Concepts — GPU
Architecture
Hikube allows attaching NVIDIA GPUs directly to virtual machines and Kubernetes clusters. GPU allocation is managed by the NVIDIA GPU Operator on the Kubernetes side, and by PCI passthrough on the virtual machine side (KubeVirt).
Terminology
| Term | Description |
|---|---|
| GPU Operator | NVIDIA GPU Operator — automatically manages drivers, the device plugin, and the GPU runtime on Kubernetes nodes. |
| Device Plugin | Kubernetes plugin that exposes GPUs as schedulable resources (nvidia.com/<model>). |
| PCI Passthrough | Technique that assigns a physical GPU directly to a VM, providing native performance. |
| CUDA | NVIDIA parallel computing platform, used for GPU acceleration (ML, HPC, rendering). |
| Instance Type | CPU/RAM resource profile for the VM. Sized based on the number of GPUs (8-16 vCPU per GPU recommended). |
Available GPU types
| GPU | Architecture | Memory | Use case |
|---|---|---|---|
| L40S | Ada Lovelace | 48 GB GDDR6 | Inference, development, prototyping |
| A100 (PCIe / SXM4) | Ampere | 80 GB HBM2e | ML training, fine-tuning |
| RTX PRO 6000 Blackwell | Blackwell | 96 GB GDDR7 | LLM, intensive computing, distributed training |
GPU identifiers in manifests
| GPU | gpus[].name value |
|---|---|
| L40S | nvidia.com/AD102GL_L40S |
| A100 PCIe 80 GB | nvidia.com/GA100_A100_PCIE_80GB |
| A100 SXM4 80 GB | nvidia.com/GA100_A100_SXM4_80GB |
| RTX PRO 6000 Blackwell | nvidia.com/GB202GL_RTX_PRO_6000_BLACKWELL_SERVER_EDITION |
GPU on virtual machines
GPUs are attached to VMs via PCI passthrough:
- The physical GPU is dedicated to the VM (native performance)
- Declared in
spec.gpus[]of theVMInstancemanifest - Multi-GPU is possible (repeat entries in
gpus[]) - NVIDIA drivers must be installed inside the VM
Recommended CPU/GPU ratio
Plan for 8 to 16 vCPU per GPU. For a single GPU, a u1.2xlarge (8 vCPU, 32 GB RAM) is a good starting point.
GPU on Kubernetes
GPUs are exposed to pods via the NVIDIA Device Plugin:
- The GPU Operator must be enabled on the cluster (
addons.gpuOperator.enabled: true) - Pods request a GPU via
resources.limits(nvidia.com/gpu: 1) - The Kubernetes scheduler places the pod on a node that has the requested GPU
- GPU nodes are configured in node groups with the
gpus[]field
VM vs Kubernetes comparison
| Criterion | GPU on VM | GPU on Kubernetes |
|---|---|---|
| Isolation | Dedicated GPU (passthrough) | GPU shared via device plugin |
| Performance | Native performance | Native performance |
| Flexibility | Full OS, manual drivers | Containers, automatic scaling |
| Multi-GPU | Via spec.gpus[] | Via resources.limits |
| Use case | Workstations, interactive environments | ML pipelines, large-scale inference |
Limits and quotas
| Parameter | Value |
|---|---|
| GPU per VM | Multiple (depending on availability) |
| GPU per Kubernetes pod | Multiple (via resources.limits) |
| GPU types | L40S, A100 (PCIe/SXM4), RTX PRO 6000 Blackwell |
| Max GPU memory | 96 GB (RTX PRO 6000 Blackwell) |
Further reading
- Overview: GPU service presentation
- API Reference: detailed GPU configuration