NVIDIA is giving away the most powerful AI for free: a genius strategy or a deadly trap for competitors?
On June 4, 2026, NVIDIA released its latest model, Nemotron 3 Ultra, to the public. This is the largest open AI model in the Nemotron 3 line. The company released not only the model weights under a free license, but also the training data and training methodologies. This is not just about charity—it is a subtle and extremely aggressive market move.
Unlike closed flagships 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 full control. This is a model built for long-lived autonomous agents and complex reasoning, not for winning benchmarks.
Architecture: A Hybrid That Changes the Game
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).
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly with length, 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 to work narrowly and precisely without requiring extra 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 like a much more compact one in terms of cost. Combined with a context window of 1 million tokens and a speed of over 300 tokens per second, this results in five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Ecosystem Over Altruism
The main value of the release, in my view, lies not in the model itself, but in the ecosystem that NVIDIA is building around its hardware. The logic is simple: whoever runs Nemotron is almost certainly doing so on NVIDIA graphics cards, fine-tuning it with its software tools, and deploying 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 of over $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, 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 your own servers—this has made it attractive to 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 around 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, 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, in my opinion, matters less and less if the open model is simply sufficient for real-world tasks. A bank deploying Nemotron 3 Ultra for loan processing on its own servers doesn't need flagship-level intelligence—it needs a model that can be fine-tuned on closed data, kept within its secure perimeter, and not share confidential information with outsiders.
My conclusion: 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 smart 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.