Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2

• URL: http://arxiv.org/abs/2512.23367v1
• Date: Mon, 29 Dec 2025 10:50:23 GMT
• Title: Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2
• Authors: Yilun Luo, HuaQing Zheng, Haoqian Meng, Wenyuan Liu, Peng Zhang,
• Abstract summary: Huawei's openPangu-Embedded-1B and openPangu-Embedded-7B integrate three distinct Chain-of-Thought (CoT) reasoning paradigms.<n>These CoT modes introduce substantial memory and latency overheads, posing challenges for practical deployment on Ascend NPUs.<n>This paper addresses these computational constraints by leveraging low-bit quantization, which transforms FP16 computations into more efficient integer arithmetic.
• Score: 3.309291427648113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Huawei's openPangu-Embedded-1B and openPangu-Embedded-7B, variants of the openPangu large language model, integrate three distinct Chain-of-Thought (CoT) reasoning paradigms, namely slow_think, auto_think, and no_think. While these CoT modes enhance reasoning capabilities, their generation of extended reasoning traces introduces substantial memory and latency overheads, posing challenges for practical deployment on Ascend NPUs. This paper addresses these computational constraints by leveraging low-bit quantization, which transforms FP16 computations into more efficient integer arithmetic. We introduce a unified low-bit inference framework, supporting INT8 (W8A8) and W4A8 quantization, specifically optimized for openPangu-Embedded models on the Atlas A2. Our comprehensive evaluation, conducted across all three CoT modes on code generation benchmarks (HumanEval and MBPP), demonstrates the efficacy of this approach. INT8 quantization consistently preserves over 90\% of the FP16 baseline accuracy and achieves a 1.5x prefill speedup on the Atlas A2. Furthermore, W4A8 quantization significantly reduces memory consumption, albeit with a moderate trade-off in accuracy. These findings collectively indicate that low-bit quantization effectively facilitates efficient CoT reasoning on Ascend NPUs, maintaining high model fidelity.

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