arXiv:2606.21257v2 Announce Type: replace Abstract: OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized. This paper conducts a controlled empirical study of OpenPangu 1B and 7B models on Huawei Ascend 910B1 NPUs. We evaluate representative weight-only and weight-activation post-training quantization methods, including RTN, GPTQ, AWQ, SmoothQuant, GPTAQ, BiLLM, and SliM-LLM, under a unified calibration and evaluation protocol. Ac
The increasing focus on national AI capabilities and the need for efficient large language model deployment on specific hardware platforms are driving this research now.
This study demonstrates progress in making advanced AI models robust on non-Western hardware, which is crucial for domestic AI development and reduces reliance on dominant tech stacks.
The ability to effectively quantize OpenPangu models on Ascend NPUs improves the feasibility and efficiency of deploying these models privately and domestically.
- · Huawei
- · Nations pursuing sovereign AI
- · AI developers using Ascend NPUs
- · OpenPangu users
- · Dependence on US/Western AI hardware
- · Inefficient AI deployment methods
Improved performance and cost-efficiency of OpenPangu models on Ascend NPUs for specific applications.
Accelerated development and adoption of sovereign AI initiatives leveraging Ascend hardware and OpenPangu models.
Potential for a more fragmented global AI ecosystem with distinct national or regional AI technology stacks.
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Read at arXiv cs.LG