NVIDIA is giving away powerful AI for free — and making more money from it than competitors do with paid subscriptions
On June 4, 2026, NVIDIA released its largest open AI model — Nemotron 3 Ultra. This is not just another language model, but a strategic tool that overturns the conventional business logic of the industry.
Unlike closed flagships such as ChatGPT or Claude, Nemotron 3 Ultra is available for download, fine-tuning on proprietary data, and deployment on local infrastructure. The bet here is not on maximum intelligence, but on openness, efficiency, and full control over the model.
Architecture of the Future: Hybrid Power
Nemotron 3 Ultra is not just an "upsized transformer." It is based on a hybrid architecture that combines three approaches: Mamba-2 layers, Attention layers, and Latent Mixture of Experts (Latent MoE).
Mamba-2 layers process long texts quickly and efficiently: their costs grow linearly, not exponentially like the standard attention mechanism. Attention layers, in turn, accurately retain large amounts of context in memory. Meanwhile, Latent MoE compresses data before passing it to the "experts," allowing each of them to work narrowly and precisely without unnecessary computations.
The result: the model has approximately 550 billion parameters, but only about 55 billion are activated for processing each token. With a context window of 1 million tokens and a speed exceeding 300 tokens per second, this provides five to six times greater throughput and roughly 30% lower task costs compared to alternatives.
NVIDIA's Strategy: The "Pick and Shovel" Turns into a "Gold Mine"
The main value of the release 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 own 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 away the model for free and still earn more than closed competitors charging for access.
The release gains additional weight from the political context. 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.
Not the Smartest, but the Most Efficient
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 lags behind leaders such as Kimi K2.6 (54 points) and DeepSeek. According to analysts, open models trail closed ones by three to seven months.
But this gap 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 does not 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 outsiders.
Analyst's Perspective
NVIDIA's bet on efficiency rather than benchmark 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 nearly as capable but five times cheaper wins in real-world deployment. 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.