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13 Best GPUs for Deep Learning in 2021 [Guide]

Updated on July 14, 2021



⭐️ GPU For Deep Learning

It is a hardcore fact that deep learning requires a lot of hardware to solve complex computational processing. The data scientists have swiftly moved from CPUs to GPUs. They devote proportionally more transistors to arithmetic logic units and fewer to caches and flow control as compared to CPUs.

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Before we get to know about the best GPUs for machine learning in 2021, lets discuss the importance of GPUs in deep learning .

Machine learning is a subset of artificial intelligence function that provides the system with the ability to learn from data without being programmed explicitly.

Machine learning is basically a mathematical and probabilistic model which requires tons of computations. It is very trivial for humans to do those tasks, but computational machines can perform similar tasks very easily.

In this post, we will cover the best GPUs for machine learning available in the market. NVIDIA GPU Series 2021 is recommended by technical experts when it comes to choosing a performing GPU for an enterprise data center. Let’s dig into the topic and get to know more about it. In 2021, HP launches new laptops with wireless AirPods and upgrade NVIDIA GeForce graphics.

Important Facts:

GPUs are faster than CPUs and suitable for the computation of AI and deep learning applications. GPUs are optimized for training artificial intelligence and deep learning models as they can multiprocess neural networks simultaneously.

GPUs are a safer bet for fast machine learning because, at its heart, data science model training consists of simple matrix math calculations, the speed of which may be greatly enhanced if the computations are carried out in parallel. (Source: Reddit)

GPUs are faster for computing than CPUs. With large datasets, the CPU takes up the task sequentially which is not recommended for deep learning. GPUs are equipped with VRAM (Video RAM) memory. Thus, the CPU’s memory can be used for other tasks.

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What is A GPU?

GPU is a graphics processing unit, a logic chip known as a processor. It mainly helps the PC or laptop to offer ideal graphics and visuals to the user. This is suitable for coders, designers, video editors, and just about anybody who wants top-notch images.

You can find the best GPU for deep learning in the plug-in card. It is located in the chipset on the motherboard of a PC.  CPU or central processing unit is considered as the main functional unit of a PC or laptop but its working depends on the GPU.

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Is GPU necessary for deep learning?

So, if you are planning to work on other ML areas or algorithms, a GPU is not necessary. If your task is a bit intensive, and has a manageable data, a reasonably powerful GPU would be a better choice for you. A laptop with a dedicated graphics card of high end should do the work.

The concept of deep learning involves mathematical calculations and extreme operations, including matrix multiplication. It is a field that depends on the kind of GPU you plan on using for your calculations.

So, we can consider the GPU as an integral device for actualizing the concept of deep learning. Choosing a high-performing GPU will not only help you compute fast but also help you achieve outstanding performance. You must be able to design the kind of products you plan on using Artificial Intelligence.

A distinctive GPU will help you obtain a high-quality image with HD definition. Investing in purchasing a top-quality GPU is a smart move for acquiring the best outcomes especially with deep learning.

You can process the images and videos at a much quicker rate and increase the efficiency of your CPU. Users need to understand that their workflow can become sluggish when not using a prominent GPU.

 The Best GPUs for Deep Learning in 2021

GPUs for deep learning

⭐️Parameters to Consider While Choosing a GPU for Deep Learning

In the rapidly growing market of GPUs, a variety of options are available that are designed to keep the need of a graphics designer, video editor, deep learning consultant, or somebody interested in AI in mind.

Listed below are some of the main parameters to consider before purchasing the GPU.

Compatibility – Here the compatibility means the support of the power supply and enough space on the device. The better synchronization of these two will make the GPU work effectively.

Platform – Deep learning is a conceptual concept for which the better support of graphics and high screen visibility is a must. So, while choosing a GPU , you must pick one that supports all types of processors and the latest versions of the display.

TDP value – At times, the GPU might be heated up. It is indicated with the TDP value. You must ensure that your GPU is at a cool temperature. When your unit requires more power for functioning, then it can heat up quicker.

Memory Capacity – A larger memory in the device is considered as bliss and foremost requirement for running AI and deep learning applications. In deep learning, we make use of intense power and memory size. Those owning ultra-high-resolution monitors may want to use top-notch RAM. Therefore, GPUs having memory in TBs are required.

