NVIDIA is giving away powerful AI for free: a strategy that generates billions
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open AI model in the Nemotron 3 line. The model weights, training data, and training methodologies were released to the public under a free license. At first glance, this is a generous gesture. But behind it lies a cold market calculation that allows the company to earn more than any closed competitor.
Nemotron 3 Ultra is not just an "upscaled transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and a Latent Mixture of Experts (Latent MoE). Mamba-2 processes long texts quickly and efficiently: costs grow linearly, not exponentially as in the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to experts, each of which works 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. Thanks to this, it thinks like a massive system but behaves 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 provides five to six times greater throughput and roughly 30% lower task costs compared to analogs.
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. 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 do 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 a nearly 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 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 to countries building independent national AI, from Europe to Southeast Asia. No one can remotely disable such a model, 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 ranking, 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 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 secure perimeter, and not share confidential information with outsiders.
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. 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.
Analyst's Opinion: NVIDIA's strategy resembles the classic "razor and blades" model—give away the razors for free to make money on the blades. Only here, the "blades" are the graphics cards and software that anyone who decides to run an open model will need. Given the company's scale, this approach could not only strengthen its dominance but also fundamentally change the rules of the game in the AI market, where openness will become not just an alternative, but the primary driver of growth.