adapt to vllm-ascend v0.18.0rc1
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starkwj
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README.md
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README.md
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<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm-ascend/main/docs/source/logos/vllm-ascend-logo-text-dark.png">
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<img alt="vllm-ascend" src="https://raw.githubusercontent.com/vllm-project/vllm-ascend/main/docs/source/logos/vllm-ascend-logo-text-light.png" width=55%>
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</p>
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<h3 align="center">
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vLLM Ascend Plugin
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</h3>
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<div align="center">
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[](https://deepwiki.com/vllm-project/vllm-ascend)
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</div>
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<p align="center">
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| <a href="https://www.hiascend.com/en/"><b>About Ascend</b></a> | <a href="https://docs.vllm.ai/projects/ascend/en/latest/"><b>Documentation</b></a> | <a href="https://slack.vllm.ai"><b>#SIG-Ascend</b></a> | <a href="https://discuss.vllm.ai/c/hardware-support/vllm-ascend-support"><b>Users Forum</b></a> | <a href="https://tinyurl.com/vllm-ascend-meeting"><b>Weekly Meeting</b></a> |
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</p>
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<p align="center">
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<a ><b>English</b></a> | <a href="README.zh.md"><b>中文</b></a>
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</p>
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---
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*Latest News* 🔥
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- [2026/02] We released the new official version [v0.13.0](https://github.com/vllm-project/vllm-ascend/releases/tag/v0.13.0)! Please follow the [official guide](https://docs.vllm.ai/projects/ascend/en/v0.13.0/) to start using vLLM Ascend Plugin on Ascend.
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- [2025/12] We released the new official version [v0.11.0](https://github.com/vllm-project/vllm-ascend/releases/tag/v0.11.0)! Please follow the [official guide](https://docs.vllm.ai/projects/ascend/en/v0.11.0/) to start using vLLM Ascend Plugin on Ascend.
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- [2025/09] We released the new official version [v0.9.1](https://github.com/vllm-project/vllm-ascend/releases/tag/v0.9.1)! Please follow the [official guide](https://docs.vllm.ai/projects/ascend/en/v0.9.1/tutorials/large_scale_ep.html) to start deploying large-scale Expert Parallelism (EP) on Ascend.
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- [2025/08] We hosted the [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/7n8OYNrCC_I9SJaybHA_-Q) with vLLM and Tencent! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF).
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- [2025/06] [User stories](https://docs.vllm.ai/projects/ascend/en/latest/community/user_stories/index.html) 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.
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- [2025/06] [Contributors](https://docs.vllm.ai/projects/ascend/en/latest/community/contributors.html) page is now live! All contributions deserve to be recorded, thanks for all contributors.
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- [2025/05] We've released the first official version [v0.7.3](https://github.com/vllm-project/vllm-ascend/releases/tag/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](https://blog.vllm.ai/2025/05/12/hardware-plugin.html).
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- [2025/03] We hosted the [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/VtxO9WXa5fC-mKqlxNUJUQ) with vLLM team! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF).
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- [2025/02] vLLM community officially created [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) repo for running vLLM seamlessly on the Ascend NPU.
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- [2024/12] We are working with the vLLM community to support [[RFC]: Hardware pluggable](https://github.com/vllm-project/vllm/issues/11162).
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---
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# XC-LLM: A Specially Optimized LLM Inference Engine for ModelHub XC
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## Overview
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vLLM Ascend (`vllm-ascend`) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
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The project is optimized based on the popular LLM inference project vLLM. This repo is for Ascend NPU.
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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](https://github.com/vllm-project/vllm/issues/11162), providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
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One of the key features of this project is efficient memory coordination, enabling multiple vLLM instances share and dynamically hold Ascend NPU's physical memory. When an instance is idle, model parameters are offloaded to host memory. Upon a new inference request, the model parameters are quickly restored to the NPU’s memory (if not exist), without the need to initialize the engine and load the model from scratch. As a result, from the application’s perspective, multiple LLM inference engines can run on the NPU even when their total memory requirements exceed the physical memory limit. This technique is referred to as `InfiniVRAM`.
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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.
