You cannot deny the importance of the GPU for Natural language processing and deep learning since deep learning projects require a lot of computing. The GPU is likely the most crucial component of your system.
Moreover, you can significantly speed up and enhance your deep learning performance with a good and potent GPU. The reason for GPU’s importance is its enormous parallelism, as it impacts the learning duration depending on the volume and complexity of your data.
Furthermore, you will find several GPUs for your computational and neural network systems. But which one you should choose depends on the training dataset’s size and your budget.
So whenever you buy the best deep learning GPU, consider features like GPU memory, CUDA and Tensor cores, memory interface, bandwidth, interoperability, and budget.
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Regarding that, we are here with the five best GPUs for deep learning 2022-2023. You can choose one that fits your needs:
- NVIDIA GeForce RTX 3090
- GIGABYTE GeForce RTX 3080
- NVIDIA GeForce RTX 3060 (12GB)
- NVIDIA Tesla v100 16GB
- ASUS GeForce GTX 1080 Turbo
Our Picks for Best GPU for 1440p 144hz
After reviewing several GPUs, we came up with the five best GPUs for 1Deep learning that can make your AI projects the most satisfactory!
1. NVIDIA GeForce RTX 3090
Reasons to Consider
The NVIDIA GeForce RTX 3090 GPU is one of the Nvidia GPUs for deep learning since it comes with amazing features like 328 tensor cores, 24 GM VRAM, and 936 GB/s bandwidth. Although this card costs you some good buck, it provides a good price-to-performance ratio. Moreover, this card is specifically designed with deep learning in mind.
- Ram: 24 GB GDDR6
- Video Output: 1x HDMI 2.1, 3x DisplayPort 1.4a
- CUDA: 10496 cores
- Tensor Cores: 328
- Boost Clock Speed: 1695 MHz
- Base Clock Speed: 1395 MHz
- Bandwidth: 936 GB/Sec
- Memory Interface: 384-bit
The NVIDIA GeForce RTX 3090 can go toe to toe with your deep learning endeavors since it comes jam-packed with amazing features. First, it has a whopping 24GB of VRAM fitted with a 384-bit interface, which is more than enough to train many deep-learning models.
Moreover, this GPU also comes with 328 tensor cores that can meet the most demanding parallel processing of deep learning. Besides, the card also contains 82 acceleration cores for raytracing.
The GeForce RTX 3090 comes with the same processor as the unlocked GeForce RTX 3090 Ti, with 10752 shaders activated, but the former has a catch. NVIDIA has disabled some shading units in RTX 3090 to meet the product’s target shader count.
Still, with 112 ROPs, 328 texture mapping units, and 10496 shading units, this GPU is potent enough to meet the most demanding processing. While this card can be good enough for most deep-learning projects, note that it is a triple-slot card.
So unless your system case has enough space, you can use this card. Moreover, this card also requires 250W of power supply unit, so check if your supply has that much power available.
2. GIGABYTE GeForce RTX 3080
Reasons to Consider
If you want a budget pick for GPU for deep learning and neural networks, go for this GIGABYTE GeForce RTX 3080. This GPU comes with enough features to meet AI processing needs while also remaining under budget. You can easily do the most demanding computational tasks with its third-generation 272 tensor and 8960 CUDA cores.
- Ram: 10GB GDDR6X
- Video Output: DisplayPort 1.4a and 2X HDMI 2.1
- CUDA: 8960 cores
- Tensor Cores: 272
- Boost Clock Speed: 1710 MHz
- Base Clock Speed: 1440 MHz
- Bandwidth: 760 GB/Sec
- Memory Interface: 320-bit
Our next pick is Gigabyte Geforce RTX 3080 graphic card. This GPU is a mid-range graphic card that can meet your deep learning and neural network needs. This is if you have a few bucks in your pocket but want a good price-to-performance ratio.
This GPU is based on Ampere architecture and boosts 272 tensor cores which are amazing for deep learning and neural networks. Note that its tensor cores are 3rd generation that sped up your parallel processing.
