Experience is vital to developing the skills necessary to apply deep learning to new issues. A fast GPU means a rapid gain in practical experience through immediate feedback. GPUs contain multiple cores to deal with parallel computations. They also incorporate extensive memory bandwidth to manage this information with ease.
With this in mind, we seek to answer the question, “What is the best graphics card for AI, machine learning and deep learning?” by reviewing several graphics cards currently available in 2021. Cards Reviewed:
Below are the results:
- Release Date: August 14, 2017
- Vega Architecture
- PCI Express Interface
- Clock Speed: 1247 MHz
- Stream Processors: 4096
- VRAM: 8 GB
- Memory Bandwidth: 484 GB/s
If you do not like the NVIDIA GPUs, or your budget doesn’t allow you to spend upwards of $500 on a graphics card, then AMD has a smart alternative. Housing a decent amount of RAM, a fast memory bandwidth, and more than enough stream processors, AMD’s RS Vega 64 is very hard to ignore.
The Vega architecture is an upgrade from the previous RX cards. In terms of performance, this model is is close to the GeForce RTX 1080 Ti, as both of these models have a similar VRAM. Moreover, Vega supports native half-precision (FP16). The ROCm and TensorFlow work, but the software is not as mature as in NVIDIA graphics cards.
All in all, the Vega 64 is a decent GPU for deep learning and AI. This model costs well below $500 USD and gets the job done for beginners. However, for professional applications, we recommend opting for an NVIDIA card.
AMD RX Vega 64 Details: Amazon
- Release Date: December 7, 2017
- NVIDIA Volta architecture
- PCI-E Interface
- 112 TFLOPS Tensor Performance
- 640 Tensor Cores
- 5120 NVIDIA CUDA® Cores
- VRAM: 16 GB
- Memory Bandwidth: 900 GB/s
- Compute APIs: CUDA, DirectCompute, OpenCL™, OpenACC®
The NVIDIA Tesla V100 is a behemoth and one of the best graphics cards for AI, machine learning, and deep learning. This card is fully optimized and comes packed with all the goodies one may need for this purpose.
The Tesla V100 comes in 16 GB and 32 GB memory configurations. With plenty of VRAM, AI acceleration, high memory bandwidth, and specialized tensor cores for deep learning, you can rest assured that your every training model will run smoothly – and in less time. Specifically, the Tesla V100 can deliver 125TFLOPS of deep learning performance for both training and inference , made possible by NVIDIA’s Volta architecture.
- Release Date: August 2018
- Turing Architecture
- 576 Tensor Cores
- CUDA Cores: 4,608
- VRAM: 48 GB
- Memory Bandwidth: 672 GB/s
- 16.3 TFLOPS
- System interface: PCI-Express
Specifically built for deep learning matrix arithmetic and computations, the Quadro RTX 8000 is a top-of-the-line graphics card. Since this card comes with large VRAM capacity (48 GB), this model is recommended for researching extra-large computational models. When used in pair with NVLink, the capacity can be increased to up to 96 GB of VRAM. Which is a lot!
A combination of 72 RT and 576 Tensor cores for enhanced workflows results in over 130 TFLOPS of performance. Compared to the most expensive graphics card on our list – the Tesla V100 – this model potentially offers 50 percent more memory and still manages to cost less. Even on installed memory, this model has exceptional performance while working with larger batch sizes on a single GPU.
Again, like Tesla V100, this model is limited only by your price roof. That said, if you want to invest in the future and in high-quality computing, get an RTX 8000. Who knows, you may lead the research on AI. Tesla V100 is based on Turing architecture where the V100 is based on Volta architecture, so Nvidia Quadro RTX 8000 can be considered slightly more modern and slightly more powerful than the V100.
Nvidia Quadro RTX 8000 Details: Amazon
- Release Date: September 20, 2018
- Turing GPU architecture and the RTX platform
- Clock Speed: 1350 MHz
- CUDA Cores: 4352
- 11 GB of next-gen, ultra-fast GDDR6 memory
- Memory Bandwidth: 616 GB/s
- Power: 260W
The GeForce RTX 2080 Ti is a budget option ideal for small-scale modeling workloads, rather than large-scale training developments. This is because it has a smaller GPU memory per card (only 11 GB). This model’s limitations become more obvious when training some modern NLP models. However, that does not mean that this card cannot compete. The blower design on the RTX 2080 allows for far denser system configurations – up to four GPUs within a single workstation. Plus, this model trains neural networks at 80 percent the speeds of the Tesla V100. According to LambdaLabs’ deep learning performance benchmarks, when compared with Tesla V100, the RTX 2080 is 73% the speed of FP2 and 55% the speed of FP16.
