Free AI Monster from NVIDIA: How Nemotron 3 Ultra Turns Openness into Gold
On June 4, 2026, NVIDIA did something that turned the AI market upside down: it released Nemotron 3 Ultra, the largest open model in the Nemotron 3 line. Weights, training data, and training methodologies—all of this was made publicly available under a free license. But don't rush to think this is just an act of charity. Behind it lies a cold-blooded business calculation that brings NVIDIA more than any paid service from its competitors.
Unlike closed flagships like 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. This is a fundamentally different approach that changes the rules of the game.
An Architecture That Hits Competitors in the Wallet
Nemotron 3 Ultra is not just an "upscaled transformer." It is based on a hybrid architecture combining three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (MoE). The MoE mechanism directs each request only to the necessary "specialists" within the model, radically boosting efficiency.
Mamba-2 layers process long texts quickly and economically: their costs grow linearly, not exponentially 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 one to work narrowly and precisely, without unnecessary computations.
In total: the model has about 550 billion parameters, but only roughly 55 billion are activated for processing each token. It thinks like a giant system but behaves cost-wise like a compact model. With a context window of 1 million tokens and a speed exceeding 300 tokens per second, this yields five to six times greater throughput and approximately 30% lower task costs compared to analogs.
NVIDIA's Strategy: Give Away the Model, Sell the Hardware
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: 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 lead developers back to purchasing the company's equipment.
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. Graphics card sales more than cover the research, so NVIDIA can give away the model for free and still earn more than closed competitors charge for paid access.
The political context adds extra 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. Such a model cannot be remotely disabled, and this is especially valuable in light of recent restrictions surrounding closed models.
Weaknesses and the Future of Open AI
For all its merits, Nemotron 3 Ultra is not the smartest model on the market. In the independent Artificial Analysis Intelligence Index rating, 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.
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 to process loans 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 hand over confidential information to outsiders.
My analysis: NVIDIA's bet on efficiency rather than test records may prove more far-sighted than it seems. 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. 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. This is not just a giveaway—it's a new business model reshaping the market.