With a slower speed, while reading and writing from memory, its 10GB of GDDR6 memory will allow the training of huge systems in large batches. Even though memory interaction is a bit slower, the 8960 CUDA cores and 1710 MHz clock speed compensate for it. In addition, this GPU will remain quiet and cool despite working excessively than its 3D Active Fan. Moreover, the tri-fan tri fan GPU will require 350 Watts of power supply and enough space, so be mindful of this. Considering its price and features, it is the best GPU for machine learning.
3. NVIDIA GeForce RTX 3060 (12GB)
Reasons to Consider
If you are into deep learning and NLP and want a good GPU with a mid-range price, go for this RTX 3060 graphic card. While not costing much, it enables you to do most of your projects efficiently. Its 3584 CUDA cores and 112 Tensor cores of 3rd gen are enough for deep learning projects.
- Ram: 12 GB GDDR6
- Video Output: 3 x DisplayPort 1.4a, 1 x HDMI 2.1a
- CUDA: 3584 Cores
- Tensor Cores: 112
- Boost Clock Speed: 1807 MHz
- Base Clock Speed: 1320 MHz
- Bandwidth: 360 GB/Sec
- Memory Interface: 192-bit
The most cost-effective GPU for deep learning is the NVIDIA GeForce RTX 3060, so if you don’t have much to spend, go for this GPU. What makes this GPU a good choice for machine learning is its VRAM of 12GB and 360 GB/sec bandwidth, which it possesses.
The 12 GB VRAM of this GPU gives it a lot of versatility. Note that this card is slower than other cards in the Nvidia GeForce RTX 30 lineup, but since it comes with a whopping VRAM, it is still a good candidate.
Moreover, if you are new to deep learning, it is OKAY to have a slower GPU with sufficient VRAM to train most models instead of having a fast one with a low VRAM.
Moreover, this card is quite good than other RTX 30’s cards. For example, the Nvidia GeForce RTX 2060 features 240 tensor cores, while the RTX 3060 has 112 tensor cores, but the latter has a newer(3rd) generation tensor core, giving it an edge over the former.
It is one of the Best AI and machine learning GPUs since it does not cost much or compromise efficiency.
4. NVIDIA Tesla v100 16GB
most potent GPU
Reasons to Consider
The NVIDIA Tesla V100 is the current market-leading graphics processing unit for deep learning. This is due to its outstanding performance in deep learning and AI applications. Since it is designed specifically for deep learning, this GPU is for professional use.
- Ram: 16GB HBM2
- Video Output: HDMI
- CUDA: 5120 Cores
- Tensor Cores: 640
- Boost Clock Speed: 1380 MHz
- Base Clock Speed: 1245 MHz
- Bandwidth: 900 GB/Sec
- Memory Interface: 4096-bit
If you are looking for the most potent GPU for deep learning and AI applications, look no further than this NVIDIA Tesla V100. This GPU has many features that make it the best for the purpose. First, it comes fitted with Tensor Core support that goes toe to toe with your high-speed computing demands, deep learning, and machine learning (HPC) projects.
Secondly, NVIDIA’s iconic Volta technology drives this GPU. So if you want to accelerate your typical tensor operations in deep learning, this GPU can do it. In
Furthermore, each Tesla V100 has up to 32GB of memory, but this particular GPU here is 16 GB which is still good for deep learning. Moreover, it also boasts 149 teraflops of performance and a 4,096-bit memory bus to meet your computational demands.
And last but not least, this GPU comes in two different form factors for the V100: PCI Express and SXM2.
The most typical version and the one that works with most motherboards is the former, and the latter is best for mezzanine cards that are mostly utilized in servers.
The V100 is a fantastic option if your business needs a strong GPU without additional VRAM. The Tesla V100’s only drawback is its slightly inflated pricing, which prevents many people from purchasing it, but if you are an industrial user, this GPU will work wonders for you.
5. ASUS GeForce GTX 1080 Turbo
Reasons to Consider
If you are into gaming and deep learning, go for this GPU. Since this card come with 3584 CUDA cores and 32 Tensor cores, you can get more efficiency while spending fewer bucks. Moreover, it also seamlessly run different deep-learning application without any problem.
