Free AI from NVIDIA: How the Corporation Turns Open Models into a Gold Mine
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open-source AI model, making weights, training data, and methodologies freely available. This is not merely a gesture of goodwill: behind the apparent generosity lies a calculated strategy.
Nemotron 3 Ultra is not just another "scaled-up transformer." It is built on a hybrid architecture combining three approaches: Mamba-2 layers, classic Attention, and Latent MoE (Mixture of Experts). Mamba-2 layers process long texts with linear cost growth, rather than the quadratic cost of standard attention. Latent MoE compresses data before passing it to experts, forcing each to work narrowly and precisely. The result: 550 billion parameters, but only about 55 billion are used per token processed. This yields 5-6 times greater throughput and 30% lower task costs compared to alternatives.
Strategy: From Model to Ecosystem
The main value of this release is not the model itself, but the ecosystem NVIDIA is building around its hardware. Anyone running Nemotron almost certainly does so on NVIDIA GPUs, fine-tunes it with NVIDIA tools, and deploys it on NVIDIA software. Openness here is not charity, but a way to steer developers back to purchasing the company's hardware.
NVIDIA's financial resources are incomparable to the model's costs. With a market cap exceeding $5 trillion, training Nemotron 3 Ultra—likely costing hundreds of millions of dollars—is a nearly negligible expense for the company. GPU sales more than cover research costs, allowing NVIDIA to give the model away for free and still earn more than closed competitors charging for access.
Political Context and Real Limitations
An open American model that can be inspected, modified, and run on private servers is especially valuable for countries building independent national AI—from Europe to Southeast Asia. It cannot be remotely disabled, an advantage given recent restrictions around closed models.
However, Nemotron 3 Ultra is not the smartest model on the market. On the Artificial Analysis Intelligence Index, it scored 48 points—the best among open US models, but trailing leaders like Kimi K2.6 (54 points) and DeepSeek. Open models generally lag behind closed ones by three to seven months. But this gap matters less if an open model is sufficient for real-world tasks. A bank deploying Nemotron for loan processing on its own servers doesn't need flagship-level intelligence—it needs a model that can be fine-tuned on private data, kept within a secure perimeter, and not expose confidential information to outsiders.
Analyst's Comment: NVIDIA's bet on efficiency rather than benchmark records may prove more farsighted. In mass AI adoption, the cost of running a model becomes paramount, and one that is nearly as capable but five times cheaper wins in real-world deployment. Given its resources, motivation, and distribution channels, NVIDIA can release increasingly powerful open models faster than any other company. This turns "free" AI into a powerful lever for selling hardware.