Nemotron

Nemotron is the name of a series of computer programs that can understand and write text. Such programs are called language models. They work similarly to ChatGPT: you ask a question, the program answers in complete sentences. They are developed by NVIDIA, a company from California best known for making graphics and computing chips. The difference from many well-known competitors lies in how they are released: NVIDIA makes the finished models available for download, including their internal numerical values. Anyone with enough computing power can thus run Nemotron on their own computers instead of using an external service on the internet.

Why a chipmaker builds its own models

NVIDIA doesn’t make its money from language models, but from the chips they run on. That explains the unusual strategy. The more companies build their own AI systems, the more NVIDIA hardware gets purchased. From this perspective, free, well-documented models are advertising for the actual product.

For companies, this is interesting for a different reason. Many are not allowed to send their data to external providers, such as hospitals or banks. A model that runs in one’s own data center solves this problem. One is also not dependent on a provider keeping its prices stable or continuing to operate a model.

A second purpose is less obvious. Nemotron models are explicitly offered for generating training material for other AI systems. Such artificially generated example texts are called synthetic data. Anyone who wants to train their own small model but has too few real examples can have millions of them written by Nemotron.

From base model to responsive assistant

At its core, Nemotron is what’s known as a transformer model — the design that underlies virtually all of today’s language models. First, the model reads enormous amounts of text and in doing so learns only one task: predicting the next piece of a word. This is followed by a second step, in which humans evaluate which answers are helpful and which are useless. Only through this fine-tuning does a text predictor become a usable assistant.

Nemotron comes in several sizes, usually measured in billions of parameters. Parameters are the adjustable numbers inside the model, comparable to control knobs. Small variants with a few billion parameters run on a single graphics card. Large variants require entire server racks, but answer more accurately in return.

Some Nemotron models are not built from scratch, but on top of open models from other companies, such as Meta's Llama. NVIDIA retrains these and optimizes them for its own chips. That’s why names like Llama Nemotron appear. Newer versions can also “think” through several steps before answering, which helps with math and programming tasks.

Where Nemotron shows up in practice

As an ordinary user, you will rarely consciously encounter Nemotron. The models are usually invisibly embedded in other companies' products: in customer service systems, in search functions for company documents, or in software assistants for programmers. The end customer sees the provider’s name there, not the model’s.

In business news, on the other hand, Nemotron comes up regularly. It’s usually about NVIDIA’s role in the AI market: the company no longer just supplies chips, but also software and models. Analysts interpret this as an attempt to make itself indispensable — not just as a supplier, but as a platform.

A common misconception is that Nemotron is a finished chatbot like ChatGPT. That’s not the case. It is a component that developers download, adapt, and build into their own applications. Still, you can try out the models: they are hosted on platforms like Hugging Face, a kind of public repository for AI models, and can be tested there directly in the browser.