.” In this context, deep learning refers to networks with multiple hidden layers and GPUs optimized for computationally intensive tasks. To train these deep neural nets, a tremendous amount of parallel computing power is needed – and it must also be highly programmable.

This means that not all GPUs are suitable for deep learning applications. Still, those specifically built for this use case have been designed with the computational capabilities required to support these networks. In addition, they have also been optimized to minimize memory latency, which plays a crucial role in training these models.

Best GPUs For Deep Learning at a Glance:

  1. NVIDIA Titan RTX Graphics Card
  2. Nvidia Tesla v100 16GB
  3. PNY NVIDIA Quadro RTX 4000
  4. ZOTAC GeForce GTX 1070
  5. ASUS GeForce GTX 1080
  6. Gigabyte GeForce GT 710
  7. EVGA GeForce RTX 2080 Ti XC
  8. EVGA GeForce GTX 1080 Ti 

Our Recommendation for the Best GPUs For Deep Learning in 2022

NVIDIA Titan RTX Graphics Card

NVIDIA Titan RTX Graphics Card
Image credit: Amazon

SPECIFICATIONS

Brand: NVIDIA | Graphics RAM Type: GDDR6 | Graphics Ram Size: 24 GB | Tensor Cores: 576 |

REASONS TO BUY

Best Performance for deep learning

Excellent frame buffer

REASONS TO AVOID

Expensive

Less powerful than Titan V

NVIDIA Titan RTX Graphics Card is NVIDIA’s newest graphics card that can be used for deep learning. The most powerful GPU for deep learning has a Turing Tensor Core architecture. The latest GPU technology from NVIDIA delivers up to 120 teraflops of deep learning performance with real-time ray tracing and artificial intelligence features. This new architecture also includes 576 Tensor Cores for processing AI algorithms and 72 RT cores for accelerating ray-traced rendering. The result is an easy way to get high-end machine learning performance on our desktops.

Besides, NVIDIA Titan RTX Graphics Card will give you the best GPU experience in your home or workstation while giving your creative deep learning projects some extra power to back it up. This GPU is the complete package for computer graphics, whether you are building prototypes or final products. This NVIDIA Titan RTX Graphics Card has 4K resolution graphics at 60 frames per second with one monitor and can drive multiple monitors in surround view depending on your monitor specifications. 

Another big focus of the presentation was how this new technology would create high-fidelity simulations using Project Holodeck with natural world physics properties. In addition, full room experiences will be able to run on the NVIDIA Titan RTX, opening home-like experiences for gamers and professionals alike to explore high-end simulation environments like never before.

Nvidia Tesla v100 16GB

Nvidia Tesla v100 16GB
Image credit: Amazon

SPECIFICATIONS

Brand: PNY | Graphics RAM Type: 72-Pin EDO SIMM Memory | Graphics Ram Size: 16 GB | Tensor Cores: 640 |

REASONS TO BUY

Powerful Graphics Processor

Excellent Performance for Deep Learning

REASONS TO AVOID

Expensive

Nvidia’s Tesla v100 is a powerful new card that can handle complex AI and deep learning tasks. It also has 16GB of HBM2 memory, making it capable of handling large datasets. The card also has a high-bandwidth memory interface, which allows it to transfer data faster than other cards.

Nvidia Tesla is designed for enterprise, data center, and hyper-scale applications that require the highest performance and deep learning capabilities. They offer 640 Tensor Cores and connect multiple V100 GPUs at 300 GB per second. They can train virtual personal assistants and teach autonomous cars to drive with these GPUs. 

Best GPUs For Deep Learning
Source: https://www.nvidia.com

However, The best GPU for deep learning servers is designed for data centers and supercomputers. The v100 has 5,120 CUDA cores, making it the most powerful GPU available. The v100 is also very energy-efficient, consuming just 250 watts. The GPU is ideal for data centers and supercomputers, where power consumption is paramount.

PNY NVIDIA Quadro RTX 4000

PNY NVIDIA Quadro RTX 4000
Image credit: Amazon

SPECIFICATIONS

Brand: PNY | Graphics RAM Type: GDDR6 | Graphics Ram Size: 8 GB | Video Output Interface: DisplayPort |

REASONS TO BUY

Latest versions of GDDR memory

Turbo GPU

REASONS TO AVOID

Pricey

Less Ram size

It continues to push the boundaries with its latest GPU architecture, Turing. NVIDIA Quadro RTX 4000 is a professional-grade graphics card that delivers up to 6x faster performance than previous generations for deep learning training and rendering applications.

It also offers 8GB of memory capacity for large datasets and multi-display setups. This incredible performance is matched by equally impressive features, including an all-new display engine capable of both 8K HDR video playback as well as 10-bit color support in every single frame.

You can create with confidence, knowing that your workstation is equipped to handle the most demanding design visualization workloads without missing a beat.

Nvidia Quadro RTX 4000 graphics card brings significant advances to content creators and designers of all kinds, including Machine learning developers, Deep learning developers, architects, engineers, manufacturing professionals, and many more.

