NVIDIA is giving away powerful AI for free: a strategy that generates billions
On June 4, 2026, NVIDIA made a bold move by releasing the Nemotron 3 Ultra model — the largest open-source AI model in the Nemotron 3 lineup. The weights, training data, and methodologies were published under a free license. This is not just a gesture of goodwill: it is a well-thought-out business strategy that allows the company to earn more than its closed competitors by giving away its product for free.
Unlike ChatGPT or Claude, Nemotron 3 Ultra can be downloaded, fine-tuned on your own data, and run on your own infrastructure. The bet is not on maximum intelligence, but on openness, efficiency, and control. NVIDIA is deliberately creating conditions where developers return to purchasing its hardware.
Nemotron 3 Ultra Architecture: A Hybrid of Innovations
The model is based on a hybrid architecture combining 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 accurately retain large amounts of context in memory. Latent MoE compresses data before passing it to experts, allowing each to work narrowly and precisely.
The model has approximately 550 billion parameters, but only about 55 billion are activated for processing each token. This gives it enormous throughput with modest computational costs. A context window of 1 million tokens and a speed of over 300 tokens per second provide 5-6 times greater performance and roughly 30% lower task execution costs compared to alternatives.
NVIDIA's Strategy: The Ecosystem as the Main Asset
The main value of the release is not the model itself, but the ecosystem that NVIDIA is building around its hardware. The logic is simple: anyone running Nemotron is almost certainly doing so on NVIDIA graphics cards, fine-tuning it with its tools, and deploying it on its software. Openness here is not charity, but a way to bring developers back to purchasing hardware.
With a market capitalization of over $5 trillion, training Nemotron 3 Ultra — likely costing hundreds of millions of dollars — is a nearly negligible expense for the company. Graphics card sales more than cover research costs, so NVIDIA can give away the model for free and still earn more than closed competitors with paid access.
The political context also adds weight to the release. An open American model can be inspected, modified, and run on your own servers — making it attractive for countries building independent national AI, from Europe to Southeast Asia. Such a model cannot be remotely disabled, which is especially valuable given recent restrictions around closed models.
Limitations and Prospects
Despite all its advantages, 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-source US models, but trailing leaders like Kimi K2.6 (54 points) and DeepSeek. Open-source models lag behind closed ones by three to seven months.
However, this lag matters less and less if the open-source 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 a secure perimeter, and not expose confidential information to outsiders.
My expert opinion: 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 deployment. I expect the open-source ecosystem to only strengthen: NVIDIA has the resources, motivation, and distribution channels to release increasingly powerful open-source models faster than any other company.