The stream processors – The stream processor is also known as the CUDA core. These are suitable for professional gamers and deep learning. Using a GPU with a high CUDA core increases work efficiency in deep learning applications.

GPU Factors to Consider When Buying or Upgrading

A perfect GPU selection can help you produce benefits in integrating and clustering deep learning applications. In the long run, all enterprises will run these complex applications in their routine work. Hence, opting for a production-grade or data center GPU is a wise step.

Along with scaling and high performance of GPUs, 3 main factors are important to consider. They are:

Note: We have considered NVIDIA GPUs with the latest configurations.

Ability to Interconnect GPUs

Interconnections of GPU are directly related to the scalability of your implementation and the ability to use multi-GPU and distributed training strategies. Here we must note that NVIDIA has removed interconnections on GPUs below RTX 2080.

Supporting Software

NVIDIA GPUs are the best supported in terms of machine learning libraries and integration with common frameworks, such as PyTorch or TensorFlow. The NVIDIA CUDA toolkit includes GPU-accelerated libraries, a C and C++ compiler and runtime, and optimization and debugging tools. It enables you to get started right away without worrying about building custom integrations.

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Another factor to consider is NVIDIA’s guidance regarding the use of certain chips in data centers. As of a licensing update in 2018, there may be restrictions on use of CUDA software with consumer GPUs in a data center. This may require organizations to transition to production-grade GPUs.

3 Algorithm Factors Affecting GPU Use

In our experience helping organizations optimize large-scale deep learning workloads, the following are the three key factors you should consider when scaling up your algorithm across multiple GPUs.

  • Data parallelism
  • Memory use
  • Performance of the GPU

⭐️ Best GPU for Deep Learning in 2021 – Top 13

Choosing the best GPU is a difficult task as the definition is vast. Some need a scalable GPU while others opt for a bigger capacity. Here are some considerations that can help you select a GPU or set of GPUs that is best suited for your needs.

RUN AI s a GPU management and workload orchestration for machine learning infrastructure. With Run: AI, you can automatically run as many compute-intensive experiments as needed. Some of its capabilities are:

  • Advanced visibility
  • No more bottlenecks
  • A higher level of control

gpu performance in deep learning chart

Some of the industries best GPUs in 2021 are

NVIDIA TITAN XP Graphics Card (900-1G611-2530-000)

NVIDIA TITAN XP Graphics Card - GPU for deep learning

This product has been professionally inspected and tested to be fully functional by the sellers. The card can be installed on the latest gaming machines and can be used on servers also. The product is specially designed to cater to the needs of gamers and miners.

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NVIDIA Titan RTX Graphics Card

NVIDIA Titan RTX - GPU for deep learningThis card is ideal for neural network training and inference. A large amount of RAM, plus support for the NVLink interconnect to combine the memory pools of two such cards. This can handle enormous data sets.

The powerful new graphics card is also being touted as a tool for studios that work on games and VR experiences built around ray tracing. It is an affordable solution in comparison to other similar options available in the market.

Pick the Graphic Card of Your Choice From Our RTX Graphic Cards Collection

ZOTAC GeForce GTX 1070 Mini 8GB GDDR

ZOTAC GeForce GTX 1070 gpu for deep learning

A dual-link DVI port GPU is compact, and can efficiently handle VR titles because it is immensely powerful. It has an impressive overclock and is fairly quiet. However, it fits into the latest PCs or laptops only.

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ASUS GeForce GTX 1080 8GB

Considered as one of the best for deep learning with a higher core count, fast memory. and speed. It is a product having good value for money accessed with many advanced features and consistent performance.

Browse Through Our Huge Collection of Asus Graphic Cards

Gigabyte GeForce GT 710 Graphic Cards

Gigabyte GeForce GT 710 Graphic Cards have a higher boost clock, higher core count, fast memory. It is an affordable choice showing excellent performance with deep learning applications. The compact size and 4K resolution are added features.

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EVGA GeForce RTX 2080 Ti XC

The graphics card has six times better performance compared to previous-generation graphics cards. With a Real-Time RAY TRACING in games for cutting-edge, hyper-realistic graphics, it has dual HDB fans offering higher performance cooling and much quieter acoustic noise. The graphics card can adjust with the lighting demand of PCs by tuning utility monitors

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EVGA GeForce GTX 1080 Ti FTW3 Gaming

This product has been professionally inspected and tested to be fully functional by the sellers. The product has 9 additional temp sensors to monitor Memory and VRM The graphics card has a real base clock of 1683 MHz, 11264MB memory,and comatability with 32 /64 bit Windows OS. The graphics card is good for personal gaming use.

