NVIDIA is giving away powerful AI for free — and making more money than its competitors from it
On June 4, 2026, NVIDIA released Nemotron 3 Ultra, the largest open AI model in the Nemotron 3 line. The company made the model weights, training data, and training methodologies publicly available under a free license. The model is designed for long-running autonomous agents and complex reasoning.
Unlike closed flagship models such as ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on proprietary data, and deployed on your own infrastructure. The focus here is not on maximum intelligence, but on openness, efficiency, and control over the model.
What makes the model architecture special
Nemotron 3 Ultra is not just a "scaled-up transformer." It is based on a hybrid architecture consisting of three different approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE) — a mechanism that routes each request only to the relevant "specialists" within the model.
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly with length, rather than exponentially like standard attention mechanisms. Attention layers, in turn, accurately retain large volumes of text in memory. Latent MoE compresses data before passing it to the experts, allowing each 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 a speed exceeding 300 tokens per second, this results in five to six times greater throughput and roughly 30% lower task costs.
NVIDIA's strategy and bet on the ecosystem
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 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 private servers — making 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 amid recent restrictions surrounding closed models.
Where the model falls short and what comes next
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.
But this gap, 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 secure perimeter, and not share confidential information with outsiders.
NVIDIA's bet on efficiency rather than test records may prove more farsighted. In mass AI adoption, the cost of running 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 view: This move by NVIDIA is not just a goodwill gesture, but a calculated strategy to capture the enterprise segment. While competitors compete on benchmarks, NVIDIA is securing the infrastructure on which all future enterprise solutions will be built. And that is far more valuable than temporary leadership in benchmarks.