Latest News 🔥
- [2026/10] We released the new official version v0.30.0! 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.
- [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 Huawei 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.
DeepWiki
DeepWiki is a dynamic knowledge base collaboratively maintained by the community and AI, designed to provide you with deeper technical insights beyond conventional documentation. If official documentation serves as a "quick start" guide to help you get going, the DeepWiki is your technical companion for "deep understanding". Here, you can explore: core architecture and design principles, key source code interpretations, technical context, and decision-making logic.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
