Overview
The GPU (Graphics Processing Unit) is a critical component of hardware when it comes to processing computer graphics. In essence, it is an electronic circuit capable of carrying out several parallel computations. Your computer also uses it to improve the quality of every image you view on your screen.
Deep learning computer operations can be significantly improved without sacrificing efficiency or power thanks to GPU, which assembles a massive number of cores that use fewer resources.
Our experts were highlighting the 16 Best Graphics Cards for Deep Learning.
Top Picks
Check out our list of Best Graphics Cards for Deep Learning suggested by Experts.
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Best overall: Nvidia Tesla v100 16GB
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Best premium choice: NVIDIA RTX A5000
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Best purchased product: NVIDIA Quadro RTX 4000
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Best value product: NVIDIA GeForce RTX 3090 Ti
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Best affordable product: EVGA GeForce GTX 1080
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Best ultra cheap product: GTX 1660 Super
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Best performance choice: NVIDIA Titan RTX
Reviews
1. Nvidia Tesla v100 16GB
The AI GPU Nvidia Tesla V100 combines hardware and software to streamline graphics processing.
Additionally, the GPU uses less energy than a lot more conventional silicon devices, which is crucial for those setting up long-term data center operations.
The most recent GPU to reach the market, the Nvidia Tesla v100, provides data scientists with the necessary computational capacity.
A trillion matrix multiplications and thousands of deep neural networks may be processed quickly using NVIDIA Volta's cutting-edge V100 Tensor Core GPU.
Nvidia's new Tesla V100 will carry you to new heights with its 640 Tensor Cores. It is ideal for massive projects, data centers, cutting-edge scientific computing, and just about anything else you can imagine.
Because of this advancement in processing speed, artificial intelligence may now resolve problems previously seen as impossible.
Pros
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Incredible audio quality, video, and graphics.
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Volta architecture.
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Integrated security.
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16GB of HBM2 memory.
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Ease-of-use.
Cons
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Very expensive.
2. NVIDIA GeForce RTX 2080
The device has a nice appearance and uses the quickest memory available—GDDR6.
The GPU also supports SLI setups with multiple GPUs.The device is perfect for deep learning because it has a memory clock speed of 15.5 GHz and a core clock speed of 1650 MHz.
Additionally, this GPU includes 8GB of faster 15.5 Gbps GDDR6 memory.
The card has a 250-watt TDP, which is adequate.
You'll need a strong enough power supply and an adequately ventilated enclosure to support the card.
Pros
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One of the best performance cards available.
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It has the current fastest RAM (GDDR6) available.
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An elegant design.
Cons
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Expensive.
3. NVIDIA GeForce RTX 2070
The RTX 2070 Super employs the same Turing architecture as earlier RTX cards; therefore, its architecture is unchanged. However, the GPU is faster thanks to the more CUDA cores and higher clock speed.
The GTX 2070 super's 40 streaming processors each include 64 CUDA cores, 1 RT core, 4 texture units, and 8 tensor cores.
It contains 8 GB of VRAM and a 448 GB per second transfer rate.
In other words, the RTX 2080 has replaced the GTX 2070 Super.
Even though the GPU does not support SLI, no one anticipates these configurations at these prices.
Pros
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Impressive synthetic performance.
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Supports DLSS and ray tracing.
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Extremely lower energy consumption.
Cons
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No SLI option.
4. NVIDIA Titan RTX
The fourth-placed graphics card is the NVIDIA Titan RTX. They are great and give users fantastic results.
Additionally, the architecture of the item is long-lasting and marvelous.
The device's fan stands out as a distinctive feature. It has two 13-blade fans that produce 3X greater airflow while being incredibly silent.
The highest capacity RAM on the market at 24GB offers exceptional performance.
To work on the device, you can use either 64-bit Linux or Windows 10.
Most of those who bought it appreciated its beautiful appearance and robust design, and they suggested others buy it only for the claimed purposes of deep learning, CAD, and video editing.
Pros
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It has an effective power management system built right in.
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For heavy professional and deep learning workloads, 24GB of memory is sufficient.
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Improved Tensor cores specifically enhance inferencing performance.
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Attractive design.
Cons
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The design of axial fans exhausts (a lot of) heat into your case.
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Very expensive.
5. EVGA GeForce RTX 3080
EVGA GeForce RTX 3080 Ti graphics cards are perfect for deep learning projects and gaming.
This GPU includes 12GB of GDDR6X VRAM memory with a Real Boost Clock speed of 1800 MHz.
This card is inexpensive and has all the features a gamer could expect in a high-end gaming product.
EVGA was the best company when it came to including deep learning and AI technology in their graphics cards with RTX.
The exclusive RTX, TensorRT-accelerated Deep Learning Super Sampling, which is still at the forefront, harvests features from images that no other GPU can for immaculate textures.
Pros
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Innovative cooler design.
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More affordable and huge performance increase over than RTX 2080 Ti.
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2nd generation hardware-accelerated ray tracing.
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Idle fan stop.
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Support for HDMI 2.1, AV1 decode.
Cons
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Very power-hungry.
FAQs
Q: Deep Learning: What is it?
Modern GPU servers are made to be able to complete the deep learning process quickly. Deep learning is a technique in the artificial intelligence field of machine learning, which also includes massive data, neural networks, parallel processing, and computing a lot of matrices. All these methods use algorithms that process a lot of data and turn it into usable software. High-performance computing is automated by deep learning, which also addresses AI issues.
Q: Why Do GPUs Matter for Deep Learning?
Because a GPU has the most computing units available for deep learning activities, it is crucial for deep learning jobs. The GPU is the computational device with the most cores in a deep learning server. When comparing the CPU and GPU in a deep learning server—the only components capable of doing calculations—the GPU comes out on top by a wide margin for deep computing workloads. The GPU is the only processing component in a deep learning server that can handle the complexity of the neural network training process. Such tasks are not appropriate for the CPU.
Q: How to Choose The Best GPU for Deep Learning?
The following elements should be considered when selecting the optimal GPU for deep learning, starting with the GPU's power, measured by the number of cores and the speed at which large amounts of data are transmitted between those cores. Additionally, it would help if you considered the potential of connecting GPUs that are identical to one another. Deep learning can be accelerated by many GPUs operating in cooperative mode. The manufacturer's use of linked software and licensing concerns should get special consideration.
Final Thought
The top 16 Best Graphics Cards For Deep Learning have just been introduced to you that are loved and trusted by many people. These are recommendations taken from the expertise of our professionals. Numerous factors, including budgets, wants, and other considerations, influence which product is best for you. Therefore, think carefully before making a purchase and seek guidance from your loved ones if you are unsure.