NVIDIA is giving away powerful AI for free: a hidden strategy that generates billions
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open-source artificial intelligence model in its lineup. The weights, training data, and methodologies were published under a permissive license. But don't be quick to think this is an act of charity. It's a subtle and highly profitable move.
Unlike closed giants like 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 control. And this changes the rules of the game.
An Architecture That Breaks the Mold
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 layers process long texts quickly and efficiently: their costs grow linearly with length, not exponentially like 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," forcing each to work narrowly and precisely, without unnecessary computation.
The result: the model has around 550 billion parameters, but only about 55 billion are activated for processing each token. It thinks like a massive system but behaves cost-wise like a much more compact one. 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 as a Weapon
The main value of the release is not the model itself, but the ecosystem NVIDIA is building around its hardware. The logic is simple: whoever runs Nemotron almost certainly does so on NVIDIA GPUs, fine-tunes it using its software tools, and deploys 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. 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. GPU 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—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 in light of 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 lag 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 private data, kept within its secure perimeter, and not expose confidential information to outsiders.
My expert opinion: NVIDIA's bet on efficiency rather than test records may prove more far-sighted. In mass AI adoption, the cost of running a model takes center stage. A model that is almost as smart but five times cheaper wins in real-world deployment. NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company. This is not just giving away AI—it's capturing the market through an ecosystem.