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## Prerequisites
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## Installation
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- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
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- OS: Linux
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- Software:
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- Python >= 3.10, < 3.12
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- CANN == 8.5.0 (Ascend HDK version refers to [here](https://www.hiascend.com/document/detail/zh/canncommercial/83RC2/releasenote/releasenote_0000.html))
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- PyTorch == 2.9.0, torch-npu == 2.9.0
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- vLLM (the same version as vllm-ascend)
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### Build from Dockerfile
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## Getting Started
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Clone this repository:
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Please use the following recommended versions to get started quickly:
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```bash
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docker build -t $build_image -f ./Dockerfile .
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```
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| Version | Release type | Doc |
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|------------|--------------|--------------------------------------|
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| v0.17.0rc1 | Latest release candidate | See [QuickStart](https://docs.vllm.ai/projects/ascend/en/latest/quick_start.html) and [Installation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html) for more details |
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| v0.13.0 | Latest stable version | See [QuickStart](https://docs.vllm.ai/projects/ascend/en/v0.13.0/quick_start.html) and [Installation](https://docs.vllm.ai/projects/ascend/en/v0.13.0/installation.html) for more details |
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## Usage
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## Contributing
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> [!NOTE]
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> Some platforms may not allow multiple containers to share the same Ascend NPU. You may try to use privilegd container to bypass this restriction and mount all NPUs, and set the env ASCEND_RT_VISIBLE_DEVICES to specify the target device to use.
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See [CONTRIBUTING](https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/contribution/index.html) for more details, which is a step-by-step guide to help you set up the development environment, build and test.
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0. To share NPU, processes coordinate via shm, so you need to set all containers with `ipc=host`.
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1. Start a daemon process in a standalone container, by running `vllm_vnpu_daemon` installed inside the image.
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2. Start LLM services with this image, following the official usage instructions.
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3. Due to the limited stream resource of Ascend NPU, you may need to restrict graph capture sizes or disable ACLgraph by setting `--enforce-eager`, especially when launching multiple LLMs. Refer to the [link](https://docs.vllm.ai/projects/ascend/en/latest/faqs.html#how-to-troubleshoot-and-resolve-size-capture-failures-resulting-from-stream-resource-exhaustion-and-what-are-the-underlying-causes).
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We welcome and value any contributions and collaborations:
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### Environment Variables
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- `VNPU_RESERVED_VRAM_SIZE_GB`: The amonut of reserved GPU memory for other miscellaneous memory. Only needs to be set for `vllm_vnpu_daemon`. Try increasing the variable if you launch multiple LLM services and encounter OOM. Default: `8`.
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- `VLLM_VNPU_SHM_NAME`: The name of the shm file. Needs to be set for all containers of the shared vNPU group. Default: `/vllm_acl_vnpu_offload_shm`.
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- Please let us know if you encounter a bug by [filing an issue](https://github.com/vllm-project/vllm-ascend/issues)
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- Please use [User forum](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-support) for usage questions and help.
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## Branch
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## Limitations
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vllm-ascend has a main branch and a dev branch.
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- **main**: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
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- **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.
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Below are the maintained branches:
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| Branch | Status | Note |
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|------------|--------------|--------------------------------------|
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| main | Maintained | CI commitment for vLLM main branch and vLLM v0.17.0 tag |
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| v0.7.1-dev | Unmaintained | Only doc fixes are allowed |
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| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fixes are allowed, and no new release tags anymore. |
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| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
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| v0.11.0-dev | Maintained | CI commitment for vLLM 0.11.0 version |
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| releases/v0.13.0 | Maintained | CI commitment for vLLM 0.13.0 version |
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| rfc/feature-name | Maintained | [Feature branches](https://docs.vllm.ai/projects/ascend/en/latest/community/versioning_policy.html#feature-branches) for collaboration |
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Please refer to [Versioning policy](https://docs.vllm.ai/projects/ascend/en/latest/community/versioning_policy.html) for more details.
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## Weekly Meeting
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- vLLM Ascend Weekly Meeting: <https://tinyurl.com/vllm-ascend-meeting>
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- Wednesday, 15:00 - 16:00 (UTC+8, [Convert to your timezone](https://dateful.com/convert/gmt8?t=15))
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## License
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Apache License 2.0, as found in the [LICENSE](./LICENSE) file.
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- Restricted by the fact that HCCL cannot be shared, deploying more than one model with multi-GPU (e.g., TP) is not feasible currently.
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- The prefix cache will be reset when the LLM is restored, since we just simply discard the KV cache when the LLM is offloaded.
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