SmolVLM: Redefining small and efficient multimodal models

Andrés Marafioti, Orr Zohar, Miquel Farré, Merve noyan, Elie Bakouch, Pedro Manuel Cuenca Jiménez, Cyril Zakka, Loubna Ben allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro Von Werra, Thomas Wolf

Keywords: Vision Language Models, Large Multimodal Models, Vision Understanding, Video Understanding

TL;DR: We explore extremely efficient VLMs starting at 256M parameters, which run with less than 1GB

Abstract:

Large Vision-Language Models (VLMs) deliver exceptional performance but require significant computational resources, limiting their deployment on mobile and edge devices. Smaller VLMs typically mirror design choices of larger models, such as extensive image tokenization, leading to inefficient GPU memory usage and constrained practicality for on-device applications.

We introduce SmolVLM, a series of compact multimodal models specifically engineered for resource-efficient inference. We systematically explore architectural configurations, tokenization strategies, and data curation optimized for low computational overhead. Through this, we identify key design choices that yield substantial performance gains on both image and video tasks within minimal memory footprints.

Our smallest model, SmolVLM-256M, uses less than 1GB GPU memory during inference and outperforms the 300-times larger Idefics-80B model, despite an 18-month development gap. Our largest model, at 2.2B parameters, rivals state-of-the-art VLMs consuming twice the GPU memory. SmolVLM models extend beyond static images, demonstrating robust video comprehension capabilities.

Our results emphasize that strategic architectural optimizations, aggressive yet efficient tokenization, and carefully curated training data significantly enhance multimodal performance, facilitating practical, energy-efficient deployments at significantly smaller scales.

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Submission Number: 29