NVIDIA is giving away powerful AI for free: the hidden strategy of the giant that generates billions
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open-source AI model in the Nemotron 3 lineup. The weights, training data, and training methodologies were published under a permissive license. However, this is not charity, but a finely calculated business move.
Unlike closed flagship models like ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on proprietary data, and run on your own infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and control over the model.
An Architecture That Changes the Game
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture comprising 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. Attention layers accurately retain large volumes of text in memory, while Latent MoE compresses data before passing it to experts, allowing each to work narrowly and precisely.
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. With a context window of 1 million tokens and a speed exceeding 300 tokens per second, this yields five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Ecosystem Instead of Sales
The main value of the release, according to industry analysts, lies not in the model itself, but in the ecosystem NVIDIA is building around its hardware. The logic is simple: whoever runs Nemotron almost certainly does so on NVIDIA graphics cards, fine-tunes it using its software tools, and deploys it on its software. Openness here is not charity, but a way to steer developers back towards 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 a nearly 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 release gains additional weight from the political context. An open American model can be inspected, modified, and run on private servers—making it attractive for countries building independent national AI, from Europe to Southeast Asia. No one can remotely disable such a model, and this is especially valuable given recent restrictions surrounding closed models.
Where the Model Falls Short and What's Next
For all its merits, 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 trails leaders like Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, lag behind 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. In mass AI adoption, the cost of running the model comes to the forefront, and one that is nearly as smart but five times cheaper wins in real-world operation.
My expert conclusion: NVIDIA is not just giving away "free cheese"—it is building an impenetrable ecosystem where every new user of the open model becomes a customer of its hardware. While competitors chase test records, NVIDIA is quietly capturing the infrastructure layer of the market. And this, in my view, is a far more sustainable strategy than competing for benchmark leadership.