NVIDIA is giving away AI for free, but making the most money: the Nemotron 3 Ultra strategy
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open-source AI model. The weights, training data, and methodologies were published under a permissive license. This is not just a goodwill gesture: it is a clever market move that brings the company more profit than closed competitors earn from paid access.
Unlike ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on your own data, and run on your own infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and full control over the model. And this changes the game.
Architecture: A Hybrid That Works Faster and Cheaper
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE). Mamba-2 processes long texts quickly and efficiently: costs grow linearly, not exponentially like in the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to "specialists" within the model, allowing each to work narrowly and precisely without requiring unnecessary computations.
The model has about 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 of over 300 tokens per second, this provides five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: A Free Model as a Way to Sell "Shovels"
The main value of the release is not the model itself, but 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 bring developers back to purchasing the company's hardware.
NVIDIA can afford this because its financial capabilities are incomparable to the costs of the model itself. With a market capitalization exceeding $5 trillion, training Nemotron 3 Ultra, which likely cost hundreds of millions of dollars, is a nearly negligible expense for the company. Graphics card sales more than cover the research, so NVIDIA can give the model away 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 your own servers—this has made it attractive to countries building independent national AI, from Europe to Southeast Asia. No one can remotely disable such a model, and this is especially valuable in light of recent restrictions around closed models.
Where the Model Falls Short and What Comes Next
Despite all its advantages, Nemotron 3 Ultra is not the smartest model on the market. In the independent Artificial Analysis Intelligence Index, 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. Open models, according to analysts, trail closed ones by three to seven months.
But this gap, in my opinion, matters less and less if the 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 share confidential information with outsiders.
NVIDIA's bet on efficiency rather than test records may prove more farsighted. With mass AI adoption, the cost of running a model comes to the forefront, and one that is almost as smart but five times cheaper wins in real-world operation. 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.
Expert opinion: This is a classic example of how a company controlling the "means of production" in AI uses open models to further tighten the market's dependence on its hardware. Competitors betting solely on closed APIs risk being left behind—not because of quality, but because of the economies of scale that NVIDIA can afford.