Developed a major enhancement for the modular/modular repository by integrating the Gemma3ForConditionalGeneration model into the MAX architecture, enabling advanced multimodal capabilities. Focused on deep learning and model development using Python, the work introduced multi-GPU readiness and improved input handling to support scalable inference across diverse data types. Architecture integration was refined and validated against the ChartQA benchmark, ensuring robust performance for multimodal tasks. This update addressed modular/modular#5588 and established a technical foundation for future expansion of multimodal systems. The approach emphasized maintainability and scalability, leveraging machine learning best practices to prepare the codebase for broader deployment scenarios.