Recent advances in multimodal large models, particularly Vision-Language models (VLMs), have demonstrated strong capabilities in handling reasoning tasks that involve both textual and visual information. However, in low-resource languages such as Vietnamese, research in educational contexts remains limited due to the absence of suitable multimodal datasets. To address this gap, we introduce ViPPS (Vietnamese Physics Problem Solving), the first multimodal dataset designed for physics problem solving in Vietnamese. ViPPS consists of nearly 500 physics-related images (e.g., circuit diagrams, mechanics illustrations, experimental setups, and graphs) paired with more than 5,500 corresponding textual questions collected from Vietnamese educational Q&A platforms. Each entry reflects authentic student learning scenarios in secondary and high school physics. We detail the dataset creation pipeline, cleaning procedures, and provide comprehensive statistical analyses of linguistic and visual characteristics. To benchmark ViPPS, we fine-tune recent VLM baselines under a supervised multimodal setting, and report their performance across different physics domains. Results highlight both the potential of current VLMs and their limitations in solving domain-specific multimodal reasoning problems in Vietnamese. We expect ViPPS to serve as a valuable resource for advancing research on multimodal learning, physics education, and AI for low-resource languages.