What Is NVIDIA Nemotron 3 Ultra?
Nemotron 3 Ultra is an open model from NVIDIA built specifically for long-running, agentic workflows. It uses a mixture-of-experts design, on the order of 550 billion total parameters with roughly 55 billion active per token, and supports a context window of up to one million tokens. The headline is not raw size; it is that the model is tuned for the kind of multi-step, tool-using reasoning that AI agents actually do.
Why Agent Builders Care
Most general-purpose chat models are optimized for single-turn answers. Agents are different: they plan, call tools, read results, and keep going across many steps, often over a long session. Nemotron 3 Ultra is positioned for exactly that loop, with a large context so the agent can hold a lot of working state, and strong tool-use behavior so it picks the right action instead of hallucinating one.
For anyone running an autonomous agent, a model that stays coherent across long, tool-heavy sessions is worth a look, especially when there is a free tier to test it on.
Where You Can Run It
- OpenRouter — available through OpenRouter, including a free tier, so you can route requests to it with an API key.
- Ollama — you can pull it locally with
ollama pull nemotron-3-ultraif your hardware can handle it.
Because it is reachable via OpenRouter and Ollama, it slots straight into agent frameworks that already support those providers.
How to Try It With Your Agent
Both OpenClaw and Hermes can use Nemotron 3 Ultra through OpenRouter. We have step-by-step setup guides for each:
On OpenClaw Launch, you can pick your model from the dashboard and bring your own OpenRouter key, so trying a new model is a dropdown change rather than a redeploy.
Should You Switch?
If your agent does short, simple tasks, a smaller and cheaper model is usually fine. If it runs long, tool-heavy sessions where it has to remember a lot and stay on track, an agent-tuned model with a huge context window like Nemotron 3 Ultra is exactly the kind of upgrade worth testing. Start on the free tier, compare it against your current model on your real workload, and keep whichever gives you better results per dollar.