Was successful creating an endpoint using the API rather than the UI:

import requests
import json

# Set the name of the MLflow endpoint
endpoint_name = "test-throughput-endpoint"

# Name of the foundation model in Unity Catalog
model_name = "system.ai.llama-4-maverick"

# Get the API endpoint and token for the current notebook context
API_ROOT = dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiUrl().get()
API_TOKEN = dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiToken().get()

headers = {"Content-Type": "application/json", "Authorization": f"Bearer {API_TOKEN}"}

# Configuration for Unity Catalog foundation model
data = {
  "name": endpoint_name,
  "config": {
    "served_entities": [
      {
        "entity_name": model_name,
        "entity_version": "1",  # Use string version for UC models
        "workload_size": "Small",  # Foundation models typically need GPU
        "scale_to_zero_enabled": True
      }
    ]
  },
}

response = requests.post(
  url=f"{API_ROOT}/api/2.0/serving-endpoints", 
  json=data, 
  headers=headers
)

print(f"Status Code: {response.status_code}")
print(json.dumps(response.json(), indent=4))