ZOTAC GeForce GTX 1070

ZOTAC GeForce GTX 1070
Image credit: Amazon

SPECIFICATIONS

Brand: ZOTAC | Graphics RAM Type: GDDR5 | Graphics Ram Size: 8 GB | Video Output Interface: DisplayPort, DVI, HDMI |

REASONS TO BUY

Better than Titan V and GTX980 Ti

Good software with simple UI

Combination of both elegant and solid build quality

REASONS TO AVOID

Limited overclocking

Less Ram size

The ZOTAC GeForce GTX 1070 is a graphics card that will not disappoint. It has an 8GB GDDR5, 1920 Cuda cores, and a boost clock of 1708 MHz. The GPU also provides 3x DisplayPort 1.4 connections for monitors, while the HDMI 2.0 port supports 4K resolution at 60Hz, and improved cooling & stability.

This best budget gpu for deep learning can be overclocked to deliver even more performance than it already has without any problem. This card has fantastic features, so do not miss out on it. The ZOTAC GeForce GTX 1070 is a unique graphics card that will easily fit into anyone’s computer and provide the necessary power needed for any game.

ASUS GeForce GTX 1080

ASUS GeForce GTX 1080
Image credit: Amazon

SPECIFICATIONS

Brand: ASUS | Graphics RAM Type: GDDR5 | Graphics Ram Size: 8 GB | Video Output Interface: DisplayPort, DVI, HDMI |

REASONS TO BUY

Excellent performance

Power efficient

Support 4K and VR

4 big fan

REASONS TO AVOID

Pricey

Fans don’t turn off in idle

The GeForce GTX 1080 is the most advanced gaming graphics card ever created, delivering genuinely game-changing performance and a level of realism never experienced before. This GPU has ultra-fast FinFET technology to deliver the incredible speed and power efficiency. This means you can enjoy high frame rates on 4K TVs or monitors, support for DirectX 12 features like Microsoft’s new Game Mode, as well as video playback in 360 degrees for VR.

G-SYNC technology synchronizes the display refresh rates to your GeForce GTX GPU for the fast, smooth training you would expect. With G-SYNC, frames are synced with the monitor’s refresh rate to reduce tearing and stuttering. You can enable G-SYNC functionality over both DisplayPort and HDMI connections – all you need is an NVIDIA GeForce GTX graphics card and a G-SYNC capable monitor.

The options are nearly endless, with a wide variety of hardware, displays & configurations. In addition, ROG Strix GeForce® GTX 1080 comes overclocked to 1835 MHz in OC mode, so you get the best deep learning training experience from this powerful GPU.

ASUS graphics cards are produced using industry-leading Auto-Extreme technology, an industry-first 100% automated manufacturing process, and feature premium Super Alloy Power II components that enhance efficiency, reduce power loss, decrease component buzzing under load, and lower thermal temperatures for unsurpassed quality and reliability.

In addition, with Aura RGB Lighting, ROG Strix graphics cards can display millions of colors and six different effects for a personalized gaming system.

Gigabyte GeForce GT 710

Gigabyte GeForce GT 710
Image credit: Amazon

SPECIFICATIONS

Brand: Gigabyte | Graphics RAM Type: GDDR5 SDRAM | Graphics Ram Size: 2GB | Video Output Interface: DVI, HDMI |

REASONS TO BUY

Easily install

Less overheat

Quality output

REASONS TO AVOID

Not good software is installed

Constantly screen flickers

Are you a deep learning developer who wants to train and extract features from images but if you don’t have a budget then this card is perfect for you? This Gigabyte GeForce GT 710 can run any deep learning task with medium settings at 4096 X 2160 resolution. With 2GB of GDDR5 RAM and a 64-bit memory interface, it’s more than not enough power for your training.

The GT710 also features HDMI and DVI ports compatible with most monitors or TVs out there. And because it is fanless, this card will work quietly without annoying fans as some other cards do. So if you’re looking for something best cheap GPU for deep learning yet still powerful enough to handle your deep learning algorithm, then the Gigabyte GTX GT 710 is what you need.

EVGA GeForce RTX 2080 Ti XC

EVGA GeForce RTX 2080 Ti XC
Image credit: Amazon

SPECIFICATIONS

Brand: EVGA | Graphics RAM Type: GDDR6 | Graphics Ram Size: 11 GB | Graphics Coprocessor: NVIDIA GeForce RTX 2080 Ti |

REASONS TO BUY

Support 2K and 4K

Beast performance

The air cooler for GPU is cool

Powerful single GPU

REASONS TO AVOID

Pricey

Thick radiator

The EVGA GeForce RTX 2080 Ti XC is the ultimate graphics card. It has a new generation of GPU with 4352 CUDA Cores, making it the most powerful gaming GPU on the planet. In addition, the GeForce RTX 2080 Ti delivers truly unique real-time ray-tracing technologies for cutting-edge, hyper-realistic lighting and special effects.

It is the best GPU for deep learning with the most advanced graphics card created with the EVGA GeForce RTX 2080 Ti XC.

