Multimodal vision models are advancing AI applications by integrating visual, textual, and sometimes auditory data to enable more sophisticated capabilities beyond traditional language models. These models, including vision-language models and vision-reasoning models, combine vision encoders, language models, and fusion mechanisms to connect different modalities. Prominent models like Gemma 3, Qwen 2.5 VL, Pixtral, Phi-4 Multimodal, DeepSeek Janus, and Llama 3.2, developed by companies such as Google DeepMind, Alibaba Cloud, Mistral AI, Microsoft, DeepSeek, and Meta, showcase diverse capabilities such as image captioning, scene interpretation, and multimodal reasoning. With varying parameter sizes and language support, these models are optimized for efficient deployment across cloud and on-device platforms, using serverless GPUs for cost-effective scaling and real-time processing. The deployment of these models is facilitated by platforms like Koyeb, which offers serverless GPUs for streamlined fine-tuning and inference without the need for complex infrastructure management.