Multimodal Large Language Models: Architectures, Training, and Real-World Applications
Last Updated on February 9, 2026 by Editorial Team
Author(s): Hamza Boulahia
Originally published on Towards AI.
A breakdown of main architectures, training pipeline stages, and where current models actually work
With the rise of AI Agents in these last few years, we reached what we could describe as an inflection point, where models can no longer afford to be confined to a single modality.
This article explores the emergence of Multimodal Large Language Models (MLLMs), which integrate multiple forms of data such as text, images, and audio for more effective AI interaction. It discusses their architectures, training methodologies, and real-world applications, emphasizing the need for models that reflect human-like understanding. The challenges of combining different modalities and the distinctions between various architecture approaches are explored, alongside practical use cases including document interpretation, visual question answering, and agent functionality within user interfaces.
Read the full blog for free on Medium.
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