GPU Virtualization

GPU Virtualization​

Container-level Fractional GPU Scaling

Assigning slices of SMP / RAM to containers

Shared GPUs: inference & education workloads 

Multiple GPUs: model training workloads 

With a proprietary CUDA virtualization layer

* Registered patent in Korea, US and Japan​

Proprietary CUDA virtualization layer​

  • Supports all GPU models for CUDA 8 to 12(desktop / workstation / datacenter)
  • No code change required for user programs
  • No customization/rebuild required for DL frameworks
  • It is not limited to TensorFlow/PyTorch; any GPU-accelerated computing workload works!
  • Supports multi-GPU for single container using multiple fractions from different GPUs
  • Reproducible R&D environments for faster experiment cycles
  • On-demand resource provisioning on top of bare-metal, VMs, and containers
  • Optimized for clusters of high-end nodes with many CPUs and accelerators​

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