NVIDIA gives away powerful AI for free — and earns more than its competitors from it
On June 4, 2026, NVIDIA released its largest AI model to the public — Nemotron 3 Ultra. The weights, training data, and training methodologies were published under a free license. This is not just a "gift" to the community; it is a strategic move that allows NVIDIA to earn more than its competitors, even by giving the product away for free.
Nemotron 3 Ultra is not just another "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 layers process long texts quickly and efficiently: costs grow linearly, not exponentially like with a standard attention mechanism. The attention layers, in turn, accurately retain large volumes of text in memory. Meanwhile, Latent MoE compresses data before passing it to experts, allowing each expert to work narrowly and precisely without requiring unnecessary computations.
The model has approximately 550 billion parameters in total, but only about 55 billion are activated for processing each token. This makes it a massive system in terms of capability, yet compact in terms of cost. A context window of 1 million tokens and a speed exceeding 300 tokens per second provide five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Betting on the Ecosystem
The main value of the release is not the model itself, but the ecosystem that NVIDIA is building around its hardware. Anyone running 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 steer developers back toward 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 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 charging for access.
The political context adds additional 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 disable 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 globally it trails leaders like Kimi K2.6 (54 points) and DeepSeek. According to analysts, open models lag behind closed ones by three to seven months.
But 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 private data, kept within its own secure perimeter, and not expose confidential information to outsiders.
My analysis: NVIDIA's bet on efficiency, rather than test records, may prove more forward-thinking. With mass AI adoption, the operational cost of a model comes to the forefront — and one that is nearly as smart but five times cheaper wins in real-world deployment. NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company. This is not just "free cheese" — it is a trap for competitors, disguised as a gift.