For most people, and it’s completely understandable, Nvidia is still shorthand for GPUs and AI chips. The company’s silicon dominates the AI data center conversation and headlines.
But Nvidia’s real “moat,” and I use that word purposefully, is the combination of its silicon offerings with an increasingly deep software stack. Stated a bit differently, Nvidia has built an end-to-end AI platform that includes:
CUDA — the company’s foundational GPU programming platform
cuDNN — specialized GPU-accelerated libraries for deep learning
NeMo — a higher-level framework for training and deploying large language and multimodal models
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.
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