Ray’s Disaggregated Hybrid Parallelism Boosts Multimodal AI Training by 30%

Iris Coleman

Dec 10, 2025 01:06

Ray’s innovative disaggregated hybrid parallelism significantly enhances multimodal AI training efficiency, achieving up to 1.37x throughput improvement and overcoming memory challenges.

In a significant advancement for artificial intelligence training, Ray has introduced a disaggregated hybrid parallelism approach that accelerates the training of multimodal AI models by 30%, according to Anyscale. This development addresses the complexities and computational challenges of training models that process diverse data types such as text, images, and audio.

Challenges in Multimodal AI Training

Multimodal AI models, unlike traditional homogeneous large language models, consist of specialized modules with varying computational and memory needs. Vision-Language Models (VLMs), for example, integrate a vision encoder with a large language model (LLM). This integration results in architectural complexities, particularly when dealing with high-resolution images and long sequences. Traditional techniques like tensor parallelism and DeepSpeed ZeRO3 often fall short, resulting in inefficiencies and potential out-of-memory errors.

Ray’s Innovative Approach

Ray’s disaggregated hybrid parallelism leverages the flexibility of its universal framework, enabling tailored parallelization strategies for each module within a multimodal model. By utilizing Ray’s actor-based architecture, developers can allocate resources independently, optimizing for the unique requirements of each module. This results in a more efficient orchestration of complex workloads, as demonstrated with the Qwen-VL 32B model.

Benchmarking and Performance

In tests conducted with the Qwen-VL 32B model, Ray’s approach showed up to a 1.37x improvement in throughput compared to traditional methods. The strategy combined sequence parallelism for the vision encoder with tensor parallelism for the LLM, effectively managing memory and computational demands across different modules. This method not only improved speed but also enabled the training of sequences up to 65,000 tokens long, surpassing the capabilities of DeepSpeed ZeRO3 which encountered memory issues at 16,000 tokens.

Future Prospects

The success of Ray’s disaggregated hybrid parallelism in enhancing AI training efficiency paves the way for its application across larger GPU clusters and diverse hardware setups. Its ability to adapt to various multimodal architectures highlights its potential for broader implementation in AI development.

For those interested in exploring this innovative approach, Ray’s implementation is available for experimentation and feedback on their GitHub repository.

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