List inference application templates

Returns a list of available machine learning application templates from the catalog. Each template includes metadata such as name, description, cover image, documentation, tags, and a set of configurable components (e.g., model, ui). Components define parameters, supported deployment flavors, and other attributes required to create a fully functional application deployment.

GET /cloud/v3/inference/applications/catalog

Python

Python

import os
from gcore import Gcore

client = Gcore(
    api_key=os.environ.get("GCORE_API_KEY"),  # This is the default and can be omitted
)
inference_application_template_list = client.cloud.inference.applications.templates.list()
print(inference_application_template_list.count)

200

{
  "count": 1,
  "results": [
    {
      "components": {
        "model": {
          "description": "Llama 3.2 1B is a multilingual large language model developed by Meta.",
          "display_name": "Llama 3.2 1B",
          "exposable": true,
          "license_url": "https://example.com/license",
          "parameters": {
            "prefix_cache": {
              "default_value": "1",
              "description": "Controls automatic prefix caching; set to 1 to speed up inference for repetitive prompts, or 0 to disable for diverse inputs.",
              "display_name": "prefix_cache",
              "examples": [
                "0",
                "1"
              ]
            }
          },
          "readme": "Detailed documentation",
          "required": true,
          "suitable_flavors": [
            {
              "name": "inference-16vcpu-232gib-1xh100-80gb"
            }
          ]
        }
      },
      "cover_url": "https://cdn.example.com",
      "description": "This is a demo application",
      "display_name": "Llama-3.2-1B-Instruct",
      "name": "demo-app",
      "readme": "Detailed documentation",
      "tags": {
        "category": "Model",
        "model_docs_endpoint": "/docs",
        "model_inference_engine": "vllm",
        "model_owner": "Meta",
        "model_type": "Text Generation"
      }
    }
  ]
}

Authorizations

Authorization · string · header · required

API key for authentication. Make sure to include the word apikey, followed by a single space and then your token. Example: apikey 1234$abcdef

Response

200 - application/json

OK

count · integer · required

Number of objects

Required range: x >= 0

Example:

1

results · AIAppCatalog · object[] · required

Objects

Show child attributes