NVIDIA MCP — Free Access to 100+ AI Models

An MCP server that gives Claude, Cursor, and any other AI agent free access to NVIDIA's hosted model catalog — Nemotron, Llama, GPT-OSS, DeepSeek, vision models, and embeddings — through one API key, with automatic model routing.

NVIDIA MCP is a Model Context Protocol server that connects your AI coding agent to build.nvidia.com. You get a free NVIDIA API key, add one config block, and your agent can call more than 100 models: reasoning models for hard problems, vision models for images, code models for refactoring, and embedding models for semantic search. The server picks the right model for each task automatically, so you describe the job instead of memorizing model names.

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Why use it

• Free NVIDIA API credits. NVIDIA gives developers free credits on build.nvidia.com. No credit card to start.
• One key, 100+ models. Nemotron, Llama 3.3, GPT-OSS, DeepSeek, Riva translation, NV-Embed, and more — all through a single OpenAI-compatible endpoint.
• Automatic routing. Say "refactor this module" or "bu görseldeki tabloyu çıkar" and the server picks a suitable model. English and Turkish task descriptions are both understood.
• Parallel cross-checking. nv_fanout sends one prompt to up to 6 models at once and returns every answer side by side — useful when one model's answer isn't enough.
• Honest availability data. Being listed in NVIDIA's catalog does not mean a model is servable. This repo ships measured probe results and a nv_probe tool so you can verify your own account.

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How to get a free NVIDIA API key

You need an nvapi-... key from NVIDIA. It takes about two minutes.

1. Go to build.nvidia.com.
2. Click Login (top right) and sign in, or create a free NVIDIA account. A personal email works; you do not need an enterprise account.
3. Open any model page — for example llama-3.3-70b-instruct.
4. Click the Get API Key button on that page (usually above the code sample on the right).
5. Click Generate Key. Your key appears once, starting with nvapi-.
6. Copy it immediately and store it somewhere safe. NVIDIA will not show it again — you would have to generate a new one.

Notes on the free tier:

• New accounts receive free API credits, enough for a substantial amount of experimentation.
• The same key works for every model in the catalog; you do not generate a separate key per model.
• Credits and per-model availability depend on your account tier. Run nv_probe (below) to see exactly what your key can reach.

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Install

git clone https://github.com/holm-digital-io/nvidia-mcp.git
cd nvidia-mcp
python3 -m venv .venv
.venv/bin/pip install -e .

Create a .env file next to the package:

NVIDIA_API_KEY=nvapi-your-key-here

Verify:

.venv/bin/python -c "from nvidia_mcp import client; print(len(client.list_model_ids()), 'models reachable')"

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Connect it to your agent

Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "nvidia-models": {
      "command": "/absolute/path/to/nvidia-mcp/.venv/bin/python",
      "args": ["-m", "nvidia_mcp.server"],
      "env": { "PYTHONPATH": "/absolute/path/to/nvidia-mcp" }
    }
  }
}

Cursor

Add the same block to ~/.cursor/mcp.json.

Claude Desktop

Add the same block to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\).

Restart the client afterwards so it picks up the new server.

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Tools

Tool	What it does
nv_categories	Lists every task category the router knows, with its top models.
nv_list_models	Live model list from your account, tagged by capability and liveness.
nv_route	"What should I use for X?" Recommends models without calling any.
nv_health	Verifies the API key and reports catalog size.
nv_probe	Actually calls each model and reports which ones answer, and how fast.
nv_call	Calls one specific model. Supports images for vision models.
nv_auto	Routes and runs in one step. Pass an image and it selects a vision model.
nv_fanout	Runs one prompt across up to 6 models in parallel, returns all answers.
nv_embed	Embeds texts. Returns shape only unless you pass save_to, to save context.
nv_similarity	Ranks candidate strings against a query by cosine similarity.

Images can be passed as an HTTPS URL, a data: URI, or a local file path — the server base64-encodes local files for you.

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Task categories

reasoning, general_chat, fast_cheap, code, vision, document_parse, embedding, embedding_code, embedding_multimodal, rag_qa, safety, reward, translation, creative, long_context, domain_medical, domain_finance, video, image_gen.

Routing has two layers: a hand-ranked table in nvidia_mcp/catalog.py, plus name-pattern heuristics so models NVIDIA adds later still get tagged. Curated position encodes quality; measured liveness is applied as a separate filter, so a code specialist still outranks a faster generalist inside code.

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Which models actually respond?

This is the part most NVIDIA integrations get wrong. GET /v1/models returned 102 model ids, but on a free-tier key only about a third answer a request — the rest return 404 Not Found or never respond.

