Huawei's primary AI chip production challenge stems from U.S. export controls restricting access to advanced manufacturing equipment, forcing reliance on SMIC's less mature 7nm process with poor yields and high costs. This leads to technological lag compared to Nvidia's 4nm chips, and Huawei struggles with high-bandwidth memory (HBM), inter-chip connectivity, and an immature software ecosystem (CANN, MindSpore) needed for large-scale model training. While using advanced packaging and chiplet designs can boost performance, these methods also face limitations in a resource-constrained environment.

• Export Controls: U.S. and Dutch sanctions limit China's access to cutting-edge chipmaking equipment and services, hindering the production of advanced, high-yield chips.
• 7nm Process Bottleneck: Huawei's advanced chips, like the Ascend series, are produced by SMIC, but using older, less mature 7nm-class nodes results in low yields and poor quality.
• Intergenerational Lag: This reliance on older technology places Huawei "a generation or two behind" its U.S. competitors, who use more advanced nodes like TSMC's 4nm process for high-end chips.

• Memory Constraints: AI training and running large language models demand High-Bandwidth Memory (HBM), but Huawei faces challenges in both HBM availability and its own chip's inter-chip connectivity and memory speeds.
• Chiplet Design Limitations: Huawei uses chiplet designs (like combining two smaller dies) to improve performance on its newer chips, such as the 910C, but this also makes advanced packaging critical and complex to implement with domestic capabilities.