NVIDIA is giving away powerful AI for free: The secret strategy that generates billions
On June 4, 2026, NVIDIA released its largest open-source AI model — Nemotron 3 Ultra, making not only the model weights publicly available but also the training data and training methodologies. This is not just another release; it is a strategic move that upends the conventional business model of the artificial intelligence industry.
Unlike closed giants such as ChatGPT and 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.
The model's architecture is unique. It is a hybrid of 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 the standard attention mechanism. Attention layers, in turn, accurately retain large volumes of text in memory. And Latent MoE compresses data before passing it to experts, allowing each to work narrowly and precisely without unnecessary computation.
With a total volume of approximately 550 billion parameters, only about 55 billion are activated for processing each token. This allows the model 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.
The Ecosystem as the Main Asset
The main value of the release, in my assessment, lies not in the model itself, but in 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 an almost 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 charge for paid access.
The political context also adds weight to the release. An open American model can be inspected, modified, and run on your own servers — this has made it attractive for 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.
Results and Prospects
For all its merits, 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 trailing leaders such as Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, lag behind closed ones by three to seven months.
But this lag, 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 does not need flagship-level intelligence — it needs a model that can be fine-tuned on closed data, kept within its own secure perimeter, and not share confidential information with outsiders.
My expert opinion: NVIDIA's bet on efficiency, rather than on test records, may prove more far-sighted than all others. 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. NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company. The market has not yet fully realized this, but it soon will.