Fake GPU Operator
https://github.com/run-ai/fake-gpu-operator
Install
Pull the helm chart:
$ helm pull oci://ghcr.io/run-ai/fake-gpu-operator/fake-gpu-operator
Pulled: ghcr.io/run-ai/fake-gpu-operator/fake-gpu-operator:0.0.63
Digest: sha256:5538e7eb2391fe77af76b5477d4951a8c7fe9b2a9aaa1b5878c87994bc467b8d
Because of the confliction of RuntimeClass nvidia, the real GPU Operator must be uninstalled first:
$ helm uninstall gpu-operator -n gpu-operator
release "gpu-operator" uninstalled
Create file fake_gpu_operator_values.yaml to override the default values:
devicePlugin:
image:
pullPolicy: IfNotPresent
statusUpdater:
image:
pullPolicy: IfNotPresent
topologyServer:
image:
pullPolicy: IfNotPresent
statusExporter:
image:
pullPolicy: IfNotPresent
kwokGpuDevicePlugin:
image:
pullPolicy: IfNotPresent
migFaker:
image:
pullPolicy: IfNotPresent
topology:
nodePools:
a100:
gpuProduct: A100
gpuCount: 8
gpuMemory: 83968
h100:
gpuProduct: H100
gpuCount: 8
gpuMemory: 83968
Here we created two topology.nodePools named h100 and a100. There is also a default pool in the default values files.
Now label the nodes to use our pool settings:
$ kubectl label node las1 run.ai/simulated-gpu-node-pool=default
node/las1 labeled
$ kubectl label node las2 run.ai/simulated-gpu-node-pool=a100
node/las2 labeled
$ kubectl label node las3 run.ai/simulated-gpu-node-pool=h100
node/las3 labeled
Then install the Fake GPU Operator:
$ helm upgrade -i gpu-operator fake-gpu-operator-0.0.63.tgz --namespace gpu-operator --create-namespace -f fake_gpu_operator_values.yaml
Release "gpu-operator" does not exist. Installing it now.
NAME: gpu-operator
LAST DEPLOYED: Tue Sep 23 13:56:11 2025
NAMESPACE: gpu-operator
STATUS: deployed
REVISION: 1
TEST SUITE: None
List the running pods:
$ kubectl get po -n gpu-operator -owide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
device-plugin-6rpxv 1/1 Running 0 2m31s 192.168.185.53 las3 <none> <none>
device-plugin-7ltdt 1/1 Running 0 2m31s 192.168.67.160 las2 <none> <none>
device-plugin-brrmf 1/1 Running 0 2m31s 192.168.221.163 las1 <none> <none>
kwok-gpu-device-plugin-5c68bdbb58-jbdsd 1/1 Running 0 7m55s 192.168.67.153 las2 <none> <none>
nvidia-dcgm-exporter-fxsn2 1/1 Running 0 2m7s 192.168.185.9 las3 <none> <none>
nvidia-dcgm-exporter-wbrqz 1/1 Running 0 2m7s 192.168.67.166 las2 <none> <none>
nvidia-dcgm-exporter-x4s6g 1/1 Running 0 2m8s 192.168.221.164 las1 <none> <none>
status-updater-5bdc9dd8cb-6mt4v 1/1 Running 0 7m55s 192.168.185.48 las3 <none> <none>
topology-server-67947cbd54-kgj99 1/1 Running 0 7m55s 192.168.185.28 las3 <none> <none>
The is one device-plugin for each labelled node.
List the GPU devices:
$ kubectl get no -o custom-columns=NAME:.metadata.name,GPU:.status.capacity.nvidia\\.com/gpu
NAME GPU
las0 <none>
las1 2
las2 8
las3 8
Usage
Apply the following file to create GPU pod:
apiVersion: v1
kind: Pod
metadata:
name: gpu
spec:
restartPolicy: OnFailure
containers:
- image: ubuntu:22.04
imagePullPolicy: IfNotPresent
name: gpu-ubuntu
command: ["sh", "-c", "trap exit INT TERM; sleep infinity & wait"]
resources:
requests:
cpu: "1"
memory: 1Gi
nvidia.com/gpu: "1"
limits:
cpu: "1"
memory: 1Gi
nvidia.com/gpu: "1"
env:
- name: NODE_NAME
valueFrom:
fieldRef:
fieldPath: spec.nodeName
The env NODE_NAME is crucial to make the fake nvidia-smi work. After the pod is running, execute nvidia-smi in the pod:
$ kubectl exec gpu -- nvidia-smi
Tue Sep 23 09:36:52 2025
+------------------------------------------------------------------------------+
| NVIDIA-SMI 470.129.06 Driver Version: 470.129.06 CUDA Version: 11.4 |
+--------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
+--------------------------------+----------------------+----------------------+
| 0 H100 Off | 00000001:00:00.0 Off | Off |
| N/A 33C P8 11W / 70W | 83968MiB / 83968MiB | 100% Default |
| | | N/A |
+--------------------------------+----------------------+----------------------+
+------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
+------------------------------------------------------------------------------+
| 0 N/A N/A 8 G sh-ctrap exit INT TERM; s.. 83968MiB |
+------------------------------------------------------------------------------+
Not all the cards are shown. Actually, there is no /dev/nvidia* device files at all.
Pitfalls
The active conf for fake GPUs is stored in ConfigMaps:
$ kubectl get cm -n gpu-operator
NAME DATA AGE
hostpath-init 1 10m
kube-root-ca.crt 1 10m
topology 1 10m
topology-las1 1 10m
topology-las2 1 10m
topology-las3 1 10m
Unfortunately, if you re-label a node to change its fake GPU pool, the modification will not take effect automatically, even after the operator is reinstalled. The ConfigMap topology-<NODE_NAME>s are not deleted when the operator is uninstalled.
To make it effective, you need to delete the corresponding topology-<NODE_NAME>, then restart status-updater:
$ kubectl rollout restart deploy status-updater -n gpu-operator
deployment.apps/status-updater restarted
This will regenerate topology-<NODE_NAME>. To make it effective on the node, the device plugin on the node must be restarted.