🚀 Excited to share the vLLM-Omni technical report: a unified serving runtime for omni-modality generation. 📄 Paper: 🔗 Repo: Speech assistants, visual generation, world models, and robot loops have pushed serving past a single text decode loop. The execution patterns diverge with the output: multi-stage autoregressive pipelines, iterative diffusion, and sessions that carry state from step to step. LLM servers and diffusion stacks each go deep on only one of these, so deployments fall back to stitching disjoint runtimes together. vLLM-Omni is the shared control plane for that mix. An orchestrator advances each request across stages; specialized engines run the compute; a connector carries the payloads; and the same session path keeps duplex, world-model, and robot loops on one runtime. Really grateful to the vLLM and vLLM-Omni teams for the collaboration and support. Contributions are welcome. 🙏