7 of the Best GPUs for Deep Learning

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In recent years, deep learning has become one of the hottest areas of research within machine learning. Deep learning is the application of machine learning techniques to data sets with many features. The field has seen a proliferation of research and development by many companies, across many industries. The latest NVIDIA architectures have been particularly successful at delivering the performance required to run deep learning algorithms.

There are many advantages of deep learning, which is a branch of machine learning. The main advantage is that it is scalable, and also it could be divided into many type of algorithms. However, the performance of deep learning still needs improvement, especially for GPU. The performance of GPU is not high enough to process deep learning. So you need GPU to solve deep learning problems. GPU is not only used as deep learning accelerator, but also used in many other application and it’s popular.

Deep learning is a term used to describe a type of artificial intelligence that involves the creation of software that can learn things on its own. Deep learning is also used in many other fields, including computer vision, speech recognition, and natural language processing. Its a very interesting field and there are many products on the market for personal computers, but today I want to share with you 7 of the best GPUs for deep learning.

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Deep Learning is a popular topic in Machine Learning right now.

Machine Learning is a branch of Artificial Intelligence that uses data to solve AI issues, and Deep Learning is a popular and effective method within Machine Learning.

A Quick Look at the GPUs with the Best Deep Learning Performance

Because GPUs are in such great demand, the links in this article, as of the time of writing, mainly go to search result pages, allowing you to more quickly choose which listing is best for you.

Let’s look at why you may require a GPU (Graphics Processing Unit) before we go into the finest GPUs for Deep Learning.

There are several other excellent GPUs out there, but they may not be accessible right now. Even if any are available, they may be prohibitively expensive. The Titan RTX, for example, is a fantastic GPU, but it is currently unavailable at a fair price.

What is Deep Learning, and how does it work?

Deep learning is a sophisticated machine learning method that uses many hidden layers of neurons in a deep neural network. A neural network, also known as an artificial neural network, is a computer program that is intended to imitate the learning process of human brains. The following is an example of a typical neural network:

7-of-the-Best-GPUs-for-Deep-Learningibm.com is the source of this image.

When it comes to processing output using a neural network, they are all matrix multiplications on the inside.

This is where the graphics processing unit (GPU) comes into play.

GPUs were created to generate 3D pictures, and images have matrix representations as well. As a result, you can see how matrix calculations for deep learning on GPU are comparable.

Why a GPU Might Be Necessary for Deep Learning

When you train a neural network, you’re basically searching for the ideal set of values for the parameters that the network learns.

Let’s say you have a neural network model with a few hundred or even thousands of parameters to train.

In such scenario, the conventional CPU (Central Processing Unit) on your computer may be able to do the task. However, there may be millions or billions of parameters to learn in a deep neural net, also known as deep learning. Hundreds of millions, if not billions, are the figures we’re talking about.

This many operations on your CPU can’t be solely for our deep learning model. It can also do stuff like play music for you, start Chrome so you can browse and read this post, and so on. So, who is there to assist you? Your graphics processing unit.

The more data you have or utilize to train your deep learning model, the more operations you’ll have to do.

As previously stated, CPUs are capable of performing these operations when the numbers are not in the millions. Even then, training your model will take much too long.

A CPU only has a few cores that can execute distinct tasks in a sequential manner. A GPU, on the other hand, may contain hundreds to thousands of cores to execute many tasks at the same time.

As a result, GPUs may utilize multiple cores to do a job in parallel, resulting in increased efficiency. And, since neural network calculations can be done in parallel, this GPU capacity of simultaneous execution is ideal for deep learning.

Another benefit of GPUs is that they have more memory bandwidth than CPUs, which helps with deep learning. A GPU can quickly retrieve huge amounts of data. This isn’t to imply, however, that CPUs are the worst. CPUs are considerably quicker than GPUs in retrieving data, but only when the data isn’t as big.

Let’s have a look at an example. Assume we have a CPU with two cores, one of which is retrieving data for you at a rate of up to 2 GB/sec. We do, however, have a GPU with multiple cores, each of which can retrieve up to 1 Gigabyte of data per second.

The CPU cores may be shown to be quicker. However, if you needed to retrieve 100 Gigabytes of data, you might appreciate the GPU’s full capability.

Our dual-core CPU would require 50 seconds to complete the task if just one of its cores was active. However, since GPUs contain many cores, the GPU may still be able to retrieve the 100 Gigabytes of data in less than a second if 100 cores are being utilized for this reason alone.

Machines do not become sluggish as a result of having to execute too many operations. They slow down because they don’t have all of the data they need near enough to the processor to complete all of the operations.

So CPUs must fetch the data, which is where they slow down since fetching a large amount of data may take a long time. GPUs, on the other hand, can accomplish this better since they have more cores and greater memory bandwidth.

