For detailed hardware and software requirements, please refer to the [vllm-ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
The default workdir is `/workspace`, with vLLM, vLLM-Ascend and vLLM-Omni code placed in `/vllm-workspace` installed in development mode.
For other installation methods (pip installation, building from source, custom Docker builds), please refer to the [vllm-ascend installation guide](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
We are keeping [issue #997](https://github.com/vllm-project/vllm-omni/issues/997) up to date with the aligned versions of vLLM, vLLM-Ascend, and vLLM-Omni, and also outlining the Q1 roadmap there.
`vllm-ascend` offers Docker images for deployment. You can just pull the **prebuilt image** from the image repository [ascend/vllm-ascend](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and run it with bash.
Supported images as following.
| image name | Hardware | OS |
|-|-|-|
| image-tag | Atlas A2 | Ubuntu |
| image-tag-a3 | Atlas A3 | Ubuntu |
vLLM-Omni offers Docker images for Ascend NPU deployment. You can just pull the **prebuilt image** from the image repository [ascend/vllm-ascend](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and run it with bash.
Here's an example deployment command that has been verified on 6 x NPUs:
You can use this docker image to serve models the same way you would with in vLLM! To do so, make sure you overwrite the default entrypoint (`vllm serve --omni`) which works only for models supported in the vLLM-Omni project.