SmolVLM introduces a series of compact multimodal models specifically designed for efficient deployment on mobile and edge devices with limited computational resources.

Key innovations:

• Optimized architecture for resource-efficient inference without sacrificing performance
• Reduced image tokenization to improve GPU memory usage efficiency
• Tailored design for practical on-device applications rather than mimicking large model architectures
• Engineering focus on balancing model capability with deployment constraints

This research enables broader adoption of vision-language capabilities in resource-constrained environments, making advanced AI more accessible and practical for real-world applications.

SmolVLM: Redefining small and efficient multimodal models