加载中
正在获取最新内容，请稍候...
vLLM Ascend Backend: 高效的华为昇腾AI硬件插件
Community-maintained hardware plugin enabling high-throughput serving of large language models (LLMs) using vLLM on Huawei Ascend AI hardware. Optimize your LLM inference performance on Ascend accelerators.
Python
Added on 2025年6月12日749
Stars190
ForksPython
LanguageProject Introduction
Summary
This project is a community-driven effort to develop and maintain a hardware backend plugin that allows vLLM to run efficiently on Huawei Ascend AI hardware. It aims to unlock the power of Ascend accelerators for serving large language models with vLLM's state-of-the-art techniques like PagedAttention.
Problem Solved
vLLM, a popular high-throughput serving library for LLMs, previously lacked native support for Huawei Ascend AI hardware, limiting hardware choices for users invested in the Ascend ecosystem. This project bridges that gap.
Core Features
Ascend Hardware Compatibility
Provides a backend implementation for vLLM specifically tailored for Huawei Ascend AI processors.
High-Performance Backend
Leverages Ascend's capabilities to offer competitive LLM inference throughput and reduced latency compared to generic solutions.
Seamless vLLM Integration
Integrates seamlessly with the existing vLLM framework, allowing users familiar with vLLM to easily utilize Ascend hardware.
Tech Stack
Python
vLLM
Ascend AI Ecosystem (CANN, PyTorch/MindSpore on Ascend)
AI Accelerators (Huawei Ascend)
使用场景
This plugin is essential for scenarios requiring high-performance LLM inference on Huawei Ascend hardware.
Serving LLMs on Ascend Servers
Details
Deploying large language models (e.g., Llama, Mistral) on servers equipped with Huawei Ascend AI processors for applications like chatbots, content generation, or analysis.
User Value
Achieve high user concurrency and low latency for LLM inference on Ascend infrastructure.
Enterprise AI Integration
Details
Integrating LLM capabilities into cloud services or enterprise applications running on platforms powered by Ascend hardware.
User Value
Leverage the efficiency of vLLM on Ascend for scalable and cost-effective AI services.
Recommended Projects
You might be interested in these projects
Asabeneh30-Days-Of-Python
A comprehensive, step-by-step guide designed to help beginners learn the Python programming language over 30 days. While structured for 30 days, the challenge can be completed at your own pace.
Python
467458920
libp2prust-libp2p
A Rust implementation of the libp2p networking stack, providing a flexible and modular foundation for building decentralized applications and peer-to-peer systems.
Rust
50281079
akvoradoakvorado
Akvorado is an open-source network flow collector, enricher, and visualizer designed for monitoring, analysis, and security of network traffic. It provides deep insights into network behavior by processing NetFlow, sFlow, and other flow protocols.
Go
1648104