The NVIDIA GeForce RTX 2080 Ti is the flagship GPU of the latest generation. It is designed for enthusiasts, delivering 10% better performance over the last gen’s GTX 1080 Ti on average. It has more cores than its predecessor – 4352 vs. 3584 – and runs at more than 1.5GHz.

In addition, it implements new technologies, like real-time ray-tracing and AI-enhancements for photorealistic graphics and smooth gameplay across all titles. It is also equipped with an 11GB GDDR6 memory, enabling 452GB/s of bandwidth for a fluid gaming experience with 4K and VR.

The GeForce RTX 2080 Ti XC uses iCX2 technology, which delivers 9% better cooling performance and is tested over four times more than the traditional heat spreader design.

In addition, it has an adjustable RGB LED that looks stunning and can be easily configured to match any PC build. A windowed HB SLI Bridge is also included in the box for real-time monitoring of the cooler’s performance.

EVGA GeForce GTX 1080 Ti 

EVGA GeForce GTX 1080 Ti 
Image credit: Amazon

SPECIFICATIONS

Brand: EVGA | Graphics RAM Type: GDDR5 | Graphics Ram Size: 11 GB | Graphics Coprocessor: NVIDIA GeForce GTX 1080 Ti |

REASONS TO BUY

11 GB VRM

Beast performance

DisplayPort-to-DVI adapter in the box

Good cooling performance

Safety fuse to protect components

REASONS TO AVOID

Pricey for most the people

Not good design as a competitor

The EVGA GeForce GTX 1080 Ti is the most powerful graphics card ever created. This fantastic piece of technology will not only make your games run smoother and also for the best deep learning training, but it will also allow you to enjoy applications such as 3D rendering or video editing more than on any other GPU on the market.

The GTX 1080 Ti has 11GB of GDDR5X memory that runs at 11Gbps with a 352-bit bus width, giving you enough power to render 4k videos in real-time without sacrificing performance. It’s fair to say that this is the ultimate computer graphics card for gamers who want nothing but raw power at their disposal.

The GeForce GTX 1080 Ti is the latest addition to the ultimate gaming platform. Compared with its predecessors, it delivers improved speed and power efficiency while also providing new gaming technologies and breakthrough experiences.

NVIDIA Pascal powers GeForce® GTX graphics cards—the most advanced GPU architecture created. In addition, the innovative NVIDIA Game Works™ technologies offer smooth, cinematic gameplay. “Ultra-high resolution (UHD) delivers 4x the resolution of 7680 x 4320 content, enabling much sharper and crisper images with delicate details during gaming.

DesignGraphics cardsRetailer
NVIDIA Titan RTX Graphics Card
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Nvidia Tesla v100 16GB
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PNY NVIDIA Quadro RTX 4000
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ZOTAC GeForce GTX 1070
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ASUS GeForce GTX 1080
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Gigabyte GeForce GT 710
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EVGA GeForce RTX 2080 Ti XC
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EVGA GeForce GTX 1080 Ti 
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Our Opinion

GPUs are essential tools for deep learning research. We test and review the best GPUs for deep learning in 2022. Our findings show that the NVIDIA GeForce RTX 2080 Ti is the best GPU for GAN and CNN models.

We benchmarked the RTX 2080 Ti XC on two deep learning models: GAN and CNN. The results show that the RTX 2080 Ti XC is excellent for deep learning.

The RTX 2080 Ti XC achieved a higher throughput than the GTX 1080Ti on both the GAN(Generative adversarial networks) and CNN models. The GPU also performed a lower inference time than the GTX 1080Ti.

Overall, the RTX 2080 Ti XC is an excellent choice for deep learning, providing superior performance on both GAN and CNN models.

Conclusion

Final Word, Deep learning is a branch of machine learning, and it’s one of the most promising forms of artificial intelligence. It uses algorithms to teach computers how to learn from data without being explicitly programmed. To do this effectively, you need just as much processing power as possible – your GPU needs to handle deep neural nets with ease.

Our team has put together a list of some of the best GPUs available today that will provide you with more than enough horsepower so you can train those networks quickly. Which model have you been using? Let us know in the comments below.

Frequently Asked Questions

What is the Best GPU For Convolutional Neural networks (CNNs)?

GeForce RTX 2080 Ti XC is best for convolutional neural networks. It works smoothly for feature extraction.

What is the Best GPU For Generative adversarial networks (GANs)?

NVIDIA Titan RTX is best for Generative adversarial networks. It is also possible to train Generative adversarial networks (GANs) using a single CPU, but this will significantly increase your training time and slow down performance. We recommend running dual GPUs on one machine for better performance or faster training times.

Which GPU Is Best For Tensorflow?

There is no one-size-fits-all answer to this question, as the best GPU for TensorFlow will vary depending on your specific needs and setup. However, Nvidia’s GPUs RTX 2080 Ti tends to be better suited for TensorFlow than those from other manufacturers.

John
John is a tech writer who specializes in laptop and PC hardware. He has been writing about technology for over 3 years, and his work has been featured in major publications such as Thrive Global.