How to add NVIDIA free models to VS Code

NVIDIA offers free access to their powerful Nemotron models through their NVIDIA Build platform. In this post, I’ll walk you through how to get a free API key and configure it in VS Code to use models like nvidia/nemotron-3-ultra-550b-a55b directly in VS Code. Also you can choose any other model from here.

Prerequisites

VScode needs to be above 1.27 it needs to have customendpoint supporting

Getting Your Free NVIDIA API Key

1. Go to build.nvidia.com
2. Sign in or create an NVIDIA account
3. Navigate to the model you want to use (e.g., Nemotron 3 Ultra)
4. Click “Get API Key” button

Note: In my experience, the “Get API Key” button in the UI didn’t work properly (it appeared to do nothing). If this happens to you, here’s how to get your API key:

1. Open your browser’s Developer Tools (F12) → Network tab
2. Click the “Get API Key” button again
3. Look for a POST request to https://api.nvcf.nvidia.com/v2/nvcf/api-keys
4. Click on that request and check the Response tab - you’ll find your API key there

The API call looks like this (for reference - you don’t need to run this manually):

# This is what the UI calls behind the scenes - for reference only
curl 'https://api.nvcf.nvidia.com/v2/nvcf/api-keys' \
  -H 'Accept: */*' \
  -H 'Accept-Language: en-GB,en;q=0.9' \
  -H 'Accept-Encoding: gzip, deflate, br, zstd' \
  -H 'Referer: https://build.nvidia.com/' \
  -H 'content-type: application/json' \
  -H 'Origin: https://build.nvidia.com' \
  -H 'Connection: keep-alive' \
  -H 'Cookie: ...' \
  -H 'Sec-Fetch-Dest: empty' \
  -H 'Sec-Fetch-Mode: cors' \
  -H 'Sec-Fetch-Site: same-site' \
  --data-raw '{"name":"","type":"AI_PLAYGROUNDS_KEY","expiryDate":"2027-01-19T11:37:44Z","policies":[{"product":"nv-cloud-functions","scopes":["invoke_function"],"resources":[{"id":"*","type":"account-functions"}]}]}'

The response will contain your API key:

{
  "apiKey": {
    "keyId": "1a9e12d7-b35f-4ed0-943c-3935c442a5c9",
    "name": "NVIDIABuild-Autogen-54",
    "type": "AI_PLAYGROUNDS_KEY",
    "value": "nvapi-...",
    "ownerId": "...",
    "status": "ACTIVE",
    "expiryDate": "2027-01-19T11:37:44.000Z",
    "createdDate": "2026-07-19T11:39:27.197Z",
    "policies": [
      {
        "product": "nv-cloud-functions",
        "productDisplayName": "NVIDIA Cloud Functions",
        "resources": [
          {
            "id": "*",
            "type": "account-functions"
          },
          {
            "id": "*",
            "type": "authorized-functions"
          }
        ],
        "roles": [],
        "scopes": [
          "invoke_function",
          "list_functions",
          "list_functions_details",
          "queue_details"
        ]
      },
      {
        "product": "ai-foundations",
        "productDisplayName": "Public API Endpoints"
      },
      {
        "product": "artifact-catalog",
        "productDisplayName": "NGC Catalog"
      },
      {
        "product": "private-registry",
        "productDisplayName": "NVIDIA Private Registry"
      }
    ]
  }
}

Important: Save the value field (the nvapi-... key) - you’ll need it for VS Code configuration.

Configuring VS Code

The recommended way is to use the VS Code UI to add the custom model, which will properly encrypt and store your API key. Custom language models are configured in a separate file called chatLanguageModels.json (not in settings.json under chat.mcp.servers - that’s for MCP servers, which is a different feature).

Using VS Code UI

Check the video if you have not done before. Below are the steps

1. Open VS Code Command Palette (Cmd/Ctrl + Shift + P)
2. Search for “Chat: Manage Language Models” here the docs
3. Click “Add Model” or “Add Custom Model”
4. Fill in the details:
   • Name: Nvidia (or any name you prefer)
   • Vendor: customendpoint
   • API Key: Enter your nvapi-... key (VS Code will encrypt and store it securely)
   • Model ID: nvidia/nemotron-3-ultra-550b-a55b
   • Model Name: nvidia/nemotron-3-ultra-550b-a55b
   • URL: https://integrate.api.nvidia.com/v1/chat/completions
   • Tool Calling: ✅ Enabled
   • Max Input Tokens: 262144
   • Max Output Tokens: 32000

in chatLanguageModels.json will have json like below

[
  {
		"name": "Nvidia",
		"vendor": "customendpoint",
		"apiKey": "${input:chat.lm.secret.6878c97f}",
		"models": [
			{
				"id": "nvidia/nemotron-3-ultra-550b-a55b",
				"name": "nvidia/nemotron-3-ultra-550b-a55b",
				"url": "https://integrate.api.nvidia.com/v1/chat/completions",
				"toolCalling": true,
                "vision": false,
                "maxInputTokens": 262144,
                "maxOutputTokens": 32000
			}
		]
	}
]

Using the Model in VS Code

Once configured, you can use the model in VS Code Chat:

1. Open the Chat view (Ctrl+Alt+I / Cmd+Option+I)
2. Click the model selector dropdown
3. Select “Nvidia” → “nvidia/nemotron-3-ultra-550b-a55b” (or whatever name you gave the model)
4. Start chatting!

The model supports:

• Tool calling - Can use tools and functions
• Vision - Can analyze images
• 128K context window - Large context for long conversations
• 16K output tokens - Long responses

Model Details

Tips

1. Rate Limits: The free tier has rate limits. Check NVIDIA’s documentation for current limits.
2. API Key Expiry: The key expires on the date shown in the response (e.g., 2027-01-19). You’ll need to generate a new one when it expires.
3. Multiple Models: You can add multiple NVIDIA models to the same configuration by adding more entries to the models array.
4. VS Code Insiders: If you’re on VS Code Insiders, the settings path might be slightly different.

Resources

• NVIDIA Build Platform
• Nemotron 3 Ultra Model Card
• NVIDIA API Documentation
• VS Code Copilot Custom Models Documentation
• All free model from Nvidia

Happy coding with NVIDIA’s free models! 🚀