- Ram: 8 GB GDDR5X
- Video Output: 2x HDMI 2.0 and 2x DisplayPort 1.4
- CUDA: 3584 cores
- Tensor Cores: 32
- Boost Clock Speed: 1582 MHz
- Base Clock Speed: 1480 MHz
- Bandwidth: 320 GB/Sec
- Memory Interface: 352-bit
The ASUS GeForce GTX 1080 Turbo is another ideal GPU for deep learning GPU benchmarks. If you are up to running various iterations of the same model to learn the best result of your machine learning project, this GPU is the one to consider.
Although its 8 GB VRAM might seem insufficient, the ASUS GeForce GTX 1080 Turbo can be sufficient for large datasets. Moreover, this GPU goes hand in hand with your AI simulations and 3D visual rendering projects besides deep learning.
Moreover, this GPU also boasts 3584 CUDA cores. In addition, you can execute numerous tasks in parallel without experiencing any issues using NVIDIA’s classic CUDA technology.
Moreover, it has 32 tensor cores which may need more for deep learning since it is a gaming GPU. Still sufficient to run the majority of deep learning frameworks, such as TensorFlow, Caffe, Scikit-learn, and Microsoft Cognitive Toolkit.
Also, this GPU has an HDMI 2.0b port instead of an HDMI 1.4a port. Therefore you can get 4K video output at 60Hz refresh rates, ensuring consistently sharp visuals.
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How to Choose the Best GPU for Deep Learning?
1. Go for an NVIDIA GPU, Not AMD
NVIDIA GPUs are recommended if you wish to get a GPU for deep learning.
Since AMD GPUs lack Tensor Cores or comparable cores, which prevents them from performing neural networks and deep learning training, therefore are not as effective for deep learning as NVIDIA GPUs.
Moreover, the software environment for ROCm, an AMD GPU equivalent of “CUDA,” isn’t as strong as for NVIDIA s CUDA. This implies that your selection of machine learning algorithms, modules, and tools will be limited. Although the ROCm organization is expanding, it must still be stronger than the CUDA one.
2. Memory Bandwidth
You might be familiar that the memory bandwidth of a GPU tells you how much data it can handle in a specified amount of time. Since they must be capable of managing huge volumes of data quickly, GPUs generally have a substantial memory bandwidth. But in deep learning, GPU needs even more memory bandwidth so that the tensor core can handle more data in less time.
For instance, if the Tensor Cores must wait for data to come from memory while the process outputs have been computed, it will reduce the GPU’s total performance.
On the other side, if memory bandwidth is good, the GPU will be capable of reaching its maximum capacity, and the Tensor Cores will be able to continue functioning.
3. GPU Memory (VRAM)
VRAM is crucial for deep learning experts to consider when picking a GPU. Since they can seamlessly handle more datasets and develop artificial neural networks faster with greater GPU memory, go for more VRAM figures.
A GPU with 4GB of VRAM might be plenty for short datasets and basic neural networks but not recommended. A GPU with at least 8GB VRAM is a good choice for 4GB when working with massive datasets and more complicated machine learning.
In short, the dataset’s magnitude and the neural network’s intricacy determine how much GPU RAM is required.
In short, 12 to 16 GB of VRAM is a top pick for machine learning and AI but the more, the better.
4. Tensor Cores
Tensor Cores are the new specialized hardware components that accelerate deep learning activities. They’re accessible on several Nvidia GPU models> moreover, you can uplift your deep learning speed for machine learning applications with tensor cores.
You already know that Deep learning works well with GPUs since they can process more parallel computations. But with Tensor Cores, you can take this parallel functioning to a new level.
So, training deep neural networks on a GPU with Tensor Cores becomes significantly faster. It is best to choose more tensor cores of the GPU since it will make things faster. Tensor Cores can also help expedite inference, using a trained model to predict new data.
5. CUDA Cores
In a GPU, information is processed using CUDA cores, as in the CPU, the processing cores. CUDA cores can carry out some instructions a lot quicker than a CPU. This is because a CUDA core was created especially for parallel processing.
This is crucial for deep learning experts since it affects how quickly GPU can prepare a deep learning model.
In deep learning, tasks are broken down into smaller subtasks to process via the CUDA cores concurrently, which is why GPU is ideally suited for parallel computing. This is so that each CUDA core can carry out a subtask independently. So for deep learning, a greater number of CUDA cores is good.