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PNY NVIDIA Quadro RTX 8000

A safe product that can bear the installation damages is one of the latest graphics cards enabling real-time ray tracing. A single Quadro RTX 8000 board can render complex professional models with physically accurate shadows, reflections, and refractions to empower users with instant insight.

Choose from our best Collection of PNY NVIDIA Quadro RTX Graphic cards which suits your PC.

Working in concert with applications leveraging APIs such as NVIDIA OptiX, Microsoft DXR, and Vulkan ray tracing, systems based on Quadro RTX 8000 will power truly interactive design workflows to provide immediate feedback for unprecedented levels of productivity.

PowerColor VGA AXRX 570 Graphic Card

PowerColor VGA AXRX 570 Graphic Card has a reference design of AMD with increased clock speed and memory speed. However, its performance is measured excellent for 1080 & 1440p. It is known for its outstanding cooling that is the prime requirement of a deep learning application.

Select what suits you PC from our best collection of Graphic Card.

NVIDIA GeForce RTX 2080 Graphics Card

Nvidia GeForce RTX 2080 Super Graphic Card is among the top graphic cards because of its high performance. It is a preferred choice of most enterprises for its effective cooling, automatic overclocked property. However, the big size is sometimes a troublesome feature with the advanced compact size machines.

Get the full range of NVIDIA GeForce Graphics Card here

Best Nvidia GPU’s for Deep Learning: Our Top Picks for 2021 👌

If you are looking for a GPU for deep learning then you can have a look at the top picks of the Saitech Technologies that enterprises have chosen for running large-scale projects and data centers. Here is the list of the best GPUs in 2021 at Saitech Technologies, USA.

Our Top Picks for Large-Scale Projects and Data Centers –


GeForce RTX 2080 Ti - Best nvidia gpu for deep learningThe RTX 2080 Ti is the best GPU for deep learning for almost everyone. Despite being a branded as a consumer-grade “gaming card”, it remains the workhorse of choice for state-of-the-art research among grad students and professors at every university (even at schools with relatively large budgets, like MIT).

NVIDIA Tesla V100

(GPU computing processor – Tesla V100 – 16 GB – for UCS SmartPlay Select C240 M5, SmartPlay Select C240 M5L, SmartPlay Select C240)

The NVIDIA Tesla V100 is a Tensor Core enabled GPU that was designed for machine learning, deep learning, and high-performance computing (HPC).

It is powered by NVIDIA Volta technology, which supports tensor core technology, specialized for accelerating common tensor operations in deep learning. Each Tesla V100 provides 149 teraflops of performance, up to 32GB memory, and a 4,096-bit memory bus.

Nvidia Tesla p100

(GPU computing processor – Tesla P100 – for UCS C220 M4S, Smart Play 8 C240, Smart Play C220 M4, SmartPlay Select C220 M4S)

The Nvidia Tesla p100 is a GPU based on an NVIDIA Pascal architecture that is designed for machine learning and HPC. Each P100 provides up to 21 teraflops of performance, 16GB of memory, and a 4,096-bit memory bus.

NVIDIA Tesla K80

(GPU computing processor – Tesla K80)

The Nvidia Tesla K80 is a GPU based on the NVIDIA Kepler architecture that is designed to accelerate scientific computing and data analytics.

It includes 4,992 NVIDIA CUDA cores and GPU Boost™ technology. Each K80 provides up to 8.73 teraflops of performance, 24GB of GDDR5 memory, and 480GB of memory bandwidth.

End notes

From the above discussion, we can say that there are a variety of options available when it comes to choose a GPU for deep learning. NVIDIA  is playing a great role in meeting the high-end demand of GPUs for data centers and enterprises. The RTX 3070 series is one of the latest ones in the deep learning market. The GPU is optimized as per the need of an AI application In gaming computers and other deep learning applications , it is one of the good options.

Selecting the right unit is crucial to ensure that you enjoy your deep learning and gaming experience. We highly recommend the NVIDIA series as worth considering. Each of the products have unique features. They are latest top-notch and have commendable specifications.


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