Qualcomm plans China-specific data center chips — new Dragonfly lineup will include nerfed AI accelerators that comply with export thresholds
While the U.S. flip-flops on chip sanctions, China is building its own chip supply market
SMIC — China's largest and most advanced foundry — this week announced that its Q2 2026 revenue increased to $3.005 billion, up from $2.505 billion in Q1 2026, and from $2.209 billion in Q2 2025. This suggests that the company is both increasing the output of chips and its prices. However, it remains to be seen whether SMIC's 36% YoY revenue increase is an indicator that it can increase output of high-end AI accelerators by over 2X compared to 2025.
Another major bottleneck for the Chinese industry is the lack of domestic production of high-bandwidth memory (HBM). Huawei has reportedly acquired plenty of HBM2-class memory from Samsung, but its stock is not endless, so its Ascend 950-series AI accelerators are set to rely on proprietary HiBL 1.0 and HiZQ 2.0 types of memory , not industry-standard HBM2 or HBM3. While China's DRAM champion CXMT is gearing up for HBM3 manufacturing in late 2026 , it remains to be seen how quickly the company can ramp up production to decent levels.
Nvidia's CUDA software stack is the company's biggest advantage after the performance and versatility of its AI accelerators. But while performance can be matched with brute force , the software stack cannot be reproduced quickly. Last year, Huawei opened up its CANN software stack to accelerate its development, though we do not know if the company has achieved its targeted goals with this. Yet, without a doubt, China's AI software stack is getting more mature every year, so many new AI deployments may indeed rely on domestic stacks rather than on CUDA.
U.S. restrictions on exports of advanced AI accelerators, combined with China's own efforts to limit the use of American AI processors domestically, have largely pushed companies like AMD and Nvidia out of the Chinese market. Analysts now expect China-based independent hardware vendors to control 90% of the domestic AI accelerator market in 2026.
Chinese AI hardware has come a long way, and Huawei's solutions can outperform Nvidia's NVL72 GB200 rack-scale system, albeit while consuming more power. Therefore, if power is not a concern, Huawei can build AI data centers with performance that matches or exceeds those based on Nvidia GPUs.
However, replacing American GPUs almost completely while maintaining AI accelerator unit TAM at 4 million units will require China's industry to product 1.96 million AI accelerators in 2026, 2.2X more than in 2025. This seems impossible not only for TSMC, but also for local memory makers that still have to start making HBM memory.
To that end, while Chinese AI accelerators may indeed capture 90% of the domestic market, without hardware from American companies, that market can shrink dramatically in terms of units.
Anton Shilov is a contributing writer at Tom\u2019s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends. ","collapsible":{"enabled":true,"maxHeight":250,"readMoreText":"Read more","readLessText":"Read less"}}), "https://slice.vanilla.futurecdn.net/13-4-25/js/authorBio.js"); } else { console.error('%c FTE ','background: #9306F9; color: #ffffff','no lazy slice hydration function available'); } Anton Shilov Social Links Navigation Contributing Writer Anton Shilov is a contributing writer at Tom’s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends.
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- https://www.tomshardware.com/tech-industry/artificial-intelligence/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/artificial-intelligence/chinas-homegrown-ai-accelerators-to-supply-90-percent-of-the-countrys-domestic-market-analysts-suggest-cambricon-and-huawei-expected-to-be-the-biggest-winners-in-the-shift-away-from-nvidia-and-amd#main
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