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On June 4, 2026, NVIDIA released its largest AI model to date, Nemotron 3 Ultra, to the public. Unlike closed solutions such as ChatGPT or Claude, this model is distributed under a free license: weights, training data, and methodologies are available for download, fine-tuning, and deployment on your own infrastructure.

NVIDIA's strategy here is clear: not to chase maximum intelligence, but to bet on openness, efficiency, and control. Nemotron 3 Ultra was not created to break benchmark records, but for real, long-lived autonomous agents and complex reasoning.

Architectural Breakthrough: Mamba-2, Attention, and Latent MoE

At the core of Nemotron 3 Ultra is a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and a Latent Mixture of Experts (Latent MoE). Mamba-2 layers efficiently process long texts with linear cost growth, while Attention accurately retains large amounts of data in memory. The key innovation is Latent MoE, which compresses data before passing it to experts, allowing each to work narrowly and precisely without unnecessary computational costs.

The model has approximately 550 billion parameters, but only about 55 billion are activated when processing each token. This allows it to think like a huge system but behave like a more compact one in terms of cost. A context window of 1 million tokens and a speed of over 300 tokens per second provide five to six times greater throughput and roughly 30% lower task costs.

Ecosystem as the Main Asset

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.

With a market capitalization exceeding $5 trillion, the training costs for Nemotron 3 Ultra, estimated at hundreds of millions of dollars, are a nearly negligible expense for NVIDIA. Graphics card sales more than cover the research, so the company can give the model away for free and still earn more than closed competitors charge for paid access.

The political context also adds weight to the release. An open American model can be inspected, modified, and run on your own servers—this makes 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.

Limitations and Prospects

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 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 an open model is simply sufficient for real-world tasks. A bank deploying Nemotron 3 Ultra for loan processing on its own servers does not need flagship-level intelligence—it needs a model that can be fine-tuned on private data, kept within a secure perimeter, and not give away confidential information to outsiders.

Analytical conclusion: NVIDIA's bet on efficiency rather than test records may prove more far-sighted. With 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. 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. For the crypto industry, where decentralization and data control are key values, this trend could be defining.