Descriptions:
NVIDIA’s Nemotron 3 Nano 30B — a hybrid mixture-of-experts model with 3 billion active parameters, a 1-million-token context window, 4x throughput over its predecessor, and 60% fewer reasoning tokens — gets a practical first-look on the All About AI channel. Rather than running canned benchmarks, the creator builds real applications using OpenCode, an open-source alternative to Claude Code, with Nemotron serving as the backend model through NVIDIA’s API.
The session covers two full build cycles: a command-line Python script that generates images via the Fal AI Nana Banana Pro API, and a Streamlit web UI wrapping the same workflow. Each iteration exposes the model’s tool-calling reliability and error-recovery loop, while the raw inference speed — fast enough that the creator repeatedly notes not being able to track what’s happening on screen — becomes the video’s most striking demonstration.
Nemotron 3 Nano 30B ships with fully open weights and disclosed training data sources, making it runnable locally on capable hardware despite the 30B parameter count. For developers evaluating fast, open reasoning models for agentic coding pipelines, this video offers a grounded look at real-world performance rather than curated showcase prompts.
📺 Source: All About AI · Published December 15, 2025
🏷️ Format: Hands On Build
