feature(cluster): Add VULTR vke as an inference cluster provider

[haarchri]

Description of your changes

Modelplane can provision inference clusters on GKE, EKS, AKS, and Nebius; Vultr was listed as Planned on the providers page. This adds Vultr VKE as a fifth provisioned cluster source, built on the xpkg.upbound.io/upbound/provider-vultr provider.

An InferenceCluster with source: Vultr composes a new VultrCluster XR, served by the new compose-vultr-cluster function. The VKE cluster carries a fixed inline system pool for control-plane components; each user GPU pool becomes a separate VkeNodePool managed resource gated on cluster readiness, so pools can be added, resized, or removed without touching the cluster. InferenceClass gains a vultr provisioning block. Vultr sizes nodes by plan (e.g. vcg-l40s-16c-180g-48vram for 1x L40S), so the block is just plan plus accelerator.

Two things VKE ships built in shape the composition. Vultr pre-installs the NVIDIA GPU Operator on GPU clusters, so rather than composing a GPU stack, VultrCluster gates its readiness on an Observe-only Object with a CEL query over the nvidia-operator-validator DaemonSet, the cluster only reports Ready once the GPU stack is validated and nvidia.com/gpu is advertised, and the serving stack never starts before that. The Vultr CSI driver likewise installs the vultr-vfs-storage RWX StorageClass (Vultr File System) on every cluster, and node autoscaling is served by VKE itself, so no CSI driver, StorageClass, or in-cluster autoscaler is composed.

VKE ships the vultr-vfs-storage ReadWriteMany StorageClass backed by Vultr File System, but it is not usable on GPU nodes, so ModelCache RWX storage is served by Longhorn instead, a Helm release installs Longhorn (tolerating the GPU taint so its node components run everywhere), an NFS-client installer DaemonSet satisfies Longhorn's RWX prerequisite on VKE's node image, and the modelplane-rwx-longhorn StorageClass pins ModelCache PVCs to driver.longhorn.io.

Known limitation:

• UED-84YHJ: fabric networking is not available for GPU nodes either, so there is no high-bandwidth interconnect for multi-node serving. Serving is therefore limited to single-node GPU instances#

Note:

• YTM-97SIL: VKE does not support VPC-only clusters (the vpc_only flag fails cluster creation with Error 500), so nodes keep public network interfaces; a VPC can be attached but not made the only network; thats how Vultr API works today

Fixes #305

I have:

• Read and followed Modelplane's contribution process.
• Run nix flake check (or ./nix.sh flake check) and made sure it passes.
• Added or updated tests covering any composition function changes.
• Signed off every commit with git commit -s.

Tested

Validated end to end: a single-node L40S GPU pool provisions, passes the GPU readiness gate, and serves a model.

• Create ProviderConfig + Secret

apiVersion: v1
kind: Secret
metadata:
  name: vultr-credentials
  namespace: crossplane-system
type: Opaque
stringData:
  api-key: ${VULTR_API_KEX}
---
apiVersion: vultr.crossplane.io/v1alpha1
kind: ClusterProviderConfig
metadata:
  name: default
spec:
  credentials:
    source: Secret
    secretRef:
      namespace: crossplane-system
      name: vultr-credentials
      key: api-key

• Create Modelplane Manifests:

---
apiVersion: modelplane.ai/v1alpha1
kind: InferenceGateway
metadata:
  name: default
  namespace: crossplane-system
spec:
  backend: Traefik
  traefik:
    version: "40.2.0"
    loadBalancer: MetalLB
    metallb:
      addressPool: "172.18.255.200-172.18.255.250"
---
apiVersion: modelplane.ai/v1alpha1
kind: InferenceClass
metadata:
  name: a16-1x
  namespace: crossplane-system
spec:
  description: "Vultr vcg-a16-6c-64g-16vram, NVIDIA A16 16GB"
  provisioning:
    provider: Vultr
    vultr:
      plan: vcg-a16-6c-64g-16vram
      accelerator:
        type: nvidia-a16
        count: 1
  devices:
  - name: gpu
    claim: DRA
    driver: gpu.nvidia.com
    deviceClassName: gpu.nvidia.com
    count: 1
    attributes:
      architecture: { string: Ampere }
      cudaComputeCapability: { version: "8.6.0" }
    capacity:
      memory: { value: "16384Mi" }
---
apiVersion: modelplane.ai/v1alpha1
kind: InferenceCluster
metadata:
  name: vultr-demo
  namespace: crossplane-system
  labels:
    modelplane.ai/region: ewr
spec:
  cluster:
    source: Vultr
    vultr:
      region: ewr
  nodePools:
  - name: gpu-a16
    className: a16-1x
    nodeCount: 1
    minNodeCount: 1
    maxNodeCount: 1
---
apiVersion: v1
kind: Namespace
metadata:
  name: ml-team
---
apiVersion: modelplane.ai/v1alpha1
kind: ModelDeployment
metadata:
  name: qwen-7b
  namespace: ml-team
spec:
  replicas: 1
  template:
    spec:
      engines:
      - name: qwen-7b
        members:
        - role: Standalone
          nodeSelector:
            devices:
            - name: gpu
              count: 1
              selectors:
              - cel: |
                  device.capacity["gpu.nvidia.com"].memory.compareTo(quantity("14Gi")) >= 0
          template:
            spec:
              containers:
              - name: engine
                image: vllm/vllm-openai:v0.9.2
                args:
                - --model=Qwen/Qwen2.5-7B-Instruct-AWQ
                - --served-model-name=qwen-7b
                - --max-model-len=8192
                - --gpu-memory-utilization=0.85
                - --enforce-eager
---
apiVersion: modelplane.ai/v1alpha1
kind: ModelService
metadata:
  name: qwen-7b
  namespace: ml-team
spec:
  endpoints:
  - selector:
      matchLabels:
        modelplane.ai/deployment: qwen-7b

• Tested Model + ModelEndpoint:

{"id":"chatcmpl-5f3c5820-b281-4872-8ed0-9137b2890359","object":"chat.completion","created":1785133519,"model":"qwen-7b","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"Hello from Vultr! It's nice to meet you. Vultr is a cloud computing platform that offers flexible and affordable virtual server instances. How can I assist you today with regards to Vultr or cloud computing in general?","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":35,"total_tokens":81,"completion_tokens":46,"prompt_tokens_details":null},"prompt_logprobs":null,"kv_transfer_params":null}

[haarchri]

after chatting with @mayankdebnath we added longhorn for ModelCache StorageClass - that we can use storage with GPU Nodes ... its working as validated:

8s           Normal  SuccessfulAttachVolume  Pod/qwen-7b-bf743-qwen-7b-a7b89-69b8dc8d49-plw89       AttachVolume.Attach succeeded for volume "pvc-66369d5e-899e-47d7-9382-8c72d5fe0fd1"
7s           Normal  Pulling          Pod/qwen-7b-bf743-qwen-7b-a7b89-69b8dc8d49-plw89       Pulling image "vllm/vllm-openai:v0.9.2"