vllm-omni/docs/design/architecture_overview.md

# Architecture Overview

This document outlines the architectural design for vLLM-Omni.

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    <img alt="Omni-Modality Model Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
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# Goals

The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support:

* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
* **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
* **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
* **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.


# Representative omni-modality models

According to analysis for current popular open-source models, most of them have the combination of AR+DiT. Specifically, they can be further categorized into 3 types below:

**DiT as a main structure, with AR as text encoder (e.g.: Qwen-Image)**
  A powerful image generation foundation model capable of complex text rendering and precise image editing.

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    <img alt="Qwen-Image" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/dit-main-architecture.png" width=30%>
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**AR as a main structure, with DiT as multi-modal generator (e.g. BAGEL)**
  A unified multimodal comprehension and generation model, with cot text output and visual generation.

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    <img alt="Bagel" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-main-architecture.png" width=30%>
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**AR+DiT (e.g. Qwen-Omni)**
  A natively end-to-end omni-modal LLM for multimodal inputs (text/image/audio/video...) and outputs (text/audio...).

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    <img alt="Qwen-Omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/ar-dit-main-architecture.png" width=30%>
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# vLLM-Omni main architecture

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    <img alt="vLLM-Omni Main Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/vllm-omni-main-architecture.png" width=55%>
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## Key Components

| Component | Description |
|-----------|-------------|
| **OmniRouter** | provide an intelligent router for Omni-modality requests dispatch |
| **EntryPoints** | define the APIs for offline/online serving (APIServer, Omni/AsyncOmni) and provide the OmniStage abstraction for different AR/DiT stages |
| **AR** | adapted for omni-modality models while inheriting efficient features from vLLM, such as cache management |
| **Diffusion** | natively implemented and optimized using acceleration components |
| **OmniConnector** | supports fully disaggregation based on E/P/D/G (Encoding/Processing/Decoding/Generation) disaggregation across stages |

Disaggregated stages are managed through configuration, such as in the Qwen3-Omni example, where stages like Thinker, Talker, and Code2wav are defined as separate OmniStage instances with specific resources and input/output type.

## Main features

vLLM-Omni aims to be fast, flexible, and easy to use with the following features:

### Performance and Acceleration

The framework achieves high performance through several optimization techniques:

* **Efficient AR Support:** Leverages efficient KV cache management inherited from vLLM.
* **Pipelined Execution:** Uses pipelined stage execution overlapping to ensure high throughput.
* **Full Disaggregation:** Relies on the OmniConnector and dynamic resource allocation across stages.
* **Diffusion Acceleration:** Includes integrated support for diffusion acceleration. This is managed by the acceleration layer, which handles:
    * **Cache:** Includes DBCache, TeaCache and third-party integration(e.g., [cache-dit](https://github.com/vipshop/cache-dit)).
    * **Parallelism:** Supports TP, CP, USP, and CFG.
    * **Attention:** Provides an interface for third-party integration (e.g., FA3, SAGE, MindIE-SD).
    * **Quantization:** Supports various quantization implementations including FP8 and AWQ.
    * **FusedOps:** Allows for custom and third-party integration.

### Flexibility and Usability

vLLM-Omni is designed to be flexible and straightforward for users:

* **Heterogeneous Pipeline Abstraction:** Manages complex model workflows effectively.
* **Hugging Face Integration:** Offers seamless integration with popular Hugging Face models.
* **Distributed Inference:** Supports tensor, pipeline, data, and expert parallelism.
* **Streaming Outputs:** Supports streaming outputs.
* **Unified API:** Provides a consistent and unified API interface compatible with vLLM.
* **OpenAI-compatible API Server:** Includes a FastAPI-based server for online serving that is compatible with the OpenAI API.

# Interface design

If you use vLLM, then you know how to use vLLM-Omni from Day 0:

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Taking **Qwen3-Omni** as an example:

## Offline Inference
The **Omni** class provides a Python interface for offline batched inference. Users initialize the Omni class with a Hugging Face model name and use the generate method, passing inputs that include both text prompts and multi-modal data:

```
# Create an omni_lm with HF model name.
from vllm_omni.entrypoints.omni import Omni

omni_lm = Omni(model="Qwen/Qwen3-Omni-30B-A3B-Instruct")

# Example prompts.
om_inputs = {"prompt": prompt,
             "multi_modal_data": {
                 "video": video_frames,
                 "audio": audio_signal,
             }}

# Generate texts and audio from the multi-modality inputs.
outputs = omni_lm.generate(om_inputs, sampling_params_list)
```

## Online Serving
Similar to vLLM, vLLM-Omni also provides a FastAPI-based server for online serving. Users can launch the server using the vllm serve command with the `--omni` flag:

```
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
```

Users can send requests to the server using curl:

```
# prepare user content
user_content='[
        {
          "type": "video_url",
          "video_url": {
            "url": "'"$SAMPLE_VIDEO_URL"'"
          }
        },
        {
          "type": "text",
          "text": "Why is this video funny?"
        }
      ]'
    sampling_params_list='[
      '"$thinker_sampling_params"',
      '"$talker_sampling_params"',
      '"$code2wav_sampling_params"'
    ]'
    mm_processor_kwargs="{}"

# send the request
curl -sS -X POST http://localhost:8091/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d @- <<EOF
{
  "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
  "sampling_params_list": $sampling_params_list,
  "mm_processor_kwargs": $mm_processor_kwargs,
  "messages": [
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."
        }
      ]
    },
    {
      "role": "user",
      "content": $user_content
    }
  ]
}
```

For more usages, please refer to [examples](../user_guide/examples/).