This release is a pre-release and may not be stable for production use.
vLLM Ascend Plugin
| About Ascend | Documentation | Support Matrix | #SIG-Ascend | Users Forum | Weekly Meeting |
Latest News 🔥
- [2026/09] We released the new release candidate v0.27.1rc1! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2026/08] We released the new official version v0.23.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2026/05] We released the new official version v0.18.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2026/02] We released the new official version v0.13.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
More
- [2025/12] We released the new official version v0.11.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploying large-scale Expert Parallelism (EP) on Ascend.
- [2025/08] We hosted the vLLM Beijing Meetup with vLLM and Tencent! Please find the meetup slides.
- [2025/06] User stories page is now live! It kicks off with LLaMA-Factory/verl/TRL/GPUStack to demonstrate how vLLM Ascend assists Ascend users in enhancing their experience across fine-tuning, evaluation, reinforcement learning (RL), and deployment scenarios.
- [2025/06] Contributors page is now live! All contributions deserve to be recorded, thanks for all contributors.
- [2025/05] We've released the first official version v0.7.3! We collaborated with the vLLM community to publish a blog post sharing our practice: Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU.
- [2025/03] We hosted the vLLM Beijing Meetup with vLLM team! Please find the meetup slides.
- [2025/02] vLLM community officially created vllm-project/vllm-ascend repo for running vLLM seamlessly on the Ascend NPU.
- [2024/12] We are working with the vLLM community to support [RFC]: Hardware pluggable.
Overview
vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, 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 (MoE), Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
For detailed information on supported models and features, please refer to the support matrix.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
- OS: Linux
- Software:
- Python >= 3.10, < 3.13
- CANN == 9.1.0 (For Ascend HDK version, please refer to the Release Notes)
- PyTorch == 2.10.0, TorchNPU == 2.10.0.post4
- vLLM (the same version as vllm-ascend)
Accessing Ascend NPU
If you need to access Ascend NPU computing resources for development or testing, please visit the HiDevLab - Online Development page on the Huawei HiDevLab platform to apply for and use them.
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.27.1rc1 | Release candidate | See QuickStart and Installation for more details |
| v0.23.0 | Latest stable version | See QuickStart and Installation for more details |
Branch
vllm-ascend has a main branch and a dev branch.
- main: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- releases/vX.Y.Z: development branch, created alongside new releases of vLLM. For example,
releases/v0.13.0is the dev branch for vLLMv0.13.0version.
Below are the maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.27.1 tag |
| releases/v0.13.0 | Maintained | Only bug fixes are allowed, and no new release tags anymore. |
| releases/v0.18.0 | Maintained | CI commitment for vLLM 0.18.0 version |
| releases/v0.23.0 | Maintained | CI commitment for vLLM 0.23.0 version |
| rfc/ | Maintained | Feature branches for collaboration |
Please refer to Versioning policy for more details.
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up the development environment, build and test.
We welcome and value any contributions and collaborations:
- Please let us know if you encounter a bug by filing an issue
- Please use User forum for usage questions and help.
Weekly Meeting
- vLLM Ascend Weekly Meeting: https://tinyurl.com/vllm-ascend-meeting
- Wednesday, 15:00 - 16:00 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.
1 maintainer
Release files for vllm-ascend 0.27.1rc1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vllm_ascend-0.27.1rc1.tar.gz | 18.5 MB | Details |
Release files / vllm_ascend-0.27.1rc1.tar.gz
| Download URL | vllm_ascend-0.27.1rc1.tar.gz |
|---|---|
| Size | 18.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.14
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