As an industry professional, you must create a multi-GPU system for a sizable deep-learning project that can do numerous computations simultaneously. This is where you connect several GPUs so they can cooperate to accelerate computations. But not all GPUs are interoperable with one another, so picking the appropriate ones that will operate well is crucial.
You must look for three features while setting up a deep-learning workstation with several GPUs.
- The motherboard can be capable of housing several video cards.
- The power supply must be powerful enough to support numerous GPUs, or you must get a supplementary PSU for your system.
- Your data centers must support a large number of GPUs.
7. FLOPs (Floating Operations Per Second)
A GPU’s computing power is measured in FLOPs or floating operations per second.
Now if you ask what computational power is, it is one single operation for mathematical calculations. For instance, a multiplication operation is a FLOP. Simply put, if you multiply two numbers, this computation needs one FLOP.
The GPU’s power and FLOPs count are directly proportional, so you should opt for a GPU with a high FLOPs rating if you need one to tackle a deep learning application that is particularly demanding.
In addition, Teraflop indicates trillion floating point operations per second, so you should go for more TFLOPs.
Other GPU Considerations
In addition to the requirement for deep learning PCs, there are some general factors to consider when selecting a GPU for deep learning.
Thermal Design Power (TDP)
To choose a GPU, you must first ascertain your graphics card’s Wattage needs. You can predict how much juice your card will need by considering the TDP. The maximum power that the card may utilize is known as the TDP. You may ascertain the TDP of your GPU by looking at its specs.
Deep learning workstations need more advanced machines than standard computers as adaptive algorithms are complex.
As a result, ensure that your PSU has enough power that goes hand in hand with your deep-learning workstation.
While picking a GPU for deep learning, you must consider the cooling strategy. If the GPU you buy doesn’t have the adequate cooling capacity, it could overheat and possibly wear down.
To choose the cooling system that best meets your need researches the various available types.
The GPU’s form factor (card size and slot type) also has much to do with your selection. If you cannot fit a card into your PC in the first place, what is the point of buying it? So the card size and slot type must match your motherboard and case.
Compatibility with CPU
The CPU you choose must be able to communicate with the GPU you choose. You want to ensure the two are interoperable to get the best performance possible from your system while avoiding any unwanted bottlenecking.
You’ll need a decent GPU if you’re interested in machine learning and deep learning. However, it might be difficult to decide which type or model is best for you, given the variety of options available.
Your unique needs and financial situation determine which GPU is best for deep learning.
For the time, the RTX 3090 is still the undisputed top GPU performer for independent users.
The ideal option for data center applications is the Tesla A100. Although the offered list is a little extensive, it should serve as a decent place to begin your quest for the right GPU for machine learning. Ensure the selected model satisfies your affordability, GPU power, and functionality requirements.
Frequently Asked Questions
Does Deep Learning Work on AMD GPUs?
For deep learning, AMD GPUs are fine, but you’ll need the ROCm platform. Several AMD GPUs are compatible with the GPU computing platform known as ROCm (Radeon Open Compute Platform).
You could have a tougher difficulty locating software compatible with ROCm because it is less extensively supported than CUDA.
Why Utilize a GPU for Deep Learning Instead of a CPU?
For deep learning, GPUs are often much quicker than CPUs. This is because CPUs are for sequential computing, whereas GPUs are for parallel computation. Moreover, a CPU only has a few cores, whereas a GPU has thousands of cores that can simultaneously work on many aspects of computation. The CPU excels at many computations, but it needs help with parallel computations.
How Important is GPU for Deep Learning?
Since they enable you to train deep neural networks considerably more quickly than with a CPU, GPUs are crucial for deep learning. Deep learning works perfectly with GPUs because they are made for parallel computation.
What number of cores do I require for deep learning?
The number of cores will depend on the anticipated workload for non-GPU operations. Generally, using at least four cores for every GPU processor is advised. But 32 or 64 cores can be optimal if your job contains a sizable CPU computation component.
What is an entry-level GPU for deep learning?
We suggest RTX graphics cards for parallelization and deep learning, mostly 20 and 30 Series. Pick the GeForce 20 line if you’re looking for a more powerful graphics card that will enable you to execute more demanding deep learning projects besides learning parallelization and deep learning.