NVIDIA gives away AI for free, but makes more money from it than anyone else: the Nemotron 3 Ultra strategy
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open model in the Nemotron 3 lineup. The company made not only the model weights publicly available under a free license, but also the training data and training methodologies. This is not charity, but a well-thought-out business move that is already bearing fruit.
Unlike closed flagships 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 over the model.
Architecture: A Hybrid That Saves Resources
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 MoE (Mixture of Experts). Mamba-2 processes long texts quickly and efficiently—costs grow linearly, not exponentially like in the standard attention mechanism. Attention layers accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to experts, allowing each to work narrowly and precisely without requiring additional computation.
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. 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: Give Away Models, Sell Hardware
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 GPUs, 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 toward purchasing the company's hardware.
NVIDIA can afford this because its financial resources 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. GPU sales more than cover research costs, so NVIDIA can 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 to 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
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. 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 for loan processing 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 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.
Expert opinion: NVIDIA is not just catching up to the open-source trend—it is leading it, leveraging its unique position as a manufacturer of the "bricks" for AI. While competitors fight over subscription market shares, NVIDIA is quietly cementing its monopoly on hardware, betting that open models will become the de facto standard for the corporate sector.