Measured on 2026-08-11 (free tier, 45s timeout). Fastest first:

Model	Response	Best for
nvidia/nemotron-mini-4b-instruct	1.0s	fast, cheap
nvidia/riva-translate-4b-instruct-v2	1.1s	translation
nvidia/nemotron-nano-12b-v2-vl	1.5s	vision
nvidia/nemotron-3.5-lightning-30b-a3b	1.7s	fast, cheap
openai/gpt-oss-20b	1.8s	general
nvidia/nemotron-3-nano-omni-30b-a3b-reasoning	1.9s	reasoning
meta/llama-3.2-3b-instruct	2.1s	fast, cheap
nvidia/nemotron-3.5-content-safety	2.1s	moderation
meta/llama-3.2-11b-vision-instruct	2.4s	vision
nvidia/nvidia-nemotron-nano-9b-v2	2.7s	fast, cheap
nvidia/llama-3.1-nemotron-nano-vl-8b-v1	6.7s	vision
meta/muse-glimmer-30b	7.1s	creative
deepseek-ai/deepseek-v4-flash-0731	13.8s	reasoning
meta/llama-3.3-70b-instruct	15.6s	general
nvidia/llama-3.3-nemotron-super-49b-v1.5	18.3s	reasoning
poolside/laguna-xs-2.1	20.5s	code
nvidia/nemotron-3-super-120b-a12b	21.4s	general, long context
minimaxai/minimax-m3	24.0s	general
openai/gpt-oss-120b	39.1s	reasoning

Embedding models that answered: nvidia/nemotron-3-embed-1b (1.2s, 2048-dim), nvidia/nv-embedqa-e5-v5 (1.3s, 1024), nvidia/nv-embedcode-7b-v1 (3.0s, 4096), nvidia/nv-embed-v1 (3.7s, 4096), nvidia/llama-nemotron-embed-1b-v2 (4.8s, 2048), nvidia/llama-nemotron-embed-vl-1b-v2 (10.0s, 2048).

Availability is per-account and changes over time. Re-measure yours:

Ask your agent: "run nv_probe"

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Examples

Ask your agent in plain language; it selects the tool.

Route without calling

Which NVIDIA model should I use to extract a table from a scanned invoice?

Run with automatic model choice

Use nv_auto to describe what's in ./screenshot.png

Cross-check one question across models

Use nv_fanout to ask three models whether this SQL query has an injection risk

Semantic ranking

Use nv_similarity to rank these 20 support tickets against "billing problem"

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FAQ

Is the NVIDIA API really free? NVIDIA gives developers free credits on build.nvidia.com, which is enough for meaningful development and testing without a credit card. It is free credits, not unlimited free inference — heavy production use eventually requires a paid plan or self-hosted NIM containers.

Do I need an NVIDIA GPU? No. Every model runs on NVIDIA's cloud. This server only sends HTTPS requests, so it works fine on a Mac, a Windows laptop, or a small Linux VM.

Which models are best for coding? On the free tier, poolside/laguna-xs-2.1 is the only dedicated code model that responds; the other code-specific ids (Codestral, CodeLlama, StarCoder2, CodeGemma) return 404. The server therefore lists strong generalists — nvidia/nemotron-3-super-120b-a12b and meta/llama-3.3-70b-instruct — as first-class fallbacks in the code category.

Why did a model return empty text? Reasoning models spend their token budget on reasoning_content before writing an answer. If you cap max_tokens too low, text comes back empty. The server detects this and returns a note telling you to raise max_tokens (4096+ for reasoning models). nv_auto raises it automatically for reasoning tasks.

Why does a model in the catalog return 404? GET /v1/models lists the full NVIDIA catalog, not what your specific key is entitled to call. Roughly two thirds of listed ids are not servable on a free-tier key. Run nv_probe for ground truth on your account.

Does it work with Cursor and Claude Desktop? Yes. It is a standard stdio MCP server, so any MCP-compatible client works — Claude Code, Claude Desktop, Cursor, Windsurf, Cline, and others.

Is my API key sent anywhere else? No. The key is read from your local .env, used only in the Authorization header to integrate.api.nvidia.com, and is never logged or included in any tool response.

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Configuration

Variable	Default	Purpose
NVIDIA_API_KEY	—	Required. Your nvapi-... key.
NVIDIA_BASE_URL	https://integrate.api.nvidia.com/v1	Point at self-hosted NIM instead.
NVIDIA_TIMEOUT	180	Per-request timeout in seconds.

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Development

.venv/bin/python tests/test_routing.py     # routing regression suite
.venv/bin/python -m nvidia_mcp.server      # run the server directly over stdio

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License

MIT — see LICENSE.

Not affiliated with or endorsed by NVIDIA Corporation. "NVIDIA", "Nemotron", and "NIM" are trademarks of NVIDIA Corporation.