# vLLM Ascend Quantization Tool: Large Model Quantization Practice on Ascend NPUs

> The vLLM Ascend quantization tool open-sourced by the Huazhong University of Science and Technology team supports 8-bit, 4-bit, and mixed-precision quantization, providing a solution for efficient deployment of large language models on Ascend NPUs.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-10T06:15:33.000Z
- 最近活动: 2026-06-10T06:50:34.084Z
- 热度: 145.4
- 关键词: 大语言模型, 模型量化, 昇腾NPU, 华为Ascend, vLLM, 后训练量化, INT8, INT4, 国产AI芯片, 模型压缩
- 页面链接: https://www.zingnex.cn/en/forum/thread/vllm-ascend-npu-c7550ab4
- Canonical: https://www.zingnex.cn/forum/thread/vllm-ascend-npu-c7550ab4
- Markdown 来源: floors_fallback

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## vLLM Ascend Quantization Tool: Guide to Large Model Quantization Practice on Ascend NPUs

The vLLM-HUST team from Huazhong University of Science and Technology open-sourced the vllm-ascend-quant-hust project on GitHub on June 10, 2026 (link: https://github.com/vLLM-HUST/vllm-ascend-quant-hust). Optimized for Huawei Ascend NPUs, this tool supports 8-bit, 4-bit, and mixed-precision post-training quantization. It aims to solve the problem of efficient deployment of large language models on domestic Ascend chips and provides developers with flexible quantization strategy options.

## Background: Computing Power Challenges and Quantization Needs for Large Model Deployment

As the scale of large language models grows, the computing resources and memory overhead required for inference increase exponentially, placing extremely high demands on hardware. Model quantization reduces memory usage and computation while maintaining performance by lowering parameter precision. However, different hardware platforms support different quantization formats, and how to deeply integrate quantization technology with local hardware in the domestic AI chip field is a focus of industry attention.

## Core Features: Multi-Precision Quantization and Ascend NPU Optimization

This tool is extended based on the vLLM inference framework, with core features including:
- **Multi-precision support**: 8-bit quantization (INT8) balances precision and performance; 4-bit quantization (INT4/FP4) is suitable for resource-sensitive scenarios; mixed precision allows different layers to use different precisions;
- **Deep Ascend optimization**: Optimized for the matrix computing capabilities and memory access mechanisms of the Ascend NPU's Da Vinci architecture;
- **Post-training quantization (PTQ)**: No need to retrain the model, lowering the threshold for use.

## Application Scenarios: Edge, Cloud, and Domestic Replacement

The practical value of the tool is reflected in three major scenarios:
1. **Edge device deployment**: Compressed models can run on Ascend edge devices, supporting applications like intelligent customer service;
2. **Cloud inference cost optimization**: Quantized models increase concurrency and reduce memory costs;
3. **Domestic replacement**: Helps developers achieve efficient deployment of large models without relying on foreign hardware, promoting the construction of the domestic AI ecosystem.

## Technical Implementation: Calibration, Integration, and Operator Adaptation

The project implementation needs to solve three key problems:
- **Quantization calibration**: Uses statistical methods based on calibration datasets to determine per-layer quantization parameters (scaling factors and zeros);
- **vLLM integration**: Deeply integrated with vLLM's PagedAttention technology and memory management mechanism;
- **Ascend operator adaptation**: Implements or calls low-precision computing operators of Ascend NPUs to ensure efficient execution.

## Summary and Outlook: An Important Piece of the Domestic AI Ecosystem

vllm-ascend-quant-hust fills the quantization gap of the vLLM ecosystem on the Ascend platform, providing a practical tool for the deployment of large models on domestic chips. As large model applications expand, the importance of quantization technology will become increasingly prominent. We look forward to more localized optimization projects to promote the implementation of large model technology in more scenarios.