GPUs on Hikube

Hikube provides access to NVIDIA accelerators via GPU Passthrough, enabling the execution of workloads requiring hardware acceleration. GPUs are available for two types of workloads: virtual machines and Kubernetes pods.

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🎯 Usage Types

GPU with Virtual Machines

GPUs can be directly attached to virtual machines via VFIO-PCI GPU passthrough, providing complete and exclusive access to the accelerator.

Use cases:

• Applications requiring complete GPU control
• Legacy or specialized workloads
• Isolated development environments
• Graphics applications (rendering, CAD)

GPU with Kubernetes

GPUs can be allocated to Kubernetes workers and then assigned to pods via resource requests/limits.

Use cases:

• Containerized AI/ML workloads
• Automatic scaling of GPU applications
• GPU resource sharing between applications
• Complex orchestration of parallel jobs

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🖥️ Available Hardware

Hikube offers three types of NVIDIA GPUs:

NVIDIA L40S

• Architecture : Ada Lovelace
• Memory : 48 GB GDDR6 with ECC
• Performance : 362 TOPS (INT8), 91.6 TFLOPs (FP32)
• Typical usage : Generative AI, inference, real-time rendering

NVIDIA A100

• Architecture : Ampere
• Memory : 80 GB HBM2e with ECC
• Performance : 312 TOPS (INT8), 624 TFLOPs (Tensor)
• Typical usage : ML training, high-performance computing

NVIDIA H100

• Architecture : Hopper
• Memory : 80 GB HBM3 with ECC
• Performance : 1979 TOPS (INT8), 989 TFLOPs (Tensor)
• Typical usage : LLM, transformers, exascale computing

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🏗️ Architecture

GPU Allocation with VMs

GPU Allocation with Kubernetes

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⚙️ Configuration

GPU on VM

apiVersion: apps.cozystack.io/v1alpha1
kind: VirtualMachine
spec:
  instanceType: "u1.xlarge"
  gpus:
    - name: "nvidia.com/AD102GL_L40S"

GPU on Kubernetes Worker

apiVersion: apps.cozystack.io/v1alpha1
kind: Kubernetes
spec:
  nodeGroups:
    gpu-workers:
      instanceType: "u1.xlarge"
      gpus:
        - name: "nvidia.com/AD102GL_L40S"

GPU in Kubernetes Pod

apiVersion: v1
kind: Pod
spec:
  containers:
  - name: gpu-app
    image: nvidia/cuda:12.0-runtime-ubuntu20.04
    resources:
      limits:
        nvidia.com/gpu: 1

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📋 Approach Comparison

Aspect	GPU on VM	GPU on Kubernetes
Isolation	Complete (1 GPU = 1 VM)	Shared (orchestrated)
Performance	Native (passthrough)	Native (device plugin)
Management	Manual	Automated
Scaling	Vertical only	Horizontal + Vertical
Sharing	No	Yes (between pods)
Complexity	Simple	Complex

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🚀 Next Steps

For Virtual Machines

• Create a GPU VM → Practical guide
• API Reference → Complete configuration

For Kubernetes

• GPU Clusters → Workers with GPU
  • Advanced configuration → GPU NodeGroups