FAQ — GPU

What GPU models are available?

Hikube offers three NVIDIA GPU families:

GPU	Architecture	Memory	Use case
L40S	Ada Lovelace	48 GB GDDR6	Inference, graphical rendering
A100	Ampere	80 GB HBM2e	ML training, scientific computing
H100	Hopper	80 GB HBM3	LLM, exascale computing

Identifiers to use in manifests:

gpus:
  - name: "nvidia.com/AD102GL_L40S"       # L40S
  - name: "nvidia.com/GA100_A100_PCIE_80GB" # A100
  - name: "nvidia.com/H100_94GB"           # H100

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What is the difference between GPU in VM and GPU in Kubernetes?

Aspect	GPU in VM	GPU in Kubernetes
Access mode	Exclusive PCI passthrough	Shared via device plugin
Isolation	GPU dedicated to the VM	Scheduling orchestrated by K8s
Driver installation	Manual (cloud-init)	Automatic (GPU Operator)
Use case	Dedicated workstation, CUDA dev	Containerized workloads, batch

In a VM, the GPU is directly attached via PCI passthrough: the VM has exclusive access to the hardware. In Kubernetes, the GPU Operator manages drivers and scheduling allows sharing GPU resources across multiple pods.

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What CPU/GPU ratio is recommended?

For optimal usage, plan for 8 to 16 vCPU per GPU. Universal (u1) instances with a 1:4 ratio are recommended:

Configuration	Instance	vCPU	RAM
1 GPU	u1.2xlarge	8	32 GB
1 GPU (intensive)	u1.4xlarge	16	64 GB
Multi-GPU	u1.8xlarge	32	128 GB

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How are NVIDIA drivers installed?

Installation depends on the usage mode:

In a VM: install manually via cloud-init at boot:

vm-gpu.yaml

spec:
  cloudInit: |
    #cloud-config
    runcmd:
      - apt-get update
      - apt-get install -y linux-headers-$(uname -r)
      - apt-get install -y nvidia-driver-550 nvidia-utils-550

In Kubernetes: enable the GPU Operator addon which automatically installs drivers on GPU nodes:

cluster-gpu.yaml

spec:
  addons:
    gpuOperator:
      enabled: true

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How do I verify that the GPU is detected?

In a VM:

nvidia-smi

This command displays detected GPUs, their memory usage, and active processes.

In Kubernetes:

kubectl get nodes -o json | jq '.items[].status.allocatable["nvidia.com/gpu"]'

You can also verify from within a pod:

kubectl exec -it <pod-name> -- nvidia-smi

---

How do I request a GPU in a Kubernetes pod?

Specify the nvidia.com/gpu resource in the container limits:

pod-gpu.yaml

apiVersion: v1
kind: Pod
metadata:
  name: gpu-workload
spec:
  containers:
    - name: cuda-app
      image: nvidia/cuda:12.0-base
      resources:
        limits:
          nvidia.com/gpu: 1

note

The number of GPUs requested in limits must correspond to physical GPUs available on the nodes. A pod cannot request a fraction of a GPU.