GPU virtual servers optimized
for high-performance parallel computing
GPU instances
Composed of multiple processing cores, GPU is a server optimized for high-performance environments. Eligible for processing parallel instructions, it enables high-performance workloads such as AI/Deep learning, transcoding, and graphics. One less reason to purchase an expensive GPU card.
Main Features
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AI/Deep Learning CUDA can realize GPGPU to accelerate hardware performance and computational speed. It also builds artificial neural networks through parallel processing and optimizes workflows with AI augmented applications. |
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Transcoding It can reduce the encoding time by directly converting video files of different encoding formats to digital-to-digital. It accelerates the creation and playback of various video streams and allows fast delivery of dynamic contents. |
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Ray tracing/simulation It can make accurate depictions of shadows, reflections, refractions, and global illumination, making it possible to render vivid images with Turning RT Core. Using advanced shading techniques such as curved surface and edge shading, you can improve the efficiency of graphic work such as CATIA and CAD. |
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Resource virtualization Through resource virtualization and sharing, you can minimize resource waste and share resources efficiently. Increase productivity for deep learning and machine learning workloads through the Resouce virtualization function. |
Fee Scheme
GPU instances cannot be used alone. They must be used in conjunction with CPU instances.
They can be used with up to 1 GPU per instance, and only offers a monthly fee scheme.
In the case of a monthly fee scheme, the corresponding monthly fee will be charged even if it is temporarily suspended.
| GPU model name | Product name | vCore | Memory | Disk | Fee |
|---|---|---|---|---|---|
| NVIDIA Quadro RTX 4000 | [GPU] 8cores 62GB |
8 | 62GB | 50GB | 750,000 |
| [GPU] 16cores 62GB |
16 | 62GB | 50GB | 920,000 | |
| [GPU] 24cores 62GB |
24 | 62GB | 50GB | 1,010,000 | |
| [GPU] 32cores 62GB |
32 | 62GB | 50GB | 1,110,000 |
GPU model performance
- It provides more vivid image rendering using real-time ray-tracing through 36 RT cores
- It accelerates neural network training/inference using 57 teraflops of deep learning performance, 288 Turing tensor cores
- It Improves VR application performance using Variable Rate Shading, MVR, and VRworks audio technology
- It creates videos and accelerates playback at resolutions up to 8K
| Items | Specifications |
|---|---|
| GPU Architecture | Turing |
| CUDA Core | 2,304 |
| Tensor Core | 288 |
| RT Core | 36 |
| RTX-OPS | 43T |
| Ray projection | 6 Giga Rays/Sec |
| FP16 performance | 14.2 |
| FP32 performance | 7.1 TFLOPS |
Recommend to
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AI/deep learning-based companies that need to learn a lot of data |
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Companies that need to create and provide realistic contents |
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Companies that need hardware performance enhancements for high-performance computing |
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Companies that need to send large files to multiple users |
