This source introduces SmolVLM, a collection of small-scale multimodal models designed for efficiency on devices with limited computing power. The authors experiment with different architectural choices, image processing techniques, and training data strategies to create models that perform well on image and video tasks while using significantly less memory than larger models. They demonstrate that smaller vision encoders are more suitable for compact language models, extending the context window improves performance, and more aggressive visual token compression is beneficial for these models. The paper also discusses the importance of structured prompts and avoiding certain data reuse for optimal performance and shows that moderately increasing video duration during training enhances both image and video understanding.