Important considerations when purchasing a GPU for deep learning

So, now that we’ve covered the fundamentals, let’s look at some of the key features to look for in a GPU.

Tensor Cores are a kind of tensor.

As we’ve seen, the more data we utilize for our deep learning model, the more matrix multiplications we’ll need, and Tensor Cores can assist. Tensor Cores utilize tensors (high-dimensional data containers) to speed up matrix calculations. 48 additions and 64 multiplications are required for a simple multiplication between two 44 matrices.

A Tensor Core lowers the number of cycles required for these computations, allowing you to train your models faster.

2. Bandwidth of Memory

Because of the performance boost provided by Tensor Cores, certain cores may be left inactive while data is retrieved. As a result, if the memory bandwidth isn’t very high, a GPU with Tensor Cores won’t necessarily perform to its maximum capacity. That is to say, the greater the memory bandwidth, the faster the Tensor Cores will be.

3. Cache Memory and Shared Memory

Memory hierarchies are a kind of computer architecture that was created for the purpose of storing data in a computer system. At the core of it are shared memories, caching memories, and registers. Because they are so near to the CPU, these memories are very quick, but they are also quite tiny and expensive. As a result, having a GPU with more of these memory would be the ideal option.

NVIDIA’s Ampere-based GPUs, for example, have the largest shared memory, with up to 164KB/SM. Purchasing an Ampere-based GPU, rather than a Turing or Volta GPU, makes far more sense.

Compatibility of Devices

Last but not least, before purchasing a GPU, make sure it is compatible with your existing configuration. Do you have enough room to accommodate the GPU? Are you able to provide the required power? Are you using the appropriate ports and connectors? Are you willing to spend more money on these extra tools if you don’t already have them?

Deep Learning GPU benchmarking results

Let’s look at some benchmark data from Tim Dettmers for various GPUs to help you decide. He is a PhD student at the University of Washington, where he is researching representation learning as well as neuro-inspired and hardware-optimized deep learning.

1628724029_910_7-of-the-Best-GPUs-for-Deep-LearningTimdettmers.com is the source of this image.

For two of the most popular deep learning models, CNN and Transformer, this performance test compares various NVIDIA GPUs.

You may wonder why everything is NVIDIA. NVIDIA GPUs, on the other hand, are ideal for machine learning libraries and frameworks like TensorFlow and PyTorch.

The A100 and the V100, as you can see, perform the best of the group.

These aren’t, however, consumer-grade GPUs. They’re GPUs for businesses.

That’s when you see the RTX 30 series GPUs’ outstanding performance. Yes, they are consumer-level Ampere-based GPUs, as you may have suspected, and as we previously said, Ampere-based GPUs often perform better. The next greatest performance levels are provided by Turing-based GPUs like as the Quadro RTX 6000 and Titan RTX, as well as the Volta-based Titan V.

The RTX 2080 Ti is another Turing GPU that delivers impressive results.

Now, let’s compare performance per dollar, since they are, after all, expensive goods.

1628724029_284_7-of-the-Best-GPUs-for-Deep-LearningTimdettmers.com is the source of this image.

Here we can see the full potential of the RTX 30 series GPUs, particularly the RTX 3080, which has the greatest performance to cost ratio. Then there are the RTX 20 SUPER series GPUs, which offer the greatest performance-to-cost ratio among the RTX 20 series GPUs, as well as the original RTX 20 series GPUs.

Let’s go straight into our recommendations for the best GPUs for deep learning now that we have a better understanding of these GPUs.

Best GPUs for Deep Learning

Best Overall: RTX 3080 (10 GB) / RTX 3080 Ti (12 GB)

The NVIDIA GeForce RTX 3080 is our overall favorite GPU for deep learning.

The RTX 3080 is based on the Ampere architecture and comes with 10 GB GDDR6X onboard memory, making it suitable for deep learning applications like as Kaggle competitions or research projects in Computer Vision, NLP, and other fields.

The GPU has a memory bandwidth of 760 GB/sec, as well as a large number of CUDA cores (8704), as well as 272 tensor cores.

Out of the group, the RTX 3080 is the most affordable GPU with excellent deep learning capabilities. However, if you deal with big datasets, the 10 GB RAM may not be sufficient. If you need additional RAM, the following one might be a better choice. Although you may use various memory-saving methods with your RTX 3080 to accommodate big models, you may have to put in a little more work when coding.

One of these choices is the RTX 3080 Ti, which has 12 GB of GDDR6X RAM but costs almost twice as much as the RTX 3080. Although it has excellent capabilities, with 320 tensor cores and a massive 912 GB/sec memory bandwidth, it will undoubtedly outperform the RTX 3080 in your model.

If you’re going to utilize transformers, your GPU should have at least 11 GB of memory, according to Tim Dettmers. In such scenario, the RTX 3080 Ti is the superior choice.

