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On June 4, 2026, NVIDIA made a move that upended the perception of AI monetization. The corporation released its largest open model — Nemotron 3 Ultra, making not only the weights but also the training data and methodologies publicly available. At first glance, it's a generous gesture. In reality, it's a brilliant business strategy that allows the company to earn more than its closed competitors.

Unlike "black boxes" such as 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.

Architecture: A Hybrid That Changes the Game

Nemotron 3 Ultra is not just an "upsized transformer." It is based on a hybrid architecture combining three different approaches: Mamba-2 layers, Attention layers, and a Latent MoE (Mixture of Experts) mechanism. Mamba-2 processes long texts quickly and efficiently — costs grow linearly, not exponentially. Attention, in turn, accurately retains large volumes of context in memory. And Latent MoE compresses data before passing it to the experts, forcing each to work narrowly and precisely without unnecessary computational costs.

The result is impressive: with a total volume of around 550 billion parameters, only about 55 billion are activated to process each token. This yields five to six times higher throughput and approximately 30% lower task execution costs. A context window of 1 million tokens and a speed exceeding 300 tokens per second make the model ideal for long-running autonomous agents and complex reasoning.

Strategy: The Ecosystem as the Main Asset

The main value of the release lies not in the model itself, but in 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.

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 do with paid access.

The political context also adds 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 shut down such a model, and this is especially valuable in light of recent restrictions surrounding closed models.

Weaknesses and Prospects

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

However, this lag 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 closed data, kept within a secure perimeter, and not hand over confidential information to outsiders.

My analysis: NVIDIA's bet on efficiency rather than test record highs 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. 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. This is not charity — it's a new standard for AI monetization.