The $5 Trillion House of Cards: How Spectral Is About to Topple Nvidia

Now, the Nemotron family of open models turns raw compute into usable intelligence. This is a big deal. Nemotron 3 is the latest expression of that strategy, and it matters as much for Nvidia’s long-term AI position as any new GPU launch.

Why Nemotron Matters for Nvidia’s Stack

Nvidia likes to remind the market that frontier models do not live on hardware alone. In its recent blog on OpenAI’s GPT-5.2, the company stressed that leading models depend on “world-class accelerators, advanced networking, and a fully optimized software stack.”

Nvidia’s GB200 and Blackwell may get the glamour shots, but it’s software that makes tens of thousands of GPUs behave like a single, coherent AI supercomputer.

Nemotron sits right in that layer, between infrastructure and applications. It started as a way to seed the open-source ecosystem with strong, reasonably efficient models.

On an industry analyst call, Nvidia VP of Generative AI Software for Enterprise, Kari Briski, framed the motivation very simply.

Open models accelerate innovation because they let “researchers everywhere build on shared knowledge” and allow anyone, not just big tech, to fine-tune systems for their own domains.

In 2025, Nvidia was the top contributor of open models and datasets on Hugging Face, with roughly 650 models and 250 datasets. This point is relevant because it shows Nvidia is not just selling GPUs: it is actively seeding the open ecosystem with high-quality building blocks, which draws researchers, startups, and enterprises into its software orbit and makes Nvidia’s platform the default place where new AI work gets done.

In that sense, Nemotron is evolving into a brand that organizes those contributions into a roadmap rather than a grab bag. The Nemotron 3 announcement establishes the point where that roadmap becomes much more ambitious. Briski described it as “the most efficient family of open models with leading accuracy for building agentic AI applications.”

The flagship announcement is Nemotron 3 Nano, a roughly 30-plus-billion-parameter mixture-of-experts model with only about 3–4 billion parameters active per token. That architecture gives it the compute footprint of a “tiny” model while allowing it to compete on reasoning quality with much larger, dense systems.

Under the hood, Nemotron 3 combines three ideas that have become central to modern reasoning models.

First is a hybrid Mamba-Transformer architecture that combines attention layers with state-space sequence modeling to reduce memory and compute, especially for long context.

Second is a mixture-of-experts layout that activates only a small subset of parameters for each token.

Third is a context window that spans approximately one million tokens, enabling the model to operate across entire codebases, long technical specifications, and multi-day conversations in a single pass.

What Nemotron Means for Data Centers

Because the new scaling law is no longer just “more GPUs, bigger pre-train.” Briski notes that there are now three levers: “pretraining, post-training, and what we call long thinking.”

Long thinking means test-time compute and self-reflection, often with multiple agents collaborating.

That drives token usage, and, by extension, inference cost, through the roof.

Nemotron 3’s selling point is that it provides deeper reasoning at a much better tokens-to-accuracy ratio than previous open models.

There’s more to the story. Nvidia is releasing Nemotron 3 together with the exact reinforcement learning (RL) “gyms,” data, and libraries it used internally.

Briski emphasized that “Nvidia is the first to release open state-of-the-art…RL environments alongside our open models, libraries, and data.”

Ten initial gym environments cover topics such as competitive coding, math, and practical scheduling.

They let enterprises replicate Nvidia’s own training loop — simulate agents in realistic environments, score their behavior, and feed that back into the model.

For teams that might otherwise spend months building custom RL infrastructure, that is a significant accelerator.

On the data side, Nemotron 3 rides on what Nvidia calls a shift from “big data” to “smart and improved data.”