vLLM Ascend Plugin

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Latest News 🔥

• [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.
• [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 here.
• [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 here.
• [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.

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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.

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, < 3.12
  • CANN == 9.0.0 (Ascend HDK version refers to here)
  • PyTorch == 2.10.0, torch-npu == 2.10.0
  • vLLM (the same version as vllm-ascend)

Getting Started

Please use the following recommended versions to get started quickly:

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.

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/v0.13.0 is the dev branch for vLLM v0.13.0 version.

Below are the maintained branches:

Please refer to Versioning policy for more details.

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.