7 of the Best GPUs for Deep Learning – A Comprehensive Guide

Best GPU for Deep Learning
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Deep Learning is a hot trend right now in Machine Learning.

Machine Learning is a subfield of Artificial Intelligence that helps to solve AI problems using data, and within Machine Learning, a common and very useful technique available is Deep Learning.

Best GPUs for Deep Learning – A Quick Look at Our Picks

Product Name Details Check Price
RTX 3080

Memory: 10 GB GDDR6X
Clock (Ghz): 1.44~1.71
CUDA Cores: 8704
Tensor Cores: 272
Memory Bandwidth: 760 GB/s
Architecture: Ampere
TDP: 320 W
Required Power Supply: 750 W

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RTX 3080 Ti Memory: 12 GB GDDR6X
Clock (Ghz): 1.37~1.67
CUDA Cores: 10240
Tensor Cores: 320
Memory Bandwidth: 912 GB/s
Architecture: Ampere
TDP: 350 W
Required Power Supply: 750 W
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RTX 3090 Memory: 24 GB GDDR6X
Clock (Ghz): 1.40~1.70
CUDA Cores: 10496
Tensor Cores: 328
Memory Bandwidth: 936 GB/s
Architecture: Ampere
TDP: 350 W
Required Power Supply: 750 W
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RTX 3070 Memory: 8 GB GDDR6
Clock (Ghz): 1.50~1.73
CUDA Cores: 5888
Tensor Cores: 184
Memory Bandwidth: 448 GB/s
Architecture: Ampere
TDP: 220 W
Required Power Supply: 650 W
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RTX 2080 Ti (Used) Memory: 11 GB GDDR6
Clock (Ghz): 1.35~1.55
CUDA Cores: 4352
Tensor Cores: 544
Memory Bandwidth: 616 GB/s
Architecture: Turing
TDP: 250 W
Required Power Supply: 650 W
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RTX 2070 (Used) Memory: 8 GB GDDR6
Clock (Ghz): 1.41~1.62
CUDA Cores: 2304
Tensor Cores: 288
Memory Bandwidth: 448 GB/s
Architecture: Turing
TDP: 175 W
Required Power Supply: 550 W
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RTX 2070 SUPER (Used) Memory: 8 GB GDDR6
Clock (Ghz): 1.61~1.77
CUDA Cores: 2560
Tensor Cores: 320
Memory Bandwidth: 448 GB/s
Architecture: Turing
TDP: 215 W
Required Power Supply: 650 W
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Before we further discuss the best GPUs (Graphics Processing Units) for Deep Learning, let’s see why you might need one.

There are some other great GPUs, but they may not be available in the market right now. Even if some of them are available, they may be way too costly. For example, the Titan RTX is a great GPU, but you cannot find it at a reasonable price right now.

What is Deep Learning?

Deep learning is a powerful machine learning technique based on deep neural network that has multiple hidden layers of neurons. Neural Network or Artificial Neural Network is designed to mimic the learning mechanism of actual human brains. A typical neural network looks somewhat like this:

Image Credit: ibm.com

Internally, these are all matrix multiplications when it comes to processing output using a neural network.

And this is where GPU comes into play.

GPUs were originally designed for rendering 3D images, and images also have matrix representations. So, you see the similarity in matrix computations for deep learning using GPU.

Why you might need a GPU for Deep Learning

When training a neural network, you are essentially looking for the perfect set of values for the parameters involved that are learned by the net.

Now, suppose your neural network model has a few hundred or even a thousand parameters to learn.

In that case, your computer’s traditional CPU (Central Processing Unit) may be able to do the work. However, in a deep neural net, i.e., deep learning, there could be as many as millions or billions of parameters needed to be learned. Millions and billions, these are huge numbers we are talking about here.

The more data you have or use to train your deep learning model, the greater the number of operations that need to be performed.

As we said earlier, CPUs can perform these operations when the numbers are not as big as millions. Even then, it will take way too long to train your model.

A CPU only has a handful of cores that can perform unique tasks sequentially. On the other hand, a GPU can have hundreds to thousands of cores to perform tasks simultaneously.

Because of that, GPUs can deploy more cores working parallelly for a specific task, increasing efficiency. And this capability of simultaneous execution in GPU works perfectly with deep learning since the computations in neural networks can be performed in parallel execution.

Another great thing about GPU that contributes to deep learning is that GPUs have higher memory bandwidth than CPUs. A GPU can fetch large data really fast. But this isn’t to say that CPUs are the worst, not at all. CPUs can fetch data much faster than GPUs, but only when the data isn’t as large.

