Multimodal AI models, which integrate various types of data like text, images, and audio, are advancing AI technology but require complex and resource-intensive training processes. This blog post explores how disaggregated hybrid parallelism using Ray can enhance training efficiency by applying specific parallelization strategies to different modules within a model, such as using sequence parallelism for vision encoders and tensor parallelism for language models. By implementing this approach on Ray and testing it with the Qwen-VL 32B model, the authors achieved a 1.26–1.37x improvement in throughput over traditional tensor parallelism and enabled training sequences up to 7x longer than with DeepSpeed ZeRO3. The strategy allows for better memory efficiency and avoids out-of-memory errors commonly encountered with monolithic parallelization methods, demonstrating Ray's capability to handle the demands of state-of-the-art multimodal AI models effectively. The post encourages further exploration of this method across different hardware and model architectures, inviting feedback and contributions through their GitHub repository.