Overview
vLLM-Omni is an extension of the vLLM ecosystem focused on serving and inference for omni-modality (multi-modal) models. While vLLM originally targeted large autoregressive text models, vLLM-Omni broadens that capability to support images, video, and audio, plus non-autoregressive generation architectures such as diffusion transformers.
Key features
- Omni-modality support: processes text, images, video and audio within the same serving framework.
- Multi-architecture support: handles autoregressive and non-autoregressive models (e.g., DiT, diffusion-like models) and heterogeneous outputs (text, images, multimodal responses).
- Performance optimizations: inherits vLLM's efficient KV cache management for AR models, implements pipelined stage execution to increase throughput, and supports disaggregated execution with dynamic resource allocation.
- Flexible pipeline abstraction: provides heterogeneous pipeline primitives to compose complex model workflows and to integrate multiple stages (pre/post-processing, model stages, decoders).
- Integration with Hugging Face: seamless support for many open-source models available on Hugging Face, including omni models such as Qwen-Omni and Qwen-Image.
- Scalability & parallelism: supports tensor/pipeline/data/expert parallelism for distributed inference.
- Developer ergonomics & APIs: streaming outputs, OpenAI-compatible API server, and documentation/quickstart guides for easy adoption.
- Open license: distributed under the Apache License 2.0.
Typical uses
- Deploying multi-modal foundation models for production inference (e.g., vision+language assistants).
- Serving diffusion/parallel-generation models with high throughput and lower latency.
- Building pipelines that combine multiple model types or modalities and need coordinated resource allocation.
Who it's for
- MLOps and infra engineers who need a performant, production-ready inference stack for multi-modal models.
- Researchers and developers who want to prototype or serve multi-modal models with compatibility to Hugging Face and OpenAI-style APIs.
Quick links & ecosystem
- Documentation / Quickstart: the project provides hosted docs and guides for installation, supported models, and contribution.
- Community: a vLLM user forum and developer Slack are available for support and discussion.
License
vLLM-Omni is released under the Apache License 2.0.
