Initially, vendors created graphics processing units (GPU models) for rendering graphics and 3D animations in high resolution. Later, thanks to their fast parallel computing capabilities, companies in medicine and the automotive industry also started using GPUs.
This article explores specific best GPU models for industry and evaluates their suitability for different tasks across various industries.
GPU models for graphics rendering and gaming industry
In the gaming industry, design, and media, GPUs meet the growing demand for high-quality content and accelerate production processes. These include animation, visual effects, post-production, streaming video, and real-time ray tracing.
For instance, GPU models such as the NVIDIA Quadro RTX 6000 or AMD Radeon Pro W6800—with a performance of 15-50 TFLOPS and large memory capacity—effectively handle rendering of 3D animations and visual effects. Specifically, the Nvidia RTX A6000 offers 48 GB of GDDR6 memory, which is optimal for working with textures.
Developers rely on the NVIDIA RTX 3090 or AMD Radeon 6950 XT in game development to enable dynamic gameplay with detailed graphics and a frame rate of over 60 frames per second. Moreover, virtual reality setups use multiple GPUs to achieve at least 25 TFLOPS for 360° video rendering.
GPU models for scientific research and HCI
In scientific research, GPUs dramatically accelerate computing and data analysis. For example, in chemistry and biology, molecular dynamics modeling reveals microinteractions and chemical processes.
In physics, scientists use GPUs to process data from experiments such as the Large Hadron Collider, achieving performance up to 150 TFLOPS. In astronomy, researchers rely on GPUs to study the sky and identify rare phenomena.
One of the most striking examples of GPU and AI usage in science comes from neuroscience. By leveraging deep neural networks, scientists analyzed brain scans and successfully identified early stages of degenerative diseases several years before symptoms appeared.
To summarize, the NVIDIA A100 model—with 6912 CUDA cores, 40 GB of HBM2 memory, and 312 tensor cores—proves suitable for deep learning, artificial intelligence, big data processing, and complex systems modeling.
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GPU models for industry and production
Industries now actively deploy GPUs at all stages, from concept development to process optimization. By leveraging GPU-based parallel computing, engineers rapidly build digital counterparts, physical models, and monitor hardware operations.
The AMD Instinct MI200 and MI210 offer 47 TFLOPS of performance, making them ideal for mechanical and 3D modeling. Digital twins, which replicate product characteristics, operate efficiently on NVIDIA Quadro RTX or AMD Radeon Pro GPUs with performance of 50-100 TFLOPS.
Manufacturers frequently use GPU devices for real-time quality control and anomaly monitoring. For example, NVIDIA Jetson quickly detects issues and improves uptime by analyzing sensor data in real time.
GPU models for finance and cryptocurrency
In the financial industry, companies use graphics accelerators to model risks, detect fraud, and analyze trading data. As a result, GPUs enable faster and better-informed decisions through accelerated data processing.
Energy efficiency plays a key role in cryptocurrency mining. Notably, the NVIDIA GeForce RTX 3060 Ti, AMD Radeon RX 5700 XT, and NVIDIA GeForce GTX 1660 Super lead in this area. Monte Carlo risk modeling on NVIDIA A40 or AMD Instinct delivers performance of 50-250 TFLOPS, expediting portfolio assessments.
Financial institutions detect fraud via deep learning with NVIDIA Jetson. These systems efficiently identify anomalies in financial transactions. Meanwhile, high-frequency trading platforms use NVIDIA A100 or A800 to respond swiftly to market changes.
Autonomous driving technology
The automotive industry increasingly incorporates GPUs, especially for autonomous driving applications. Graphics accelerators quickly execute computer vision and AI algorithms.
To fully control an autonomous vehicle, systems need over 250 TFLOPS of performance. NVIDIA’s Drive AGX Pegasus platform, developed specifically for this, delivers more than 320 trillion operations per second.
Driver assistance features—such as automatic emergency braking—require GPUs that handle real-time data processing. For example, the NVIDIA Jetson Xavier achieves 20-30 TFLOPS to support these functions.
Developers train deep learning models for autonomous driving on large datasets. GPUs such as the NVIDIA DGX A800, with performance in the hundreds of TFLOPS, significantly speed up this process.
Healthcare and biotech also benefit from GPU acceleration in tasks like medical imaging, molecular modeling for drug development, and genomic analysis. For genome sequencing, the NVIDIA A100 or A800—delivering 100-600 TFLOPS—cut processing times dramatically compared to CPU-only methods.
Medical image visualization becomes more efficient with NVIDIA A40, which delivers 50-100 TFLOPS. Likewise, molecular modeling in pharmaceuticals runs 5-10 times faster on the NVIDIA A40 or AMD Instinct MI50.
GPU-powered cloud servers as an alternative to purchasing expensive hardware
Selecting the right GPU depends on the specific industry and task, as this determines workload and performance requirements. As technologies evolve and new solutions emerge, vendors continue developing GPU models tailored for specific use cases.
However, because GPUs can cost between 800,000 and 1 million rubles, many businesses may find ownership and maintenance unaffordable. In such cases, companies reduce expenses by renting GPU-powered cloud servers.
For example, ITGLOBAL.COM offers an AI Cloud service that enables businesses to rent cloud servers equipped with NVIDIA A800 GPUs. These servers are based on dual-socket vStack-R architecture and rely on NetApp Hi-End storage subsystems.
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