NVIDIA's Strategy: Free AI as a Tool for Market Domination
On June 4, 2026, NVIDIA released its largest open AI model — Nemotron 3 Ultra. The release includes not only the model weights but also training data and methodologies, available under a permissive license. Unlike closed giants like ChatGPT or Claude, this model 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 Approach
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, attention mechanisms, and Latent Mixture of Experts (Latent MoE). Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not exponentially. Attention mechanisms, in turn, retain large volumes of text in memory. Latent MoE compresses data before passing it to experts, allowing each to work narrowly and precisely without requiring unnecessary computations.
The model has approximately 550 billion parameters, 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. A context window of 1 million tokens and a speed of over 300 tokens per second provide 5-6 times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Ecosystem as the Key to Profit
The main value of the release is not the model itself, but the ecosystem NVIDIA is building around its hardware. The logic is simple: anyone running Nemotron is almost certainly doing so on NVIDIA GPUs, fine-tuning it with its software tools, and deploying it on its software. Openness here is not charity, but a way to bring developers back to purchasing the company's hardware.
With a market capitalization exceeding $5 trillion, training Nemotron 3 Ultra, which likely cost hundreds of millions of dollars, is almost a negligible expense for the company. GPU sales more than cover research costs, so NVIDIA can give the model away for free and still earn more than closed competitors with paid access.
The political context also adds extra weight to the release. An open American model can be inspected, modified, and run on your own servers — this is especially valuable for countries building independent national AI, from Europe to Southeast Asia. Such a model cannot be remotely shut down, which is particularly relevant in light of recent restrictions around closed models.
Limitations and Prospects
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 trailing leaders like Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, lag behind closed ones by three to seven months.
However, this lag matters less and less if the open model is 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 private data, kept within a secure perimeter, and not share confidential information with outsiders.
Analytical conclusion: 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. 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.