NVIDIA is giving away powerful AI for free: a strategy that brings in billions
On June 4, 2026, NVIDIA released its largest AI model to the public — Nemotron 3 Ultra. Under a free license, the weights, training data, and training methodologies were published. This is not just a goodwill gesture, but a calculated business move that allows the giant to earn more than its closed competitors.
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.
Architecture: A Hybrid That Changes the Game
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE).
Mamba-2 layers process long texts quickly and efficiently: costs grow linearly, not exponentially. Attention layers 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 computations.
The model has approximately 550 billion parameters, but only 55 billion are activated for processing each token. Combined with a context window of 1 million tokens and a speed exceeding 300 tokens per second, this provides 5–6 times greater throughput and roughly 30% lower task costs.
Strategy: The Ecosystem as the Main Asset
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 tools, and deploys it on its software. Openness here is not charity, but a way to bring developers back to purchasing hardware.
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. Sales of graphics cards more than cover the research, so NVIDIA can give away the model for free and still earn more than closed competitors with paid access.
The political context also adds weight: an open American model can be inspected, modified, and run on your own servers. This makes it attractive for countries building independent national AI — from Europe to Southeast Asia. Such a model cannot be remotely disabled, which is especially valuable in light of recent restrictions around closed models.
Weaknesses and Prospects
Despite all its advantages, Nemotron 3 Ultra is not the smartest model on the market. In the Artificial Analysis Intelligence Index ranking, it scored 48 points — the best result among open US models, but trailing leaders like Kimi K2.6 (54 points) and DeepSeek. Open models lag behind 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 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 a secure perimeter, and not share confidential information with outsiders.
My opinion: NVIDIA's bet on efficiency rather than test records may prove more farsighted. In mass AI adoption, the cost of running a model comes to the forefront. A model 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.