vLLM-Omni: a new approach for multimodal inference

The vLLM team has released a new paper on arXiv regarding vLLM-Omni, a system designed to serve any-to-any multimodal models. These models can jointly handle text, images, video, and audio, opening new possibilities but also new challenges in terms of inference.

Architecture and optimizations

vLLM-Omni introduces an architecture based on stage-based graph decomposition, per-stage batching, and flexible GPU resource allocation across different stages. This approach allows managing complex pipelines that combine AR LLMs, diffusion models, and encoders, overcoming the limitations of traditional paradigms.

Experimental results

The team tested vLLM-Omni with Qwen-Image-2512, achieving a Job Completion Time (JCT) reduction of up to 91.4%. The results show performance comparable to Diffusers in terms of GPU memory, but with significantly faster generation.