NVIDIA is giving away powerful AI for free: a strategy that generates billions
On June 4, 2026, NVIDIA released the Nemotron 3 Ultra model — the largest open-source AI model in the Nemotron 3 lineup. The company made the model weights, training data, and methodologies publicly available under a free license. This is not about flagship intelligence, but about transparency, efficiency, and full user control over the model.
Architecture: A Hybrid of Speed and Accuracy
The 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 — their costs grow linearly, not exponentially. Attention layers retain large volumes of context in memory. 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 in total, but only about 55 billion are activated for processing each token. This allows it to think like a massive system while behaving like a compact one in terms of cost. A context window of 1 million tokens and a speed exceeding 300 tokens per second provide five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Betting on the Ecosystem
The main value of the release is not the model itself, but the ecosystem 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 the 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 research costs, so NVIDIA can give the model away for free and still earn more than closed competitors charging for 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 — making 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.
Where the Model Falls Short and What Comes Next
Despite its advantages, the Nemotron 3 Ultra is not the smartest model on the market. In the independent Artificial Analysis Intelligence Index, it scored 48 points — the best result among open-source US models, but globally it trails leaders like Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, lag behind closed ones by three to seven months.
But this gap, in my view, 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 doesn't need flagship-level intelligence — it needs a model that can be fine-tuned on proprietary data, kept within a secure perimeter, and not share confidential information with outsiders.
My conclusion: NVIDIA's bet on efficiency rather than benchmark records may prove more farsighted. In mass AI adoption, the cost of running a model becomes paramount, and one that barely lags in intelligence but is five times cheaper wins in real-world deployment. I expect the open ecosystem will only strengthen: NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company.