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On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open-source AI model in the Nemotron line. Not only the model weights, but also the training data and training methodologies were released to the public under a free license. This is not just another "transformer." It is a full-fledged hybrid architecture designed for long-lived autonomous agents and complex reasoning.

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 complete control over the model. This is a fundamentally different approach that is already reshaping market dynamics.

An Architecture That Breaks the Mold

At the core of Nemotron 3 Ultra is a hybrid architecture consisting of three components: Mamba-2 layers, Attention layers, and Latent MoE (Mixture of Experts). Mamba-2 processes long texts quickly and efficiently: its costs grow linearly, not exponentially like a standard attention mechanism. Attention layers, in turn, reliably retain large volumes of text in memory. And Latent MoE compresses data before passing it to the experts, forcing each one to work narrowly and precisely, without unnecessary computational costs.

The result is impressive: with 550 billion parameters, only about 55 billion are activated for processing each token. The model thinks like a giant but behaves like a compact system in terms of cost. A context window of 1 million tokens and a speed of over 300 tokens per second provide 5-6 times greater throughput and roughly 30% lower task costs compared to alternatives.

NVIDIA's Strategy: Free AI as a Sales Magnet

The main value of the 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.

NVIDIA can afford this. With a market capitalization exceeding $5 trillion, training Nemotron 3 Ultra, which likely cost hundreds of millions of dollars, is almost a negligible expense for the company. Graphics card sales more than cover the research. Therefore, NVIDIA can give away the model for free and still earn more than closed competitors charge for paid access.

The political context adds weight. 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. Such a model cannot be remotely disabled, and this is especially valuable in light of recent restrictions surrounding closed models.

Where the Weakness Lies and What's Next

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 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 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 expose confidential information to 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. One that is almost as smart but five times cheaper wins in real-world operation. The open ecosystem will only strengthen: NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company.