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On June 4, 2026, NVIDIA made perhaps the most significant move in the artificial intelligence industry by releasing its flagship model, Nemotron 3 Ultra, as open source. This is not just another release—it is a strategic maneuver that changes the rules of the game. Under a free license, not only the model weights were published, but also the training data and the training methodologies themselves. Unlike closed giants 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 full control.

Architecture of the Future: A Hybrid That Works

Nemotron 3 Ultra is not just an "upscaled transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE). Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly with length, rather than exponentially like the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to the experts, allowing each expert to work narrowly and precisely without requiring additional computations.

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 approximately 30% lower task costs.

NVIDIA's Strategy: Not a Model, but an Ecosystem

The main value of the release, according to industry analysts, 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 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 charge for paid access.

The political context adds further weight to the release. An open American model can be inspected, modified, and run on private servers—this has made 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 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, in my opinion, 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 does not need flagship-level intelligence—it needs a model that can be fine-tuned on closed 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 comes to the forefront, and one that is almost 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.

My conclusion: NVIDIA is not just giving away "free cheese"—it is creating a trap for competitors. By opening the model, it does not lose profit but instead expands the market for its hardware. In the long term, this could lead even the most advanced closed models to find themselves in a catching-up position, because their business model will not withstand competition from a free but effective alternative.