Many nodes,
one InfiniBand
fabric.

Python

import fantasti

fx = fantasti.Client()                        # reads FANTASTI_API_KEY

cluster = fx.clusters.create(
    name="pretrain-a",
    gpu="B200:8",                             # 8-GPU nodes
    nodes=16,                                 # all on one InfiniBand fabric
    capacity=fantasti.Reserved("rsv_7d2k"),   # or OnDemand(), or Spot(...)
    interfaces=["kubernetes"],
    mounts={"/data": "fs_datasets"},          # shared filesystem on every node
)
cluster.wait("ready")
cluster.kubeconfig.save("~/.kube/fantasti-acme.yaml")

REST

curl -X POST https://api.fantasti.ai/v1/clusters \
  -H "Authorization: Bearer $FANTASTI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "name": "acme-train",
        "gpu": "H200:8",
        "nodes": 2,
        "capacity": { "mode": "spot", "max_price": "3.10", "currency": "USD", "unit": "gpu_hour" },
        "interfaces": ["kubernetes", "skypilot"]
      }'

{ "id": "cl_8f2k…", "status": "placing", "fabric": "single" }

kubectl

# Console: Clusters → acme → Connect → download kubeconfig
export KUBECONFIG="$HOME/.kube/fantasti-acme.yaml"

kubectl get pods -A      # your workloads and your cluster's system pods, nothing else
kubectl get nodes -L nvidia.com/gpu.product,nvidia.com/gpu.count

NAME          STATUS  ROLES   GPU.PRODUCT  GPU.COUNT
gpu-node-01   Ready   <none>  NVIDIA-H200  8
gpu-node-02   Ready   <none>  NVIDIA-H200  8

MCP

{
  "mcpServers": {
    "fantasti": { "type": "http", "url": "https://mcp.fantasti.ai/mcp" }
  }
}

clusters_list
clusters_get
clusters_create    asks for confirmation
clusters_delete    asks for confirmation