Meanwhile, this model costs nearly 7 times less than a Tesla V100. From both a price and performance standpoint, the GeForce RTX 2080 Ti is a great GPU for deep learning and AI development.
GeForce RTX 2080 Ti Details: Amazon
- Release Date: December 18, 2018
- Powered by NVIDIA Turing™ architecture designed for AI
- 576 Tensor Cores for AI acceleration
- 130 teraFLOPS (TFLOPS) for deep learning training
- CUDA Cores: 4608
- VRAM: 24 GB
- Memory Bandwidth: 672 GB/s
- Recommended power supply 650 watts
The NVIDIA Titan RTX is another mid-range GPU used for complex deep learning operations. This model’s 24 GB of VRAM is enough to work with most batch sizes. If you wish to train larger models, however, pair this card with the NVLink bridge to effectively have 48 GB of VRAM. This amount would be enough even for large transformer NLP models. Moreover, Titan RTX allows for full rate mixed-precision training for models (i.e., FP 16 along with FP32 accumulation). As a result, this model performs approximately 15 to 20 percent faster in operations where Tensor Cores are utilized.
One limitation of the NVIDIA Titan RTX is the twin fan design. This hampers more complex system configurations because it cannot be packed into a workstation without substantial modifications to the cooling mechanism, which is not recommended.
Overall, Titan is an excellent, all-purpose GPU for just about any deep learning task. Compared to other general purpose graphics cards, it is certainly expensive. That is why this model is not recommended for gamers. Nevertheless, extra VRAM and performance boost would likely be appreciated by researchers utilizing complex deep learning models. The price of the Titan RTX is meaningfully less than the V100 showcased above and would be a good choice if your budget does not allow for V100 pricing to do deep learning or your workload does not need more than the Titan RTX (see interesting benchmarks)
NVIDIA Titan RTX Details: Amazon
Choosing the best graphics card for AI, machine learning, and deep learning
AI, machine learning, and deep learning tasks process heaps of data. These tasks can be very demanding on your hardware. Below are the features to keep in mind before purchasing a GPU.
As a simple rule of thumb, the greater the number of cores, the higher will be the performance of your system. The number of cores should also be taken into consideration, particularly if you are dealing with a large amount of data. NVIDIA has named its cores CUDA, while AMD calls their cores stream processors. Go for the highest number of processing cores your budget will allow.
The processing power of a GPU depends on the number of cores inside the system multiplied by the clock speeds at which you are running the cores. The higher the speed and the higher the number of cores, the higher will be the processing power at which your GPU can compute data. This also determines how fast your system will perform a task.
Video RAM, or VRAM, is a measurement of the amount of data your system can handle at once. Higher VRAM is vital if you are working with various Computer Vision models or performing any CV Kaggle competitions. VRAM is not as important for NLP, or for working with other categorical data.
The Memory Bandwidth is the rate at which data is read or stored into the memory. In simple terms, it is the speed of the VRAM. Measured in GB/s, more Memory Bandwidth means that the card can draw more data in less time, which translates into faster operation.
GPU temperature can be a significant bottleneck when it comes to performance. Modern GPUs increase their speed to a maximum while running an algorithm. But as soon as a certain temperature threshold is reached, the GPU decreases processing speed to protect against overheating.
The blower fan design for air coolers pushes air outside of the system while the non-blower fans suck air in. In architecture where multiple GPUs are placed next to each other, non-blower fans will heat up more. If you are using air cooling in a setup with 3 to 4 GPUs, avoid non-blower fans.
Water cooling is another option. Though expensive, this method is much more silent and ensures that even the beefiest GPU setups remain cool throughout operation.
For most users foraying into deep learning, the RTX 2080 Ti or the Titan RTX will provide the greatest bang for your buck. The only drawback of the RTX 2080 Ti is a limited 11 GB VRAM size. Training with larger batch sizes allows models to train faster and much more accurately, saving a lot of the user’s time. This is only possible when you have Quadro GPUs or a TITAN RTX. Using half-precision (FP16) allows models to fit in the GPUs with insufficient VRAM size . For more advanced users, however, Tesla V100 is where you should invest. That is our top pick for the best graphics card for AI, machine learning and deep learning. That is all for this article. We hope you liked it. Until next time!