China’s AI Chip Ambitions Struggle Without Foreign High-Bandwidth Memory

China’s ambitions in the artificial intelligence accelerator market face a far more pressing challenge than just semiconductor fabrication limits: the shortage of high-bandwidth memory (HBM).

While much attention has been placed on SMIC’s production capacity and on U.S. export controls affecting advanced lithography tools, a new analysis underscores that the real bottleneck lies in memory rather than logic chips.

HBM, a stacked memory technology that drastically increases bandwidth compared to conventional DRAM, has become the backbone of AI accelerators. Training large-scale models requires massive parallelism, and without sufficient HBM modules, even the most advanced processors cannot achieve their intended performance. SemiAnalysis describes the situation bluntly: China’s AI chip industry is essentially paralyzed without foreign HBM supply, and whatever production capacity exists for chips like Huawei’s Ascend 910C remains underutilized because the country simply does not have enough compatible memory.

Before U.S. restrictions tightened, Chinese firms stockpiled significant quantities of HBM. Samsung alone is reported to have shipped more than 11 million HBM stacks into China, forming the bulk of current inventories. These stockpiles have temporarily cushioned the impact, but the flow has since slowed dramatically. While grey-market channels still exist, their volumes are nowhere near the levels needed to sustain large-scale AI infrastructure growth. As a result, Huawei and others can design and potentially produce hundreds of thousands of AI accelerators, yet lack the memory required to actually ship them in meaningful numbers.

This imbalance grants an undeniable advantage to Western firms such as NVIDIA and AMD. With steady access to HBM supply chains through Korean and Japanese partners, they can continue ramping up AI offerings while Chinese counterparts remain constrained. Ironically, AMD – the original pioneer behind HBM integration, debuting the technology with the Radeon Fury graphics card years ago – now sees its invention underpinning not only GPUs but the multi-billion-dollar AI hardware boom largely dominated by NVIDIA.

On the domestic front, Chinese memory makers like CXMT are attempting to bridge the gap. However, turning conventional DRAM production lines into facilities capable of stacking and bonding HBM dies requires highly specialized equipment – much of it restricted by export controls. Beijing is lobbying for more flexibility and pouring investment into the sector. Analysts predict that, if momentum continues, Chinese firms could realistically reach HBM3E production by around 2026. But until then, the dependency on foreign HBM will remain a severe handicap.

The implications extend beyond hardware. AI research and development pipelines depend on access to computational resources. Without enough HBM, China’s aspirations to build massive model-training clusters face delays, potentially widening the gap with U.S. and European labs. Even if compute logic is available in abundance, bottlenecks in memory supply mean those chips sit idle, limiting overall innovation speed. Unless Beijing succeeds in scaling domestic HBM within the next two to three years, its AI acceleration strategy will remain vulnerable to external pressure points.

Ultimately, the story of China’s AI chip push highlights how supply chains are built on interdependent technologies. Cutting-edge processors may draw headlines, but without the bandwidth provided by HBM, they remain little more than silicon shells. The next phase in this contest will not be decided purely by transistor counts, but by who controls the lifeblood of modern AI – memory.