Let’s look at an example here. Say we have a CPU with two cores, and one of them is fetching some data for you with speed up to 2 GB/sec. On the other hand, we have a GPU with many cores; each can fetch up to 1 Gigabyte of data per second.

You can see that the CPU cores are faster. However, if you had to fetch 100 Gigabytes of data, you could see the true potential of using GPU.

Our dual-core CPU would need 50 seconds using only one of its cores as the other might be busy. But the GPU might still be fetching the 100 Gigabytes of data within a second if 100 cores are being used for this purpose only since GPUs have many cores.

Machines get slow not because they need to perform too many operations. They get slow because they don’t have all the necessary data available close to the processor to perform all the operations.

So, CPUs need to fetch the data, and that’s where they get slow because it can take greater time to fetch a lot of data. But GPUs can do that better since they have so many cores and higher memory bandwidth.

Important factors to consider when buying GPU for deep learning

So, now that we got the basics right let’s look at some of the important factors you need to look for in a GPU.

1. Tensor Cores

As we now know, the more data we use for our deep learning model, the more matrix multiplications we require, and Tensor Cores can help with that. Tensor Cores use tensors (containers for high dimensional data) that speed up the matrix computations. A simple multiplication between two 4×4 matrices requires 48 additions and 64 multiplications.

A Tensor Core reduces the cycles needed for these calculations, hence speeding up your model training.

2. Memory Bandwidth

Because of the speed gain we get from Tensor Cores, some cores may sit idle for data to be fetched. So, a GPU with Tensor Cores will not necessarily perform to its full potential if the memory bandwidth isn’t much high. That means the higher the memory bandwidth, the more speed gain you will get from the Tensor Cores.

3. Shared Memory and Cache Memory

Memory hierarchies are a common computer architecture that was designed for storing data into the computer system. Shared memories, cache memories, registers are at the heart of that. These memories are very fast since they are too close to the processor but very small in size and costly too. So, it would be the best choice to have a GPU that has more of these memories.

For example, NVIDIA’s Ampere-based GPUs have the most shared memory of up to 164KB/SM. So, buying an Ampere-based GPU would make much more sense than, say, buying a Turing or a Volta GPU.

4. Device Compatibility

Last but not least, you must check the compatibility of the GPU with your current setup before buying it. Do you have enough space to fit in the GPU? Do you have the necessary power supply? Do you have the right ports and connectors? If not, are you ready to spend extra money on these additional tools?

GPU benchmark performances in Deep Learning

To make your decision clear, let’s look at some benchmark data for different GPUs from Tim Dettmers. He is a PhD student at the University of Washington working on representation learning and neuro-inspired and hardware optimized deep learning.

Image Credit: timdettmers.com

This performance benchmark compares several NVIDIA GPUs for two of the most common deep learning models, CNN and Transformer.

Why all NVIDIA, you may ask? Well, NVIDIA GPUs are the best suitors for machine learning libraries and common frameworks such as TensorFlow and PyTorch.

As you can see, the A100 and the V100 perform the best out of the bunch.

However, these are not consumer-grade GPUs. They are enterprise-grade GPUs.

That’s when you notice the great performances of the RTX 30 series GPUs. As you might’ve guessed, yes, these are consumer-level products powered by Ampere, and just as we explained earlier, the Ampere-based GPUs usually perform better. Then we have the Turing-based GPUs like the Quadro RTX 6000 and the Titan RTX as well as the Volta-based Titan V having the next best performance levels.

Another Turing GPU that provides significantly good performance is the RTX 2080 Ti.

Now, let’s look at the performance comparison per Dollar because, of course, these are pricey products.

Image Credit: timdettmers.com

Here we can see the RTX 30 series GPUs’ true potential, especially the RTX 3080 with the best performance to cost ratio. Then we have RTX 20 SUPER series GPUs with the next best performance-cost ratio along with the original RTX 20 series GPUs.

Now that we have an overall idea of these GPUs, let’s jump right into our picks for the best GPUs for deep learning.

Best GPUs for Deep Learning

1. RTX 3080 (10 GB) / RTX 3080 Ti (12 GB) – Best Overall

Our overall best pick GPU for deep learning is the NVIDIA GeForce RTX 3080.

Based on the Ampere architecture, the RTX 3080 comes with 10 GB GDDR6X onboard memory, making it capable for your deep learning needs, whether it is Kaggle Competitions or research projects on Computer Vision, NLP, etc.

The GPU comes with a great memory bandwidth of 760 GB/sec and a huge number of CUDA cores (8704), and 272 tensor cores.

