Nvidia is making a strong push toward open infrastructure as autonomous AI agents become more central to enterprise software. With the launch of its Nemotron 3 model family, the company is positioning itself as a foundational technology provider for organizations that want to design, deploy, and control their own AI agents—without first building massive foundation models from the ground up.

According to Nvidia, the next generation of AI agents must be capable of sustained reasoning, collaboration with other agents, and execution across extended timeframes and large knowledge contexts. Achieving this, the company argues, requires infrastructure that is flexible, transparent, and openly accessible rather than locked behind proprietary APIs.

Nemotron 3 is Nvidia’s response to that need. The company describes the release as a set of open models and tools that enable developers to assemble domain-specific agents and applications more efficiently. In addition to the models themselves, Nvidia is opening access to much of the training data and its reinforcement learning libraries, allowing teams to customize behavior and optimize agents for specific business environments.

Industry analysts see the move as a strategic reaction to recent shifts in the AI landscape. Wyatt Mayham of Northwest AI Consulting characterized the release as Nvidia’s answer to disruptive players reshaping expectations around openness and cost. Rather than selling closed, hosted AI services, Nvidia is offering what Mayham called a “production-ready open stack” that integrates enterprise support with hardware-level optimization.

Three Model Tiers for Different Agent Workloads

The Nemotron 3 lineup is built on what Nvidia calls a hybrid latent mixture-of-experts architecture, designed to balance scale, speed, and efficiency. The family is divided into three variants, each targeting different classes of AI workloads:

• Nemotron 3 Nano is the smallest and most resource-efficient option. It is designed for narrowly scoped tasks such as fast information lookup, debugging assistance, summarization, and lightweight assistant workflows. Although the model contains roughly 30 billion parameters, only about 3 billion are active at any given time, improving performance and cost efficiency. Its context window extends to one million tokens, enabling long-term memory across complex, multi-step interactions.
• Nemotron 3 Super targets higher-precision reasoning scenarios. With approximately 100 billion parameters and up to 10 billion active per token, it is intended for environments where multiple AI agents must coordinate on demanding tasks like research synthesis or strategic analysis while maintaining low response times.
• Nemotron 3 Ultra sits at the top end of the spectrum, acting as a large-scale reasoning engine for highly complex applications. It includes roughly 500 billion parameters, with as many as 50 billion activated per token.

Nemotron 3 Nano is already accessible through platforms such as Hugging Face and several inference and enterprise AI providers. Nvidia plans to expand availability soon via Amazon Bedrock on AWS, with support also coming to Google Cloud, CoreWeave, Microsoft Foundry, and other public cloud environments. The Nano model is additionally offered as a prepackaged Nvidia NIM microservice. The Super and Ultra variants are scheduled for release in the first half of 2026.

Infrastructure, Not a Hosted AI Service

Observers emphasize that Nvidia’s approach differs fundamentally from that of companies offering turnkey AI APIs.

“Nvidia is not positioning Nemotron as a competitor to hosted AI services like OpenAI or Anthropic,” Mayham explained. “Instead, they’re aiming to be the infrastructure backbone for enterprises that want to own their agents, their data, and their execution environments.”

Brian Jackson, principal research director at Info-Tech Research Group, echoed that view, noting that Nemotron models are not meant to be consumed as finished products. Rather, they function as adaptable building blocks. “It’s closer to a kit than a ready-made solution,” he said. “Developers can start with it and shape it to match very specific operational needs.”

With Nemotron 3, Nvidia is betting that the future of agentic AI will be built not on closed platforms, but on open, customizable infrastructure that enterprises can integrate deeply into their own systems—and fully control as AI agents become more autonomous and pervasive.