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.

CPU & GPU 처리 방식 비교

Main Features

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.

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.

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.

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 Specification Fee (KRW, VAT excluded)
VCore Memory Disk
NVIDIA Quadro RTX 4000 [GPU] 8 cores 62 GB 8 62GB 50GB 750,000
[GPU] 16 cores 62 GB 16 920,000
[GPU] 24 cores 62 GB 24 1,010,000
[GPU] 32 cores 62 GB 32 1,110,000

Requesting for sales/technical consultation

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
NVIDIA Quadro RTX 4000
Items Specifications
GPU Architecture Turing
CUDA Core 2,304
Tensor Core 288
RT Core 36
Items Specifications
RTX-OPS 43T
Ray projection 6 Giga Rays/Sec
FP16 performance 14.2
FP32 performance 7.1 TFLOPS

Recommend to

  • AI/deep learning-based companies that need to learn a lot of data

  • Companies that need to create and provide realistic contents

  • Companies that need hardware performance enhancements for high-performance computing

  • Companies that need to send large files to multiple users

For inquiries regarding the GPU instance