NVILA: Efficient Frontier Visual Language Models
Authors: name: Zhijian Liu, name: Ligeng Zhu, name: Baifeng Shi, name: Zhuoyang Zhang, name: Yuming Lou, name: Shang Yang, name: Haocheng Xi, name: Shiyi Cao, name: Yuxian Gu, name: Dacheng Li, name: Xiuyu Li, name: Yunhao Fang, name: Yukang Chen, name: Cheng-Yu Hsieh, name: De-An Huang, name: An-Chieh Cheng, name: Vishwesh Nath, name: Jinyi Hu, name: Sifei Liu, name: Ranjay Krishna, name: Daguang Xu, name: Xiaolong Wang, name: Pavlo Molchanov, name: Jan Kautz, name: Hongxu Yin, name: Song Han, name: Yao Lu

Visual language models (VLMs) have made significant advances in accuracy in
recent years. However, their efficiency has received much less attention. This
paper introduces NVILA, a family of open VLMs designed to optimize both
efficiency and accuracy. Building on top of VILA, we improve its model
architecture by first scaling up the spatial and temporal resolutions, and then
compressing visual tokens. This "scale-then-compress" approach enables NVILA to
efficiently process high-resolution images and long videos. We also conduct a
systematic investigation to enhance the efficiency of NVILA throughout its
entire lifecycle, from training and fine-tuning to deployment. NVILA matches or
surpasses the accuracy of many leading open and proprietary VLMs across a wide
range of image and video benchmarks. At the same time, it reduces training
costs by 4.5X, fine-tuning memory usage by 3.4X, pre-filling latency by
1.6-2.2X, and decoding latency by 1.2-2.8X. We will soon make our code and
models available to facilitate reproducibility.