Free AI from NVIDIA: How the Giant Gives Away Models for Free but Earns the Most
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open AI model in the Nemotron 3 lineup. The model weights, training data, and training methodologies were released to the public under a permissive license. This is not just another release; it is a strategic move that changes the rules of the game in the generative AI market.
Architecture of the Future: A Hybrid That Works Faster and Cheaper
Nemotron 3 Ultra is not a "scaled-up transformer." It is based on a hybrid architecture combining Mamba-2 layers, attention mechanisms, and a latent mixture of experts (Latent MoE). The Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not exponentially like standard attention. The attention layers accurately retain large volumes of text in memory. And Latent MoE compresses data before passing it to the experts, forcing each to work narrowly and precisely without unnecessary computations.
The model has approximately 550 billion parameters in total, but only about 55 billion are activated for processing each token. This allows it to think like a massive system while behaving like a much more compact one in terms of cost. Combined with a context window of 1 million tokens and a speed exceeding 300 tokens per second, this results in five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Give Away Models to Sell Hardware
The main value of the release lies not in the model itself, but in the ecosystem NVIDIA is building around its hardware. The logic is simple: anyone running Nemotron is almost certainly doing so on NVIDIA graphics cards, fine-tuning it with its software tools, and deploying it on its software. Openness here is not charity, but a way to steer developers back toward purchasing the company's hardware.
NVIDIA can afford this because its financial capabilities are incomparable to the model's costs. With a market capitalization exceeding $5 trillion, training Nemotron 3 Ultra, which likely cost hundreds of millions of dollars, is almost a negligible expense for the company. Graphics card sales more than cover the research, so NVIDIA can give away the model for free and still earn more than closed competitors charge for paid access.
The political context adds further weight to the release. An open American model can be inspected, modified, and run on one's own servers—making it attractive for countries building independent national AI, from Europe to Southeast Asia. Such a model cannot be remotely disabled, and this is especially valuable amid recent restrictions surrounding closed models.
Where the Model Falls Short and What Lies Ahead
Despite its advantages, Nemotron 3 Ultra is not the smartest model on the market. In the independent Artificial Analysis Intelligence Index ranking, it scored 48 points—the best result among open US models, but globally it lags behind leaders like Kimi K2.6 (54 points) and DeepSeek. According to analysts, open models trail closed ones by three to seven months.
But in my view, this gap matters less and less if an open model is simply sufficient for real-world tasks. A bank deploying Nemotron 3 Ultra to process loans on its own servers doesn't need flagship-level intelligence—it needs a model that can be fine-tuned on proprietary data, kept within its secure perimeter, and not expose confidential information to outsiders.
NVIDIA's bet on efficiency rather than benchmark records may prove more farsighted. With mass AI adoption, the cost of running a model takes center stage, and one that is nearly as smart but five times cheaper wins in real-world deployment. Analysts expect the open ecosystem to only strengthen: NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company.
My expert opinion: This move by NVIDIA is a brilliant example of how dominance in hardware allows dictating terms in the software market. Competitors betting on closed APIs risk being left behind when the majority of corporate clients shift to cheap, controllable open solutions.