NVIDIA is giving away powerful AI for free: a brilliant strategy generating billions
On June 4, 2026, NVIDIA released its largest open AI model — Nemotron 3 Ultra. The model weights, training data, and methodologies were made publicly available under a free license. This is not just a step toward open-source, but a calculated market maneuver that allows NVIDIA to earn more than its closed competitors.
Unlike giants such as ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on your own data, and deployed on your own infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and full control over the model. This changes the rules of the game.
An Architecture That Hits Competitors in the Wallet
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). The latter mechanism directs each request only to the necessary "specialists" within the model, sharply reducing computational costs.
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not exponentially. Attention layers, in turn, accurately retain large volumes of context in memory. And Latent MoE compresses data before passing it to the experts, allowing each of them 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 cost-wise like a much more compact one. 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: Give Away for Free, Make Money on Hardware
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 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 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. Graphics card sales more than cover the research, so NVIDIA can give the model away for free and still earn more than closed competitors do with paid access.
The political context adds further weight to the release. An open American model can be inspected, modified, and run on your own servers — this has made it attractive for countries building independent national AI, from Europe to Southeast Asia. No one can remotely shut down such a model, and this is especially valuable against the backdrop of recent restrictions surrounding closed models.
Drawbacks and Prospects
For all its merits, 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 lags behind leaders such as Kimi K2.6 (54 points) and DeepSeek. Open models, according to analysts, trail closed ones by three to seven months.
But this lag, in my opinion, matters less and less if an 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 own secure perimeter, and not share confidential information with outsiders.
My conclusion: NVIDIA's bet on efficiency, rather than on test records, may prove more farsighted. 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.