Introduction
“An incredible model, completely open source.” Jensen Huang did not bury the lede. He laid out a simple promise, then backed it with a working stack you can touch today. If you care about building reliable, fast agentic AI, the new Nemotron family is a serious invitation, not a press release.
This piece does three things. First, it explains the strategic “two factories” idea and why open source sits at the center. Second, it walks through the Nemotron lineup with a focus on Nemotron Nano 2 VL for documents and vision. Third, it gives you a practical, copy-paste path to run models locally or as NVIDIA NIM microservices with NVIDIA AI Enterprise. You’ll also find a concise benchmarks and pricing section, plus a compact playbook for production.
Table of Contents
1. Jensen’s Vision, Two Factories And A Flywheel
Jensen’s analogy is direct. Every company now needs two factories, the one that makes its products and the one that makes its intelligence. The second factory is your AI factory, a pipeline that ingests data, trains or adapts models, and ships agentic systems into real workflows.
Why open matters here is practical, not ideological. Open weights, visible data recipes, and reproducible tooling let teams audit behavior, adapt quickly, and scale across their own infrastructure. That openness drives usage, which drives demand for accelerated compute. The flywheel spins because developers start with open models that run best on NVIDIA hardware and software, then graduate to supported microservices when the workload goes live.
2. Meet The Nemotron Family, A Toolkit For Specialized Agents
The Nemotron family is not a single model. It is a matched set designed for real work.
- Nemotron Nano 3: tuned for efficient reasoning on PCs and edge devices. It uses a hybrid mixture-of-experts approach that squeezes more throughput from smaller footprints.
- Nemotron Nano 2 VL: the vision and document specialist. It handles OCR-heavy tasks, charts, forms, slides, and long-context layouts across images and video frames.
- Nemotron Parse: a production-grade extractor for tables and text that turns messy PDFs into clean, typed data.
- Safety Guard and upgraded RAG components: the guardrails and retrieval plumbing you need for agentic AI that can cite, stay on topic, and refuse risky instructions.
Taken together, Nemotron is a system for building agents that can see, read, retrieve, reason, and act. You can prototype with open weights. You can deploy through NVIDIA NIM when you need stable APIs, hardened containers, and support.
3. Performance Benchmarks, Open Roots And High Throughput
NVIDIA’s recipe is pragmatic. Start from strong open reasoning backbones. Post-train with large, carefully curated datasets. Ship optimization paths that reach production speeds. That is why Nemotron shows state-of-the-art accuracy while still running efficiently through TensorRT-LLM, vLLM, and friends.
For the vision track, Nemotron Nano 2 VL is built for the pain points teams actually have, invoices, multi-page slides, scientific figures, charts, and scanned tables. You get long context, tile-aware image handling, and robust OCR-plus-reasoning performance. The result is not just captions, it is answers that reference the right region or cell.
4. Benchmarks And Pricing For Nemotron Nano 2 VL
Below is a compact snapshot pulled from the official model card for the FP8 release. It reflects single-GPU inference on H100-class hardware with vLLM serving. Scores are percentages.