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## NVIDIA NIM: Free API Access to Advanced AI Models
**NVIDIA NIM** (NVIDIA Inference Microservices) provides free API access to a wide range of state-of-the-art AI models. Upon registration at [build.nvidia.com](https://build.nvidia.com), you receive approximately 1,000 free credits — enough for thousands of requests with smaller models.
### Why NIM Matters
The API is **fully OpenAI-compatible**, meaning any tool, library, or script that works with OpenAI's API works with NIM — just swap the base URL and API key. This makes integration trivial.
### Key Models Available
NIM hosts an impressive lineup: **DeepSeek R1** (671B MoE, excellent reasoning), **DeepSeek V3.1** (128K context with tool calling), **Kimi K2.5** (1T MoE, multimodal with video support), **Qwen 3.5 397B** (vision + agentic capabilities), **GLM-5 744B** (complex reasoning), and **Nemotron Nano 30B** (1M context window with tool calling). Smaller distilled models like DeepSeek R1 Distill Llama 8B and Qwen 32B offer great performance for simpler tasks.
### Quick Setup
1. **Register** at [build.nvidia.com](https://build.nvidia.com) (Google/GitHub login)
2. **Get an API key** from any model page (starts with `nvapi-`)
3. **Test with curl** using the OpenAI-compatible endpoint at `https://integrate.api.nvidia.com/v1`
4. **Connect to OpenClaw** by adding an `nvidia` provider to `openclaw.json` with the `openai-completions` API type
5. **Use** `nvidia/deepseek-ai/deepseek-r1` (or any other model) as your model name
### When to Use NIM
NIM is ideal for **sub-agents** (saves premium model quota), research/summarization, translation, text analysis, and code review. It's perfect for any task that doesn't involve sensitive personal or business data.
### Limitations
All data passes through NVIDIA servers (not for sensitive info), free tier has rate limits, credits are limited (~1,000 on signup), and response times vary on shared infrastructure. Not all models support tool calling (DeepSeek V3.1 and Nemotron do).
### Pro Tip
The winning combination: use Opus/Sonnet as your primary model for complex reasoning and tool use, and NIM for sub-agents handling focused tasks. Smart delegation = better results at lower cost.
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