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On June 4, 2026, NVIDIA released Nemotron 3 Ultra, its largest open AI model. The company made not only the model weights publicly available, but also the training data and methodologies. This is not charity: behind the free distribution lies a cold-blooded market calculation that allows NVIDIA to earn more than any closed competitor.

Unlike ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on your own data, and run on your own infrastructure. The bet is not on maximum intelligence, but on openness, efficiency, and control. This is a fundamentally different approach that turns the traditional SaaS monetization model on its head.

An Architecture That Breaks Stereotypes

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). The MoE mechanism directs each request only to the relevant "specialists" within the model, drastically reducing computational costs.

Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly with length, rather than explosively like standard attention. 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 requiring unnecessary 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. 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.

Strategy: Not a Model, But an Ecosystem

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 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, so NVIDIA can give away the model for free and still earn more than closed competitors charging for access.

The political context adds extra weight to the release. An open American model can be inspected, modified, and run on your own servers—this has made 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 against the backdrop of recent restrictions surrounding closed models.

Where the Model Falls Short and What's Next

Despite all its merits, Nemotron 3 Ultra is not the smartest model on the market. In the independent Artificial Analysis Intelligence Index ranking, 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. According to analysts, open models trail closed ones by three to seven months.

But this gap, in my opinion, 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 proprietary data, kept within its secure perimeter, and not hand over 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 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.

My conclusion: This is not just a model release, but a turning point. NVIDIA is ceasing to be just a "shovel seller" for the AI gold rush and is starting to mine gold itself. The open ecosystem is a new standard that could make closed models a niche product for narrow tasks, rather than the foundation of the industry.