apiVersion: apps/v1 kind: Deployment metadata: name: openclaw-brain-vllm namespace: customer1 labels: app: openclaw-brain spec: replicas: 1 selector: matchLabels: app: openclaw-brain template: metadata: labels: app: openclaw-brain spec: nodeSelector: cloud.google.com/gke-accelerator: "nvidia-a100-80gb" tolerations: - key: "nvidia.com/gpu-a100-80gb" operator: "Equal" value: "present" effect: "NoSchedule" containers: - name: vllm-brain image: vllm/vllm-openai:latest command: ["python3", "-m", "vllm.entrypoints.openai.api_server"] env: - name: VLLM_ATTENTION_BACKEND value: "XFORMERS" - name: HF_TOKEN value: "" - name: VLLM_TOKENIZER_MODE value: "mistral" args: - "--model" - "dphn/Dolphin-Mistral-24B-Venice-Edition" # Uncensored 24B (Native) - "--dtype" - "auto" - "--max-model-len" - "8192" - "--trust-remote-code" - "--gpu-memory-utilization" - "0.95" ports: - containerPort: 8000 resources: limits: nvidia.com/gpu: 1 memory: "32Gi" cpu: "8" requests: nvidia.com/gpu: 1 memory: "16Gi" cpu: "4" volumeMounts: - name: model-cache mountPath: /root/.cache/huggingface volumes: - name: model-cache persistentVolumeClaim: claimName: vllm-model-cache-pvc --- apiVersion: v1 kind: PersistentVolumeClaim metadata: name: vllm-model-cache-pvc namespace: customer1 spec: accessModes: - ReadWriteOnce resources: requests: storage: 100Gi --- apiVersion: v1 kind: Service metadata: name: openclaw-brain-service namespace: customer1 spec: selector: app: openclaw-brain ports: - protocol: TCP port: 8000 targetPort: 8000 type: ClusterIP