name: model-center
description: Unified interface to 42+ NVIDIA NIM API models — LLM chat, vision, embeddings, image generation, with price comparison and model recommendation.
version: 1.0.0
tags:

• nvidia
• ai
• llm
• api
• models
• nim

category: "Data & APIs"
metadata:
openclaw:
requires:
env:

• NVIDIAAPIKEY

bins:

• python

primaryEnv: NVIDIAAPIKEY
envVars:

• name: NVIDIAAPIKEY

required: true
description: NVIDIA NIM API key from https://build.nvidia.com
emoji: 🤖
homepage: https://build.nvidia.com

NVIDIA AI Model Center

A Python skill that provides a unified interface to 42+ NVIDIA NIM API models — LLM chat, vision analysis, text embeddings, image generation, and more.

Quick Start

from model_center import ModelCenter

center = ModelCenter()

# List all models
models = center.list_models()
print("Available categories:", list(models.keys()))

# Get model info
info = center.get_model_info('nemotron-3-super-8b')
print(f"Model: {info['name']}, Provider: {info['provider']}")

# Compare pricing
comparisons = center.compare_pricing(['nemotron-3-super-8b', 'llama-3.1-70b-instruct'])
for c in comparisons:
    print(f"{c['name']}: ${c['input_price']}/M in, ${c['output_price']}/M out")

# Get recommendations
rec = center.recommend_model('code generation', 'low', False)
print(f"Recommended: {rec}")

# Estimate cost
cost = center.estimate_cost('nemotron-3-super-8b', 1000, 500)
print(f"Estimated cost: ${cost['total_cost']}")

# Chat with a model (requires NVIDIA_API_KEY env var)
# response = center.chat_completion(
#     model='nemotron-3-super-8b',
#     messages=[{'role': 'user', 'content': 'Hello'}],
#     temperature=0.7,
#     max_tokens=100
# )

Setup

1. Get an API key from build.nvidia.com
2. Set the NVIDIA_API_KEY environment variable:

   $env:NVIDIA_API_KEY = "nvapi-..."

1. Install dependency: pip install requests
2. Import and use ModelCenter or NVIDIAAPIClient from model_center.py

API

ModelCenter

| Method | Description |
|--------|-------------|
| list_models(category) | List all models or by category |
| get_model_info(model_id) | Get detailed model information |
| compare_pricing(model_ids) | Compare pricing across models |
| recommend_model(use_case, budget, need_vision) | AI-powered model recommendation |
| estimate_cost(model, input_tokens, output_tokens) | Estimate API call cost |
| chat_completion(model, messages, ...) | Chat completion API call |
| generate_image(model, prompt, ...) | Image generation API call |
| get_embedding(model, input_text) | Embedding API call |
| chat(model, message, system_prompt) | Simple chat interface |

Categories

• llm: Chat completion models (Nemotron, Llama, Mixtral, etc.)
• vision: Image understanding models
• embedding: Text embedding models
• image: Image generation models
• moderation: Content moderation models

Source

The implementation lives in model_center.py (548 lines) in this skill directory.