Nemotron 3 Ultra turns agent cost and runtime into NVIDIA’s pitch

The enterprise agent race is shifting from benchmark screenshots to the harder question of how long-running systems are deployed, governed, and paid for. In its GTC Taipei announcement, NVIDIA framed Nemotron 3 Ultra as one piece of a larger stack: NemoClaw blueprints, the OpenShell secure runtime, CUDA-X libraries exposed as agent skills, and integrations with enterprise software vendors.

Nemotron 3 Ultra is a 550-billion-parameter mixture-of-experts model aimed at long-running agents across coding, research, and enterprise workflows. NVIDIA says it delivers up to 5x faster inference and up to 30% lower cost than open frontier models in its class. The model has also been post-trained for common agent platforms and harnesses, including Hermes Agent, LangChain Deep Agents, OpenClaw, OpenHands, and OpenCode.

The surrounding runtime may matter as much as the model. OpenShell is designed for agents that can access files, learn tools, generate sub-agents, and maintain context across sessions. NVIDIA says it provides policy and privacy controls, while Microsoft is working with the company on Windows security primitives for native personal agents. Canonical and Red Hat are integrating OpenShell into server and full-stack AI platform environments.

NVIDIA also pointed to early enterprise use cases. Cadence, Dassault Systemes, Siemens, and Synopsys are building autonomous AI engineers for simulation and verification workflows that can take days or weeks when handled manually. Cadence is using OpenShell to secure ChipStack AI Super Agent, and NVIDIA is using that system to verify its own chip designs. CrowdStrike is applying Nemotron models to specialized security agents that identify, prioritize, and remediate vulnerabilities and policy misconfigurations. Palantir is integrating Nemotron into its AI FDE platform for air-gapped enterprise systems.

Availability is the next check. NVIDIA expects Nemotron 3 Ultra to be available on June 4 through Hugging Face, ModelScope, OpenRouter, build.nvidia.com as NIM microservices, cloud partners, and inference providers. Independent latency, cost, and reliability tests will decide how much of the promise holds outside NVIDIA’s launch environment. Still, the strategic move is clear: NVIDIA wants the agent execution layer, not only the accelerator layer.