> This page is for version 1.0.0 (Latest) (default). > For other versions, use one of these documentation indexes: > - 1.0.0 (Latest) (default): https://docs.nvidia.com/nim/metadata-api/1.0.0/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nim/metadata-api/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/metadata-api/_mcp/server. # Quickstart for NIM Metadata API > Make your first NIM Metadata API request, read the response, and understand the two shapes a profile can take. This Quickstart walks you through identifying a NIM microservice, making your first request, and reading the response. For the requirements you need first, refer to [Prerequisites](/nim/metadata-api/get-started/prerequisites). ## Prerequisites Before you start, complete the following prerequisites: 1. Install an HTTP client, such as curl or the Python `requests` library. 2. Collect the `resourceId` and tag for the NIM microservice you want to inspect, as described in [Prerequisites](/nim/metadata-api/get-started/prerequisites). ## Quickstart Steps To quickly get started with the NIM Metadata API, complete the following steps. 1. Identify the NIM microservice by its `resourceId` and tag. The `resourceId` is a slash-separated repository path, and the tag is the container tag as it appears in the catalog. For this example, use `nim/nvidia/parakeet-1-1b-rnnt-multilingual` and `1.5.0`. 2. Make the request against the query-parameter form of the endpoint. #### curl ```bash curl --request GET \ --url 'https://api.ngc.nvidia.com/v2/nim/metadata?resourceId=nim/nvidia/parakeet-1-1b-rnnt-multilingual&tag=1.5.0' \ --header 'accept: application/json' ``` #### Python Install the `requests` library before you run the following example: ```bash python -m pip install requests ``` The following example makes the request and reports the profile count: ```python import requests BASE_URL = "https://api.ngc.nvidia.com/v2" response = requests.get( f"{BASE_URL}/nim/metadata", params={"resourceId": "nim/nvidia/parakeet-1-1b-rnnt-multilingual", "tag": "1.5.0"}, headers={"accept": "application/json"}, timeout=30, ) response.raise_for_status() metadata = response.json() print(f"{len(metadata.get('profiles', []))} profiles") ``` A 200 status code returns the metadata document. A 404 status code means this combination of NIM microservice and tag has no published metadata. 3. Read the four top-level sections of the response. All four are optional. * `profiles`: The deployable configurations. This is the section you use most. * `release_details`: Release-level facts, such as version, documentation, compliance, and supported architectures. * `features`: Capability flags, such as context window, tool calling, and backend support. * `related_urls`: Links to related resources. 4. Compare the profiles against your hardware. The following abridged response shows a NIM microservice with GPU-specific profiles: ```json { "profiles": [ { "profile_id": "a1b2c3d4e5f60718293a4b5c6d7e8f90", "gpu": "H100", "gpu_device": "2330:10de", "tested_gpu_devices": ["2330:10de"], "precision": "fp8", "inference_backend": "tensorrt_llm", "tensor_parallel_size": 1, "min_vram_per_device_gb": 80.0, "feat_lora": false }, { "profile_id": "0f1e2d3c4b5a69788796a5b4c3d2e1f0", "gpu": "L40S", "gpu_device": "26b9:10de", "tested_gpu_devices": ["26b9:10de"], "precision": "bf16", "inference_backend": "vllm", "tensor_parallel_size": 2, "min_vram_per_device_gb": 48.0, "feat_lora": false } ], "release_details": { "container_version": "1.2.0", "architecture": ["AMD 64", "ARM 64"], "documentation_url": "https://docs.nvidia.com/nim/" }, "features": { "context_window": 8192, "tool_calling": false, "vllm_support": true } } ``` This response describes two profiles, two different GPUs, and two different vRAM floors. If you have L40S GPUs with 48 GB each, the second profile fits and the first does not. That comparison, made in one request before you download anything, is the core purpose of this API. 5. Recognize the GPU-agnostic case. Many NIM microservices ship profiles that run anywhere with sufficient vRAM. Those profiles omit `gpu` and `gpu_device` entirely, and can carry an empty `tested_gpu_devices` array: ```json { "profiles": [ { "profile_id": "7c8d9e0f1a2b3c4d5e6f708192a3b4c5", "tested_gpu_devices": [], "precision": "bf16", "inference_backend": "vllm", "tensor_parallel_size": 1 } ] } ``` This is not an empty or broken response. Read it as "no GPUs tested with the profile" rather than "no GPUs supported." For the full set of GPU targeting signals, refer to [Core Concepts Overview](/nim/metadata-api/about/core-concepts). ## Minimal Code Example Use the following self-contained example to fetch a metadata document and report the profiles that fit a given vRAM budget: ```python import requests BASE_URL = "https://api.ngc.nvidia.com/v2" def get_nim_metadata(resource_id, tag): """Return the metadata document, or None if none is published.""" response = requests.get( f"{BASE_URL}/nim/metadata", params={"resourceId": resource_id, "tag": tag}, headers={"accept": "application/json"}, timeout=30, ) if response.status_code == 404: return None response.raise_for_status() return response.json() metadata = get_nim_metadata("nim/nvidia/parakeet-1-1b-rnnt-multilingual", "1.5.0") if metadata is None: print("No published metadata for this NIM microservice and tag.") else: for profile in metadata.get("profiles", []): required_vram = profile.get("min_vram_per_device_gb") print(profile["profile_id"], required_vram) ``` ## Next Steps Now that you have a working request, explore the following resources in greater detail: * **Select a profile for real hardware**: [Choose a Profile for Your Hardware](/nim/metadata-api/guides/choose-a-profile) * **Learn the fundamentals**: [Core Concepts Overview](/nim/metadata-api/about/core-concepts) * **Look up every field**: [Response Fields](/nim/metadata-api/reference/response-fields) * **Build a durable integration**: [Notes for Automated Agents](/nim/metadata-api/guides/automated-agents)