Nvidia Bets on Open Models to Power AI Agents

Nvidia announced on Monday (Dec. 15) the debut of the Nemotron 3 family of open models designed to power transparent, efficient and specialized agent-centric AI across industries.

The lineup includes Nano, Super and Ultra models that leverage a hybrid latent mixture-of-experts (MoE) architecture to deliver higher throughput, extended context reasoning, and scalable performance for multi-agent workflows. Developers and enterprises can access these models, associated data and tooling to build and customize AI agents for tasks ranging from coding and reasoning to complex workflow automation.

Open-Source Models Explained

Open-source AI models are large pretrained neural networks whose weights and code are publicly available for download, inspection, modification and redistribution. By contrast with closed or proprietary models controlled by a single provider, open models enable developers, researchers and enterprises to adapt the model to specific needs, verify behavior, and integrate the technology into their systems without restrictive licensing.

True open-source AI, according to industry definitions, ideally also includes transparency around training data and methodologies to ensure trust and reproducibility. Open-source models matter because they widen access to advanced AI, let independent developers tailor systems to specific domains, and provide transparency that supports safety, auditability and regulatory compliance,

In the broader AI landscape, open models occupy a dynamic middle ground. Tech giants such as Nvidia, Meta and OpenAI have experimented with open or “open-weight” releases alongside proprietary offerings.

Open models often lag one generation behind the most advanced closed models in raw capability, but they compensate with flexibility and reduced cost, making them attractive for bespoke applications and edge deployment. As open models improve rapidly, this performance gap is shrinking, driving wider adoption, as covered by Time.

Nvidia’s Open-Model Push

Nvidia’s decision to release Nemotron 3 as an open model reflects a deliberate expansion beyond its traditional role as a hardware supplier into a more direct provider of foundational AI software.

While Nvidia has released models before, including earlier Nemotron variants and domain-specific models used by partners and internal teams, those efforts were largely positioned as reference systems or tied closely to enterprise services. With Nemotron 3, the company is making model weights, datasets, reinforcement learning environments and supporting libraries broadly available, signaling a deeper commitment to open-source development.

The move is closely tied to how Nvidia sees enterprise demand evolving. As companies deploy AI agents across operations, many want models they can run on their own infrastructure, inspect for behavior, fine-tune with proprietary data and integrate tightly with existing systems. Open models provide that flexibility while still reinforcing demand for Nvidia’s GPUs, networking and software tools.

Previous Nvidia open or semi-open models have generally performed competitively in enterprise benchmarks, particularly in reasoning, instruction following and agentic workflows, even if they have not always matched the absolute frontier of closed models.

Nvidia says Nemotron 3 builds on that foundation with a hybrid architecture that combines mixture-of-experts techniques with newer sequence modeling approaches to improve efficiency and reasoning performance. The company has cited work with companies such as Accenture, Cursor, EY, Palantir, Perplexity and Zoom, where open models are used alongside proprietary systems to balance performance, cost and governance requirements.

Competitive Dynamics

Nvidia’s open-source push comes as competition intensifies globally, particularly from China, where open-access models have advanced rapidly. Chinese firms and research labs have released models that rival Western counterparts on several benchmarks while emphasizing lower training and inference costs. Those models have gained significant traction on developer platforms, reshaping the competition for open AI.

Usage data from developer platforms suggests Chinese open models are among the most downloaded and deployed globally, prompting analysts to argue that China’s emphasis on accessibility has become a strategic advantage, as reported by the Financial Times.

One prominent example is DeepSeek-V3, developed by the Hangzhou-based startup DeepSeek. The model has drawn attention for delivering strong reasoning and coding performance while relying on more efficient architectures that reduce dependence on the most advanced GPUs.

Alibaba Cloud’s Qwen family represents another major force in the open-model ecosystem. The Qwen lineup spans multiple model sizes and tasks and has seen broad adoption across enterprise and consumer applications, with frequent appearances near the top of open-model rankings on developer platforms such as Hugging Face.