cb963c53a5
### What this PR does / why we need it?
[Kthena](https://github.com/volcano-sh/kthena) is a Kubernetes-native
LLM inference platform that transforms how organizations deploy and
manage Large Language Models in production. Built with declarative model
lifecycle management and intelligent request routing, it provides high
performance and enterprise-grade scalability for LLM inference
workloads.
The platform extends Kubernetes with purpose-built Custom Resource
Definitions (CRDs) for managing LLM workloads, supporting multiple
inference engines (vLLM, SGLang, Triton) and advanced serving patterns
like prefill-decode disaggregation.
This pr added a example on deloying llm on Ascend Kubernetes clusters.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: Zhonghu Xu <xuzhonghu@huawei.com>
72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
# Welcome to vLLM Ascend Plugin
|
|
|
|
:::{figure} ./logos/vllm-ascend-logo-text-light.png
|
|
:align: center
|
|
:alt: vLLM
|
|
:class: no-scaled-link
|
|
:width: 70%
|
|
:::
|
|
|
|
:::{raw} html
|
|
<p style="text-align:center">
|
|
<strong>vLLM Ascend Plugin
|
|
</strong>
|
|
</p>
|
|
|
|
<p style="text-align:center">
|
|
<script async defer src="https://buttons.github.io/buttons.js"></script>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend" data-show-count="true" data-size="large" aria-label="Star">Star</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
|
|
</p>
|
|
:::
|
|
|
|
vLLM Ascend plugin (vllm-ascend) is a community maintained hardware plugin for running vLLM on the Ascend NPU.
|
|
|
|
This plugin is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [[RFC]: Hardware pluggable](https://github.com/vllm-project/vllm/issues/11162), providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
|
|
|
|
By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Experts, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
|
|
|
|
## Documentation
|
|
|
|
% How to start using vLLM on Ascend NPU?
|
|
:::{toctree}
|
|
:caption: Getting Started
|
|
:maxdepth: 1
|
|
quick_start
|
|
installation
|
|
tutorials/index.md
|
|
faqs
|
|
:::
|
|
|
|
% What does vLLM Ascend Plugin support?
|
|
:::{toctree}
|
|
:caption: User Guide
|
|
:maxdepth: 1
|
|
user_guide/support_matrix/index
|
|
user_guide/configuration/index
|
|
user_guide/feature_guide/index
|
|
user_guide/deployment_guide/index
|
|
user_guide/release_notes
|
|
:::
|
|
|
|
% How to contribute to the vLLM Ascend project
|
|
:::{toctree}
|
|
:caption: Developer Guide
|
|
:maxdepth: 1
|
|
developer_guide/contribution/index
|
|
developer_guide/feature_guide/index
|
|
developer_guide/evaluation/index
|
|
developer_guide/performance_and_debug/index
|
|
:::
|
|
|
|
% How to involve vLLM Ascend
|
|
:::{toctree}
|
|
:caption: Community
|
|
:maxdepth: 1
|
|
community/governance
|
|
community/contributors
|
|
community/versioning_policy
|
|
community/user_stories/index
|
|
:::
|