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Signed-off-by: wangyu31577 <wangyu31577@hundsun.com> Signed-off-by: 齐保元 <qibaoyuan@xiaomi.com> Signed-off-by: hsliu <liuhongsheng4@huawei.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Signed-off-by: GG-li <3226868735@qq.com> Signed-off-by: Sihao Li <111170255+GG-li@users.noreply.github.com> Signed-off-by: XU Mingshi <91017482+mxuax@users.noreply.github.com> Signed-off-by: mxuax <mxuax@connect.ust.hk> Signed-off-by: Baoyuan Qi <qibaoyuan@126.com> Signed-off-by: gcanlin <canlinguosdu@gmail.com> Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Signed-off-by: wuzhongjian <wuzhongjian_yewu@cmss.chinamobile.com> Signed-off-by: dongbo910220 <1275604947@qq.com> Signed-off-by: dongbo910220 <32610838+dongbo910220@users.noreply.github.com> Signed-off-by: Jiangyun Zhu <riverclouds.zhu@qq.com> Signed-off-by: Junhong Liu <98734602+LJH-LBJ@users.noreply.github.com> Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com> Signed-off-by: baoyuan qi <qibaoyuan@126.com> Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Signed-off-by: zjy0516 <riverclouds.zhu@qq.com> Signed-off-by: Prajwal A <prajwalanagani@gmail.com> Signed-off-by: Shijin Zhang <75300765+Dovis01@users.noreply.github.com> Signed-off-by: 丁宁 <nndding@gmail.com> Signed-off-by: SHIJIN ZHANG <75300765+Dovis01@users.noreply.github.com> Signed-off-by: dingning<dingning7@xiaomi.com> Signed-off-by: dingning <dingning7@xiaomi.com> Signed-off-by: dingning <dingning@xiaomi.com> Co-authored-by: wangyu <53896905+yenuo26@users.noreply.github.com> Co-authored-by: wangyu31577 <wangyu31577@hundsun.com> Co-authored-by: Zhang Shijin <zhangshijin@xiaomi.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Sihao Li <111170255+GG-li@users.noreply.github.com> Co-authored-by: XU Mingshi <91017482+mxuax@users.noreply.github.com> Co-authored-by: Canlin Guo <canlinguosdu@gmail.com> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn> Co-authored-by: JohnJan <wuzhongjian_yewu@cmss.chinamobile.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com> Co-authored-by: dongbo910220 <32610838+dongbo910220@users.noreply.github.com> Co-authored-by: Jiangyun Zhu <riverclouds.zhu@qq.com> Co-authored-by: Junhong Liu <ljh_lbj@163.com> Co-authored-by: TJian <tunjian.tan@embeddedllm.com> Co-authored-by: shijin zhang <zsj1364226740@gmail.com> Co-authored-by: Zhou Taichang <tzhouam@connect.ust.hk> Co-authored-by: root <root@hk01dgx028.cm.cluster> Co-authored-by: Prajwal A <34590600+LawJarp-A@users.noreply.github.com> Co-authored-by: Shijin Zhang <75300765+Dovis01@users.noreply.github.com> Co-authored-by: dingning <dingning7@xiaomi.com> Co-authored-by: ning ding <nndding@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Welcome to vLLM-Omni
Easy, fast, and cheap omni-modality model serving for everyone
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About
vLLM was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
- Omni-modality: Text, image, video, and audio data processing
- Non-autoregressive Architectures: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
- Heterogeneous outputs: from traditional text generation to multimodal outputs
vLLM-Omni is fast with:
- State-of-the-art AR support by leveraging efficient KV cache management from vLLM
- Pipelined stage execution overlapping for high throughput performance
- Fully disaggregation based on OmniConnector and dynamic resource allocation across stages
vLLM-Omni is flexible and easy to use with:
- Heterogeneous pipeline abstraction to manage complex model workflows
- Seamless integration with popular Hugging Face models
- Tensor, pipeline, data and expert parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
- Omni-modality models (e.g. Qwen2.5-Omni, Qwen3-Omni)
- Multi-modality generation models (e.g. Qwen-Image)
For more information, checkout the following: