NVIDIA is giving away powerful AI for free: how the open-source Nemotron 3 Ultra model turns into a goldmine for the giant
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open-source AI model. Under a permissive license, the company released the weights, training data, and methodologies. At first glance, this appears to be a generous gesture, but behind it lies a well-thought-out business strategy that allows NVIDIA to earn more than its closed competitors.
Nemotron 3 Ultra is not just another transformer. It is based on a hybrid architecture combining Mamba-2 layers, an attention mechanism, and a latent mixture of experts (Latent MoE). The Mamba-2 layers process long texts quickly and efficiently: costs grow linearly, rather than skyrocketing like standard attention. The attention layers, in turn, accurately retain large volumes of text in memory. Meanwhile, Latent MoE compresses data before passing it to the experts, forcing each to work narrowly and efficiently.
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 giant 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 compared to alternatives.
NVIDIA's Strategy: Betting on the Ecosystem, Not on Intelligence
The main value of the release lies not in the model itself, but in the ecosystem that 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 tools, and deploys it on its software. Openness here is not charity, but a way to steer developers back to purchasing the company's hardware.
NVIDIA can afford this because its financial capabilities are incomparable to the cost of the model. With a market capitalization exceeding $5 trillion, training Nemotron 3 Ultra, which likely cost hundreds of millions, is a nearly negligible expense. Graphics card sales more than cover research, so NVIDIA can give the model away for free and still earn more than closed competitors charging for access.
The political context adds additional weight to the release. An open American model can be inspected, modified, and run on one's own servers—this makes it attractive for countries building independent national AI, from Europe to Southeast Asia. No one can remotely shut down such a model, and this is especially valuable amid 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 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 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 a secure perimeter, and not share confidential information with outsiders.
NVIDIA's bet on efficiency rather than test records may prove more farsighted. With mass AI adoption, the cost of running a model takes center stage, and one that is almost 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: From a long-term strategic perspective, Nemotron 3 Ultra is not just a model, but a tool for market capture. NVIDIA is transforming AI from an expensive service into accessible infrastructure, tying customers to its hardware. In a world where computing costs become a critical factor, this approach could ensure the company's dominance for decades to come.