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NVIDIA released Nemotron 3 Content Safety, a 4B multimodal and multilingual content moderation model built on Gemma-3 4B-IT and fine-tuned with LoRA. It addresses limitations of text-only, English-centric safety models by supporting 140+ languages and processing text-image pairs jointly. The model was trained on a blend of human-annotated multilingual data, multimodal safety data, and ~10% synthetic data covering 12 languages. It achieves 84% average accuracy on multimodal harmful-content benchmarks, outperforms comparable open safety models, and offers roughly half the latency of larger alternatives. Two inference modes are supported: a fast safe/unsafe classification and a category-rich output listing violated safety categories. The model is available on Hugging Face and will be offered as an NVIDIA NIM microservice in April.

Nemotron 3 Content Safety 4B: Multimodal, Multilingual Content Moderation

Why is multimodal content safety important?