NVIDIA is giving away powerful AI for free: the hidden logic behind massive profits
On June 4, 2026, NVIDIA released its largest AI model, Nemotron 3 Ultra, to the public. Under a free license, not only the model weights were published, but also the training data and methodologies. This is not charity: behind the apparent generosity lies cold calculation and a unique business strategy.
Nemotron 3 Ultra is not just another "scaled-up transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, classic attention layers, and a latent mixture of experts (Latent MoE). Mamba-2 processes long texts quickly and efficiently—costs grow linearly, not exponentially like with the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. Meanwhile, Latent MoE compresses data before passing it to experts, forcing each to work narrowly and precisely without unnecessary computational costs.
The result is impressive: with a total of approximately 550 billion parameters, only about 55 billion are activated for processing each token. The model thinks like a giant system but behaves like a compact one in terms of cost. Combined with a context window of 1 million tokens and a speed exceeding 300 tokens per second, this provides five to six times greater throughput and roughly 30% lower task execution costs compared to alternatives.
NVIDIA's Strategy: An Ecosystem, Not a Model
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.
NVIDIA can afford this because its financial capabilities are incomparable to the costs of the model itself. 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 the research, so NVIDIA can give away the model 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 one's 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 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 rating, 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.
However, this gap 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 its secure perimeter, and not share confidential information with outsiders.
My expert opinion: 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 nearly as smart but five times cheaper wins in real-world operation. Analysts 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.