NVIDIA is giving away powerful AI for free: a strategy that generates billions
On June 4, 2026, NVIDIA released its largest artificial intelligence model, Nemotron 3 Ultra, to the public. The model's weights, training data, and methodologies were published under a free license. This is not just "another open-source LLM"—it is a full-fledged strategic move that fundamentally changes the rules of the game in the AI market.
Unlike closed flagships such as ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on your own data, and deployed on your own infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and complete control over the model.
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). This mechanism directs each request only to the necessary "specialists" within the model, radically reducing computational costs.
Mamba-2 layers process long texts quickly and efficiently—their costs grow linearly, not exponentially like standard attention mechanisms. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to the experts, allowing each to work narrowly and precisely without unnecessary computations.
The model has approximately 550 billion parameters, 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: Betting on the Ecosystem
According to industry analysts, 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: whoever runs Nemotron is almost certainly doing so on NVIDIA graphics cards, fine-tuning it with NVIDIA's software tools, and deploying it on NVIDIA's 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, allowing NVIDIA to 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. No one can remotely disable such a model, which is especially valuable in light of recent restrictions surrounding closed models.
Where the Model Falls Short and What's 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 trails leaders like Kimi K2.6 (54 points) and DeepSeek. Analysts estimate that open models lag behind closed ones by three to seven months.
However, this gap matters less and less, in my opinion, 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.
My conclusion: 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, 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.