The RTX 3080 is the best-priced GPU with great deep learning performance out of the bunch. However, the 10 GB memory may not perform the best if you work with larger datasets. If you need more memory, then the next one would probably be better for you. Although you can apply different memory-saving techniques to fit large models using your RTX 3080, you might have to put in a little extra effort when coding in that case.

The RTX 3080 Ti is one of such options that comes with 12 GB GDDR6X memory but at almost twice the cost of the RTX 3080. Although it has great capabilities, with more tensor cores (320) and a whopping 912 GB/sec memory bandwidth, it will certainly have your model perform way better than the RTX 3080.

According to Tim Dettmers, if you are going to use transformers, your GPU should have at least 11 GB of memory. So, in that case, RTX 3080 Ti would be the better option.

One important thing to consider when buying any RTX 30 series GPU is to consider the cooling system. These are very powerful GPUs and dissipate a lot of heat. To get the best performance, you might need some kind of liquid cooling setup for your RTX 30 series GPUs.

[ Find RTX 3080 (10GB) on Amazon ] [ Find RTX 3080 Ti (12 GB) on Amazon ]

2. RTX 3090 (24 GB) – Best Performance

If you are looking for the all-around best performance for deep learning, then the NVIDIA GeForce RTX 3090 should be your call. The RTX 3090 has a huge 24 GB GDDR6X memory with 936 GB/sec of memory bandwidth. It also has the most CUDA cores (10496) and many tensor cores (328), which will help your deep learning model tremendously.

All these great features, however, come with a high price point. The RTX 3090 can cost you more than twice the amount of the RTX 3080, and that’s just leaving out any extra cost you may need depending on your current system.

The RTX 3090 would be a wise choice for working with huge data if you have the money to spend. If you can get it to work with a proper cooling system and enough power supplies, then it should perform on a very higher level than the rest of our picks.

[ Find RTX 3090 (24 GB) on Amazon ]

3. RTX 3070 (8 GB)

Another great option for doing deep learning on a minimal scale is the NVIDIA GeForce RTX 3070. Its 8 GB GDDR6 memory and the Ampere architecture should perform fast enough for most neural nets. If sometimes you have a larger model to fit, you can always use methods like Gradient Checkpointing to reduce the memory footprint.

As we’ve seen in the benchmark performances from Tim Dettmers, the RTX 3070 performs fairly well compared to the RTX 3080 and the RTX 3090. However, because of its memory shortage, it performs poorly on transformers as we know transformers usually require more memory. It also has a lot of CUDA cores (5888) and a decent number of tensor cores (184) to speed up the deep learning process.

[ Find RTX 3070 (8 GB) on Amazon ]

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

The NVIDIA GeForce RTX 2080 Ti is a great GPU with 11 GB GDDR6 memory which is a plus if you have larger neural nets to train. It also has a great memory bandwidth of 616 GB/sec and the most tensor cores (544) among the bunch. So, you can assume the kind of performance gain it would provide for your deep learning models.

However, finding a RTX 2080 Ti with a reasonable price is a great concern over this GPU. At the time of writing, buying a new RTX 2080 Ti can cost you more than buying a RTX 3090 GPU. Nevertheless, you can still find a used one that would cost you fairly given its features, and if you are going to go for this one, we recommend buying a used one.

[ Find Used RTX 2080 Ti (11 GB) on Amazon ]

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

If you are on a budget and you just want to get your hands dirty with deep learning, then the NVIDIA GeForce RTX 2070 and the NVIDIA GeForce RTX 2070 SUPER are great options with 8 GB GDDR6 memories. Of course, only if you are okay with buying a used one. Otherwise, it wouldn’t make much sense.

These GPUs have a good amount of tensor cores and great memory bandwidth (448 GB/sec) that will be good enough most of the time. Both of these RTX models are using NVIDIA’s Turing architecture and require less power supply, also dissipate less heat.

[ Find Used RTX 2070 (8GB) on Amazon ] [ Find Used RTX 2070 SUPER (8GB) on Amazon ]

Final Notes

Don’t forget to check the compatibility

Always do good research on the model you are looking to buy if it’s compatible with your computer setup. For example, if you don’t have the necessary power supply or the cooling support for the RTX 3090 in your current setup, then buying a RTX 3090 shouldn’t be the choice you make. Unless, of course, you can spend extra money and time on this stuff.

Upgrading isn’t always the answer

If you already have a RTX 2080 Ti, then upgrading to a RTX 3080 doesn’t make much sense. Yes, you will have some performance gain. But the whole setup process with the RTX 3080 comes with a few headaches, and the most common one is the cooling setup. However, if you need the extra memory on the RTX 3090, then switching from the RTX 2080 Ti makes more sense.

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