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Looking for the best GPU for AI and machine learning? You’ve landed at the right place.
In the process of deep learning, the training phase is usually considered to be the most extensive phase, and not only does it stand to be time-consuming, but it also results in being costly.
The learning process involves human intervention, which is about the data scientists who spend hours of their productivity in training, which hampers their overall performance and delays the time of introducing new models in the market.
This is where the deep learning GPUs or servers with NVIDIA GPUs come into play. These processors are best to decrease the training time as it helps you in performing the AI computational operations parallelly.
While choosing the best GPU for use, you should consider its ability to interconnect different GPUs, data parallelism, performance, licensing, memory use, and supporting software.
5 Best GPU for AI Training And Machine Learning
NVIDIA GeForce RTX 3090 Founders Edition
Though the NVIDIA GeForce RTX 3090 Founders Edition was initially dedicated to gaming, its robust graphics processing unit has proven beneficial for running deep learning applications more efficiently than most of the GPUs in the market.
The foremost thing to learn about the NVIDIA GeForce RTX 3090 Founders Edition is that it NVLink accelerated, which means that only it is capable of interconnecting multiple GPUs but also allows you to connect your CPU directly with the GPU.
The ray tracing engine lets the smooth rendering of images as it is based on light rays and not polygons. Hence, the graphics look more realistic.
Along with this, the cloud GPU also has an in-built Tensor Core with 24 GB of GDDR6X memory, which means that almost every complex model can be handled without hassle.
NVIDIA Titan RTX Graphics Card
The NVIDIA Titan RTX Graphics Card is another ideal option for running deep learning applications as it is filled with a bunch of features that support the most complex of deep learning operations. It utilizes GPU libraries for smoothly performing complex calculations.
This new variant of Titan RTX comes with 24 GB of memory distributed in four stacks, providing 2x bandwidth of what the previous generations of this GPU delivered.
Thereby leading to better performance and increased speed. The NVIDIA Titan RTX Graphics Card also performs FP16 operations, which are almost double in speed compared to the FP32 operations but half as accurate.
For machine learning applications, it is essential to process the information faster and as it analyzes the data. The feature mentioned above of this GPU allows the same.
NVIDIA GeForce RTX 2080 Ti
NVIDIA GeForce RTX 2080 Ti has multiple features that make it a good fit for deep learning applications. In addition to the features, the NVIDIA GeForce RTX 2080 Ti has 13.45 teraflops of computational power and 1.6 million transistors. Thus, complex tasks are carried out smoothly and efficiently.
2944 is the number of CUDA cores in this GPU which implies that it can be used for many AI applications like the training of recurrent neural networks and convolutional neural network.
Using NVIDIA GeForce RTX 2080 Ti for deep learning operations allows you access to NVIDIA Deep Learning SDK, which has all the powerful tools for AI applications.
NVIDIA Tesla v100 16GB
This NVIDIA Tesla v100 16GB has 640 Tensor Cores which makes it a great choice for deep learning as it allows the acceleration of high-performing workloads. With this GPU, it is possible to connect various V100 GPUs at 300 GB/second while offering access to NVIDIA NVLink for more than one GPU system.
NVIDIA Tesla v100 16GB comes with 16 GB of space which is apt for high-resolution images and huge datasets.
In addition to that, because the NVIDIA Tesla v100 16GB was explicitly designed for deep learning, it utilizes less power than most of the GPUs on the market. Thus, this GPU runs for more time before overheating.
NVIDIA Quadro RTX 4000
In the NVIDIA Quadro line, the NVIDIA Quadro RTX 4000 is the newest addition, and it comes with a robust 8 GB of GDDR6 and 416 GB per second bandwidth speed. This makes it better and almost four times faster than the previous variant with GDDRX4 memory.
This GPU also comes with ray tracing, which is real-time focused, which implies that one does not need to wait till the end of the application running to see the computations ‘results.
Additionally, the NVIDIA Quadro RTX 4000 comes with three DisplayPort connectors allowing you to connect up to four monitors simultaneously while making the creation of virtual reality applications and multi-monitor displays facile.
The selection of the right GPU is all about your specific needs, and this list includes some of the best GPUs to get started. Do not forget to consider the training requirements and model specifications before buying one.
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