NVIDIA is giving away AI for free: how the open-source Nemotron 3 Ultra model is turning into a goldmine
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open AI model in the Nemotron 3 lineup. The model weights, training data, and training methodologies were released to the public under a permissive license. The model is designed for long-lived autonomous agents and complex reasoning.
Unlike closed flagship models like ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on proprietary data, and run on your own infrastructure. The focus here is not on maximum intelligence, but on openness, efficiency, and control over the model.
Architecture: Three in One
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture combining three different approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE). The Latent MoE mechanism directs each query only to the necessary "specialists" within the model, drastically reducing computational costs.
Mamba-2 layers process long texts quickly and efficiently: costs grow linearly with length, rather than exponentially as with standard attention. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to the experts, allowing each expert to work narrowly and precisely without requiring additional computation.
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 much more compact one in terms of cost. Combined with a 1 million token context window and speeds exceeding 300 tokens per second, this results in five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's Strategy: Ecosystem Over Charity
The main value of the release, according to industry analysts, lies not in the model itself, but in 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 NVIDIA's software tools, and deploying it on NVIDIA's software stack. Openness here is not charity, but a way to steer developers back towards purchasing the company's hardware.
NVIDIA can afford this because its financial resources are incomparable to the model's costs. 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. GPU sales more than cover the research, allowing NVIDIA to give the model away for free and still earn more than closed competitors charge for paid access.
The political context adds further weight to the release. An open American model can be inspected, modified, and run on private servers—making it attractive for countries building independent national AI, from Europe to Southeast Asia. No one can remotely disable such a model, which is especially valuable in light of recent restrictions surrounding closed models.
Weaknesses and Prospects
Despite its strengths, 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 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.
However, in my opinion, this gap 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 its secure perimeter, and not expose confidential information to third parties.
NVIDIA's bet on efficiency rather than benchmark records may prove more farsighted. In mass AI adoption, the operational cost of a model takes center stage, and one that is nearly as capable but five times cheaper wins in real-world deployment. Analysts expect the open ecosystem to only strengthen: NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open models faster than any other company.
My conclusion: NVIDIA isn't just giving away AI—it's building a closed ecosystem where the open model serves as bait to sell "shovels" (GPUs). In the long term, this could make it a dominant player not only in hardware but also in software, leaving competitors behind.