News
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Check out how the AI community is discussing STORM! From technical podcasts to detailed audio summaries, these resources offer a variety of ways to learn about our latest research in efficient video-LLMs. You can find the full list of videos here.
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1 paper accepted to CVPR 2026.
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3 papers accepted to NeurIPS 2025.
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We recently introduced Nemotron Nano V2, a 9B hybrid model that delivers competitive or superior accuracy on reasoning benchmarks while achieving up to 6× higher inference throughput in reasoning tasks (e.g., 8k input and 16k output tokens). The model builds on our Mamba-based Hybrid LLM work.
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Our token-efficient long video model for multimodal LLMs (STORM) is on arXiv. It achieves more than 5% improvement on MLVU and LongVideoBench compared to SOTA while reducing the computation costs by up to 8× and the decoding latency by 2.4-2.9×. Check the project page for more details!
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We intoduce efficient 2D parallel sequence modeling for image classifcaiton and generation. The paper is accepted to CVPR 2025!
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Another Mamba-Transformer Hybrid LLM is on arxiv! Check the blog post. The paper is accepted as a Spotlight to ICLR 2025!
Research Interests
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Recurrent Neural Network (RNN), State-Space Models (SSM), Linear RNNs
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Sequence Learning, Spatio-Temporal Learning
Selected Projects
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Co-authored with many colleagues at NVIDIA (incl. W. Byeon), “NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model”, arXiv, 2025
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Co-authored with many colleagues at NVIDIA (incl. W. Byeon), “Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models”, arXiv, 2025
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J Jiang, X Li, Z Liu, M Li, G Chen, Z Li, D Huang, G Liu, Z Yu, K Keutzer, S Ahn, J Kautz, H Yin, Y Lu, S Han, W Byeon, “Token-Efficient Long Video Understanding for Multimodal LLMs”, arXiv, 2025
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H Wang, W Byeon, J Xu, J Gu, KC Cheung, X Wang, K Han, J Kautz, S Liu, “Parallel Sequence Modeling via Generalized Spatial Propagation Network”, CVPR, 2025
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X Dong, Y Fu, S Diao, W Byeon, Z Chen, A S Mahabaleshwarkar, S Liu, M Keirsbilck, M Chen, Y Suhara, Y Lin, J Kautz, P Molchanov, “Hymba: A Hybrid-head Architecture for Small Language Models”, ICLR, 2025