Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more.

However, customization comes with a few challenges. It requires infrastructure, technical expertise, and software specific to the workflow, as well as resources such as GPUs and the ability to use them effectively. It also depends on specialized domain knowledge: What algorithm should I use? What environment should I use? How do I know whether the model actually learned?

Customization can appear daunting.

With open models such as the NVIDIA Nemotron 3 family—including Nano, Super, and Ultra—combined with platforms such as Prime Intellect Lab that offer training as a service, the complete customization loop is much more accessible. In this tutorial, you’ll use Prime Intellect Lab to customize NVIDIA Nemotron 3 Nano with hosted reinforcement learning and produce a downloadable LoRA adapter.

The local setup takes about five minutes—that’s all.