vLLM Ascend Quantization Tool: 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.

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.

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.

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.

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.

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.