NVIDIA is giving away powerful AI for free: how the open-source Nemotron 3 Ultra model is turning into a goldmine
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open-source AI model from the Nemotron 3 family. This is not just a release of weights: the company has made the training data, methodologies, and the model parameters themselves publicly available under a free license. This is not charity, but a well-thought-out market strategy that allows NVIDIA to earn more than its closed competitors.
Unlike giants such as 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.
Architecture: A Hybrid That Saves Resources
Nemotron 3 Ultra is built on a hybrid architecture combining three approaches: Mamba-2 layers, an Attention mechanism, and Latent Mixture of Experts (Latent MoE).
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not exponentially like classic attention. Attention layers, in turn, accurately retain large volumes of text in memory. And Latent MoE compresses data before passing it to 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. A context window of 1 million tokens and a speed of over 300 tokens per second provide five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: The Ecosystem as the Main Asset
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 graphics cards, 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 do 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 one's own servers—this has made 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 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 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 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 closed data, kept within a secure perimeter, and not expose confidential information to outsiders.
NVIDIA's bet on efficiency rather than test records may prove more far-sighted. With mass AI adoption, the cost of running a model comes to the forefront, and one that is nearly 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.
Analyst's Comment: This release is not about AI, but about an infrastructure monopoly. NVIDIA is turning its chips into a de facto standard, making competitors hostages of its own ecosystem. For the crypto industry, where data sovereignty and decentralization are key values, an open model with local deployment capabilities could become a catalyst for new projects at the intersection of AI and blockchain.