NVIDIA gives away powerful AI for free: how the giant earns more from an open model than competitors do from a paid one
On June 4, 2026, NVIDIA released its largest AI model to the public — Nemotron 3 Ultra. Unlike closed giants like ChatGPT or Claude, this model is distributed under a free license: weights, training data, and methodologies are available to everyone. But don't rush to think this is charity. It is a cold-blooded calculation.
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, an attention mechanism, and a latent mixture of experts (Latent MoE). Mamba-2 processes long texts quickly and efficiently — costs grow linearly, not exponentially. Attention layers retain large amounts of data in memory, while Latent MoE compresses information before passing it to experts, forcing each to work narrowly and precisely.
The result: approximately 550 billion parameters, but only 55 billion are activated for processing each token. The context window is 1 million tokens, with a speed of over 300 tokens per second. This provides five to six times greater throughput and roughly 30% lower task costs compared to alternatives.
NVIDIA's Strategy: Openness as a Weapon
The main value of the release is not the model itself, but 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 tools, and deploys it on its software. Openness here is not altruism, but a way to bring developers back to purchasing hardware.
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 research costs, so NVIDIA can give away the model for free and still earn more than closed competitors with paid access.
The release gains additional weight from the political context. 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 amid recent restrictions surrounding 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 trailing leaders like Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, lag behind closed ones by three to seven months.
But this lag 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 closed data, kept within a secure perimeter, and not hand over confidential information to 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. One that is almost as smart but five times cheaper wins in real-world operation. And 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.