NVIDIA gives away an AI model for free but makes more money on hardware than anyone else: breakdown of Nemotron 3 Ultra
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open AI model in the Nemotron 3 lineup. The company published its weights, training data, and methodologies under a permissive license. The model is designed for long-lived autonomous agents and complex reasoning.
Unlike closed flagship models such as 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 full control over the model.
Architecture: A Hybrid of Three Approaches
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 latter mechanism directs each request only to the necessary "specialists" within the model, saving resources.
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly with length, rather than explosively 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 the experts, forcing each of them to work narrowly and precisely, without unnecessary 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 context window of 1 million tokens and a speed exceeding 300 tokens per second, this results in five to six times greater throughput and approximately 30% lower task costs.
NVIDIA's Strategy: The Ecosystem as the Main Asset
The main value of the release, according to industry analysts, 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 GPUs, 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 a nearly negligible expense for the company. GPU 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 adds further weight to the release. An open American model can be inspected, modified, and run on one's own servers—this has made it attractive for countries building independent national AI, from Europe to Southeast Asia. No one can remotely disable such a model, and this is especially valuable in light of recent restrictions surrounding closed models.
Where the Model Falls Short and What's Next
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 globally it trails 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 for loan processing on its own servers doesn't need flagship-level intelligence—it needs a model that can be fine-tuned on private data, kept within its own secure perimeter, and not expose confidential information to outsiders.
Expert Commentary from Cryptalist: 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 nearly as smart but five times cheaper wins in real-world operation. I 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. For the crypto industry, where decentralization and data sovereignty are key values, this is a signal: the infrastructure for truly open AI is becoming more accessible than ever.