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Nemotron Nano 12B V2 VL
Optimized for low-latency deployment, it excels in optical character recognition (OCR), chart reasoning, document comprehension, and long-form video analysis.
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Nano 12B V2 VL is a 12-billion-parameter open multimodal reasoning model engineered for vision-language inference, processing text and multi-image inputs to generate coherent natural-language responses.
Nemotron Nano 12B V2 VL is an advanced 12-billion-parameter open multimodal vision-language model developed by NVIDIA. It excels in video understanding, multi-image document reasoning, and natural language output generation. Harnessing a novel hybrid Transformer-Mamba architecture, it balances transformer-level accuracy with Mamba’s memory-efficient sequence modeling. This enables fast throughput and low-latency inference, optimized for complex tasks involving text and images, especially long documents and videos.
vs Qwen3 32B VL: Nemotron excels in OCR and video benchmarks, whereas Qwen3 prioritizes versatility across tasks. Both deliver strong performance, but Nemotron is optimized for real-time applications.
vs LLaVA-1.5: While LLaVA-1.5 is a competitive research model known for innovative multimodal instruction tuning, Nemotron Nano 12B V2 VL outperforms it in document intelligence, OCR, and extended video reasoning by incorporating dedicated vision encoders and efficient video sampling techniques.
vs Eagle 2.5: Eagle 2.5 is strong in general visual question answering, but Nemotron offers more specialized capabilities in chart reasoning, document understanding, and video comprehension.
vs InternVL 14B V2: Nemotron’s hybrid Mamba-Transformer backbone achieves greater throughput on long-context tasks, making it more suitable for real-time AI agents processing dense visual and text data.
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