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On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open-source AI model in the Nemotron 3 lineup. Under a permissive license, the company released not only the model weights but also the training data and training methodologies. This is not about flagship intelligence on par with ChatGPT or Claude, but a strategy where openness and efficiency become the primary weapons.

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). This approach allows each request to be directed only to the necessary "specialists" within the model, minimizing computational costs.

Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not 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 of them to work narrowly and precisely without 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 cost-wise like a much more compact one. 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.

NVIDIA's Strategy: Betting on the 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 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. No one can remotely disable such a model, 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 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 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 private data, kept within its 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 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: In the long term, NVIDIA's strategy could upend the AI market. While competitors chase benchmark records, the company is creating a "sticky" ecosystem that ties developers to its hardware. If open models continue to improve at this pace, closed flagships risk becoming a niche product for a narrow set of tasks.