OpenClaw v2026.4.29 introduces NVIDIA as a first-class provider, giving you native access to Nemotron, Kimi, MiniMax, GLM, and other models from NVIDIA’s inference catalog. This guide walks you through getting those models working in your OpenClaw agent setup.
Prerequisites
- OpenClaw v2026.4.29 or later (update via your standard update channel)
- An NVIDIA NGC API key (free tier available at build.nvidia.com)
- OpenClaw running in a configuration where you can edit provider settings
Step 1: Update to v2026.4.29
If you haven’t already, pull the latest OpenClaw version:
# Check your current version
openclaw --version
# Update (method depends on your install)
npm update -g openclaw
# or
openclaw update
Confirm you’re on 2026.4.29 or later before proceeding.
Step 2: Get Your NVIDIA API Key
- Go to build.nvidia.com and sign in or create an account
- Navigate to API Keys in your account settings
- Create a new key — name it something descriptive like
openclaw-nvidia - Copy the key; you’ll need it in the next step
NVIDIA’s free tier includes generous API credits for testing. Production usage will vary depending on your model selection and volume.
Step 3: Add the NVIDIA Provider in OpenClaw
Edit your OpenClaw configuration file at ~/.openclaw/openclaw.json directly to add the NVIDIA provider. Refer to the OpenClaw provider configuration docs for the exact JSON structure supported by your version.
The NVIDIA provider uses an OpenAI-compatible API format. The key values you’ll need to configure are:
- API Key: Your NVIDIA NGC key (
nvapi-...) - Base URL:
https://integrate.api.nvidia.com/v1(or your self-hosted NIM endpoint) - Default Model: Your preferred model from the NVIDIA catalog
As a general pattern (verify exact key names against current docs):
providers:
nvidia:
apiKey: "nvapi-YOUR_KEY_HERE"
baseUrl: "https://integrate.api.nvidia.com/v1"
defaultModel: "nvidia/llama-3.1-nemotron-70b-instruct"
Step 4: Explore the Model Catalog
Once the provider is configured, you can test it through your normal OpenClaw interface. Key NVIDIA models available at launch via https://integrate.api.nvidia.com/v1:
| Model | Best For |
|---|---|
nvidia/llama-3.1-nemotron-70b-instruct |
General reasoning, instruction following |
nvidia/llama-3.1-nemotron-nano-8b-instruct |
Fast, lightweight tasks |
moonshot-ai/kimi-k1-5 |
Long-context reasoning |
minimax/minimax-text-01 |
Multilingual, balanced quality |
thudm/glm-4-9b-chat |
Chinese-language tasks, academic reasoning |
Step 5: Test the Connection
After saving your config and restarting the gateway (openclaw gateway restart), start a new agent session and send a test message using the NVIDIA model you configured as default. You should get a response from the Nemotron model within a few seconds. If you get an auth error, double-check your API key starts with nvapi- and is saved correctly in your config.
Step 6: Set NVIDIA as a Fallback or Routing Target
OpenClaw supports multi-provider model routing. The exact configuration format is documented in the OpenClaw routing docs — refer there for the current JSON structure, as provider routing configuration evolves with each release.
Conceptually, you can configure:
- A primary model (e.g., Claude Sonnet) with NVIDIA as fallback when rate-limited
- Task-based routing (e.g., long-context requests routed to Kimi via NVIDIA)
Check the docs for the supported routing rule syntax in your installed version.
Step 7: Configure for Self-Hosted NIM (Optional)
If your enterprise runs NVIDIA NIM microservices on-premises for data sovereignty or latency reasons, point OpenClaw at your internal endpoint:
providers:
nvidia:
apiKey: "nvapi-YOUR_LOCAL_KEY"
baseUrl: "https://your-nim-host.internal/v1"
defaultModel: "nvidia/llama-3.1-nemotron-70b-instruct"
This works identically to the cloud endpoint — NVIDIA NIM uses the same OpenAI-compatible API format that OpenClaw natively supports.
Troubleshooting
“Provider not found” error: Make sure you’re on v2026.4.29 or later. Earlier versions don’t have the NVIDIA provider built in.
Auth failures: NVIDIA NGC keys are prefixed nvapi-. If yours doesn’t start that way, you may have copied the wrong token from the dashboard.
Model not in catalog: NVIDIA’s available models change as new ones are added to NIM. Check build.nvidia.com/models for the current catalog and update your config with the correct model identifier.
Slow responses: Nemotron-70b is a large model. If latency is a concern, switch to nemotron-nano-8b for lower-stakes tasks.
Further Reading
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260430-2000
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