Free AI from NVIDIA: How the company gives away models but earns more than anyone else
On June 4, 2026, NVIDIA released its largest open-source model to the public — Nemotron 3 Ultra. Under a free license, the company published the weights, training data, and methodologies. This is not just a goodwill gesture, but a well-thought-out strategy that brings the chip manufacturer more profit than closed competitors earn from paid subscriptions.
Unlike 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 control. This is a model for long-running autonomous agents and complex reasoning, not for setting records in benchmarks.
Architecture: Three in One
Nemotron 3 Ultra is a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE). Mamba-2 processes long texts quickly and efficiently — costs grow linearly, not exponentially like with standard attention. Attention layers, in turn, accurately retain large volumes of text in memory. And Latent MoE compresses data before passing it to experts, forcing each one to work narrowly and precisely, without unnecessary computations.
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 cost-wise like a much more compact one. Combined with a context window of 1 million tokens and a speed exceeding 300 tokens per second, this results in five to six times greater throughput and roughly 30% lower task costs compared to alternatives.
Strategy: Not a Model, but an 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 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 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 charge for paid access.
The political context adds additional 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. 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 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. According to analysts, open models trail closed ones by three to seven months.
But this gap 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 proprietary data, kept within its secure perimeter, and not expose confidential information to outsiders.
My view: NVIDIA's bet on efficiency rather than benchmark records may prove to be more forward-thinking. 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. The company has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company. The market has yet to fully realize this.