When purchasing any RTX 30 series GPU, the cooling system is an essential factor to consider. These GPUs are very powerful and generate a lot of heat. You may require some sort of liquid cooling solution for your RTX 30 series GPUs to achieve the optimum performance.

[ Amazon has the RTX 3080 (10GB) ] [ Amazon has the RTX 3080 Ti (12 GB) ]

2. RTX 3090 (24 GB) – Most powerful graphics card

The NVIDIA GeForce RTX 3090 should be your choice if you want the greatest overall performance for deep learning. The RTX 3090 features a massive 24 GB GDDR6X memory with a memory bandwidth of 936 GB/sec. It also boasts the maximum CUDA cores (10496) and tensor cores (328), both of which will greatly benefit your deep learning model.

All of these fantastic qualities, however, come at a hefty cost. The RTX 3090 may cost more than twice as much as the RTX 3080, and that’s before any additional costs you could incur based on your existing setup.

If you have the funds, the RTX 3090 would be an excellent option for dealing with large amounts of data. It should perform far better than the rest of our choices if you can get it to operate with a suitable cooling system and adequate power supply.

[ Amazon has the RTX 3090 (24 GB) ]

3. RTX 3070 graphics card (8 GB)

The NVIDIA GeForce RTX 3070 is another excellent choice for performing deep learning on a small scale. The Ampere architecture and 8 GB GDDR6 RAM should be fast enough for most neural networks. If you sometimes need to accommodate a bigger model, you can always utilize memory-saving techniques like Gradient Checkpointing.

The RTX 3070, as shown by Tim Dettmers’ benchmark results, outperforms the RTX 3080 and RTX 3090. However, because to a lack of memory, it performs badly on transformers, which, as we all know, need more memory. It also features a large number of CUDA cores (5888) and a good number of tensor cores (184) to help with deep learning.

[ Amazon has the RTX 3070 (8 GB) ]

4. [Used] RTX 2080 Ti (11 GB)

The NVIDIA GeForce RTX 2080 Ti is a fantastic GPU with 11 GB of GDDR6 RAM, which comes in handy for training bigger neural networks. It also boasts the most tensor cores (544) and the highest memory bandwidth (616 GB/sec). As a result, you can guess what sort of performance boost it would provide your deep learning models.

However, obtaining a reasonably priced RTX 2080 Ti is a major issue with this GPU. At the time of writing, a new RTX 2080 Ti GPU may be more expensive than an RTX 3090 GPU. However, you can still get a used one for a reasonable price based on its characteristics, and if you’re going to get this one, we suggest getting a used one.

[ Amazon has a Used RTX 2080 Ti (11 GB) ]

5. [Used] RTX 2070 (8 GB) / [Used] RTX 2070 SUPER (8 GB)

The NVIDIA GeForce RTX 2070 and NVIDIA GeForce RTX 2070 SUPER, both with 8 GB GDDR6 memory, are excellent choices if you’re on a budget and simply want to get your hands dirty with deep learning. Of course, only if you don’t mind purchasing a used one. It wouldn’t make much sense otherwise.

These GPUs feature a lot of tensor cores and a lot of memory bandwidth (448 GB/sec), which is more than adequate for most tasks. Both of these RTX models use NVIDIA’s Turing architecture, which means they use less power and produce less heat.

[ Amazon has a Used RTX 2070 (8GB) ] [ Amazon has a Used RTX 2070 SUPER (8GB) ]

Notes at the End

Remember to double-check the compatibility.

Always do your homework to ensure that the model you want to purchase is compatible with your computer setup. For example, if your existing setup lacks the required power supply or cooling support for the RTX 3090, purchasing one should not be your first option. Unless, of course, you have the financial and time resources to devote to this.

Upgrading isn’t always the best option.

If you currently own an RTX 2080 Ti, upgrading to the RTX 3080 isn’t a good idea. Yes, you will improve your performance. However, there are a few issues with the RTX 3080’s configuration, the most frequent of which being the cooling arrangement. If you require the additional RAM on the RTX 3090, though, you need upgrade from the RTX 2080 Ti.

With real-time analysis increasingly important in developing and testing algorithms, NVIDIA’s latest high performance GPUs provide the only choice for a deep learning machine.. Read more about amd gpu for deep learning and let us know what you think.

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Frequently Asked Questions

Which is the best GPU for deep learning?

The best GPU for deep learning is the NVIDIA GeForce GTX 1080 Ti.

Is RTX 3090 good for deep learning?

RTX 3090 is a great card for deep learning.

Is Ryzen 7 good for deep learning?

Ryzen 7 is a good CPU for deep learning.

Related Tags

This article broadly covered the following related topics:

  • amd gpu for deep learning
  • deep learning gpu benchmarks
  • best gpu for deep learning 2018
  • deep learning gpu
  • nvidia gpu deep learning

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