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On June 4, 2026, NVIDIA released its largest AI model, Nemotron 3 Ultra, to the public. The model weights, training data, and training methodologies were published under a free license. This is not merely charity, but a calculated move that allows the tech giant to earn more than its closed competitors.

Unlike flagship models such as ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on proprietary data, and run on your own infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and complete control over the model. This changes the rules of the game in the artificial intelligence market.

An Architecture That Saves Resources

Nemotron 3 Ultra is not just an "upsized 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, not exponentially like the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. And Latent MoE compresses data before passing it to the experts, allowing each expert to work narrowly and precisely without requiring unnecessary computation.

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 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, Nemotron 3 Ultra provides five to six times greater throughput and roughly 30% lower task costs compared to its counterparts.

NVIDIA's Strategy: The Ecosystem as the Main Asset

The main value of this release is not the model itself, but 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.

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 costs, so NVIDIA can give the model away for free and still earn more than its closed competitors do with 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 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 in light of recent restrictions surrounding closed models.

Weaknesses and Prospects

For 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 trailing behind leaders like Kimi K2.6 (54 points) and DeepSeek. According to analysts, open models lag behind closed ones by three to seven months.

However, this gap 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 confidential data, kept within its secure perimeter, and not share sensitive information with outsiders.

My analysis: NVIDIA's bet on efficiency, rather than test records, may prove more far-sighted than it seems. With mass AI adoption, the cost of running a model comes to the forefront, and one that is almost as capable but five times cheaper wins in real-world deployment. I 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.