VideoLLaMA3, developed by the DAMO-NLP-SG team, is a state-of-the-art multimodal foundational model specializing in image and video understanding. Based on the Qwen2.5 architecture, it integrates advanced visual encoders (such as SigLip) with powerful language generation capabilities to address complex visual and language tasks. Key advantages include efficient spatiotemporal modeling, strong multimodal fusion capabilities, and optimized training on large-scale datasets. This model is suitable for applications requiring deep video understanding, such as video content analysis and visual question answering, demonstrating significant potential for both research and commercial use.