Popular GPUs for Different Industries and Tasks

Originally, vendors created graphics processing units (GPUs) for high-resolution graphics rendering and 3D animation. Later, due to their ability to perform fast parallel computations, GPUs started being used in medicine and the automotive industry.

In this material, we will consider specific models that are best suited for various tasks.

Graphics Rendering and the Gaming Industry

In the gaming industry, design, and media, GPUs are responsible for the growing demand for high-quality content and accelerate production processes: animation creation, visual effects, post-production, streaming video, and real-time ray tracing.

For 3D animation and visual effects rendering, GPU models such as the NVIDIA Quadro RTX 6000 or AMD Radeon Pro W6800 with a performance of 15–50 TFLOPS and a large amount of memory are suitable. For example, the NVIDIA RTX A6000 has 48 GB of GDDR6 memory, which is optimal for working with textures.

In game development for dynamic gameplay with detailed graphics and a frame rate of 60+ FPS, the NVIDIA RTX 3090 or AMD Radeon 6950 XT are used. For virtual reality, configurations with multiple GPUs are used, which provide a minimum of 25 TFLOPS for 360° video rendering.

Scientific Research and HPC

In scientific research, GPUs accelerate computations and data analysis. For example, in chemistry and biology, molecular dynamics modeling reveals micro-interactions and chemical processes.

In physics, GPUs process experimental data, such as from the Large Hadron Collider, providing up to 150 TFLOPS of performance. In astronomy, GPUs help in sky research, detecting rare phenomena using deep learning.

One bright example of the application of GPUs and AI in scientific research is the work of scientists. Using deep neural networks, they analyzed brain medical images and successfully detected early stages of neurodegenerative diseases years before the first symptoms appeared.

To summarize, for deep learning, AI work, big data processing, and complex system modeling, the NVIDIA A100 model with 6912 CUDA cores, 40 GB of HBM2 memory, and 312 tensor cores is suitable.

Industry and Manufacturing

Manufacturing actively uses GPUs at all stages: from concept development to process optimization. With parallel computing based on GPUs, engineers can quickly create digital twins, physical models, and control equipment operation.

The AMD Instinct MI200 and MI210 have a performance of 47 TFLOPS, making them ideal GPUs for mechanical and 3D modeling. Digital twins that recreate product characteristics work effectively on NVIDIA Quadro RTX or AMD Radeon Pro with a performance of 50–100 TFLOPS.

For quality control and anomaly monitoring, GPU-based devices that analyze equipment sensor data in real-time are usually used. GPUs, such as the NVIDIA Jetson, help quickly identify issues and increase uptime.

Finance and Cryptocurrencies

In the financial industry, graphics accelerators help model risks, detect fraudulent schemes, and analyze trades. Thanks to the fast data processing on GPUs, decision-making becomes more informed and faster.

For cryptocurrency mining, energy efficiency is crucial, and leaders here are the NVIDIA GeForce RTX 3060 Ti, AMD Radeon RX 5700 XT, and NVIDIA GeForce GTX 1660 Super. Monte Carlo risk modeling based on the NVIDIA A40 or AMD Instinct provides a performance of 50–250 TFLOPS, accelerating portfolio evaluation.

To detect fraud cases using deep learning, NVIDIA Jetson devices are used. With their help, anomalies in transactions can be quickly identified. High-frequency trading on the NVIDIA A100 or A800 allows for a rapid response to market changes.

Autonomous Driving Technology

With the development of technology, the automotive industry has started using GPUs, especially in the field of autonomous driving. Graphics accelerators can quickly process computer vision and artificial intelligence algorithms.

For full autonomous vehicle control, a performance of more than 250 TFLOPS is required. NVIDIA’s Drive AGX Pegasus system, created for this purpose, provides over 320 trillion operations per second.

For driver assistance systems, such as automatic emergency braking, GPUs capable of processing data in real-time are needed. For example, the NVIDIA Jetson Xavier with a performance of 20–30 TFLOPS.

Training deep learning models for autonomous driving requires processing large amounts of data. GPUs with hundreds of TFLOPS, such as the NVIDIA DGX A800, significantly accelerate this process.

Medicine and Biotechnology

GPUs are used in medicine to accelerate medical imaging, molecular modeling in drug development, and genomic analysis. For the latter, the NVIDIA A100 or A800 with a performance of 100–600 TFLOPS are usually used. This accelerates the sequencing process, which takes 30 hours on a CPU.

Medical image visualization becomes more efficient with the NVIDIA A40 with a performance of 50–100 TFLOPS. Molecular modeling in pharmaceuticals becomes 5–10 times faster with the NVIDIA A40 or AMD Instinct MI50.

Cloud Servers with GPUs as an Alternative to Expensive Equipment Acquisition

The choice of the right GPU depends on the specific industry and task, the definition of the main workload, and performance requirements. With the development of technology and the emergence of new ones in the future, vendors will develop and release new GPU models to the market.

However, due to the high cost of GPUs ($80,000 — $100,000), not all market participants will be able to afford the purchase and maintenance of such equipment. Budgets can be optimized using cloud servers with GPUs offered by enterprise cloud providers.