Epoch AI Analysis: Why Huawei Is Unlikely to Catch Up with Nvidia by 2030
Epoch AI researchers Venkat Somala and Lynette Bye quantify the compute gap between Huawei and Nvidia at 25× in 2026 and argue that export controls constrain both per-chip performance and production volume, leaving Huawei approximately four years behind through 2030.
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In the core infrastructure of US-China AI competition—compute capacity—China's leading AI chip designer, Huawei, faces significant headwinds. Epoch AI researchers Venkat Somala and Lynette Bye present a central thesis in their September 24, 2026 report: although Huawei plans to substantially improve its AI compute solutions by 2030, US export controls constrain its most important scaling levers, making it unlikely to catch up with Nvidia.
The report quantifies the current gap using H100-equivalents (H100e) as a baseline. In 2026, Huawei's flagship Ascend 950 chip delivers roughly one-seventh the compute throughput of Nvidia's B300 and half that of Nvidia's 2022 H100, suggesting a four-year lag in per-chip performance. On volume, Huawei is estimated to produce 1.5 million chips in 2026 compared to Nvidia's roughly 6 million. Multiplying performance by volume, Huawei will produce approximately 25 times less total compute than Nvidia in 2026.
Huawei aims to close this gap through two paths. On chip performance, because fabrication partner SMIC cannot pack transistors as densely as TSMC—and advanced lithography imports are restricted—Huawei is pursuing bigger chips, architecture optimizations for lower precision, and vertical stacking called LogicFolding. However, vertical stacking won't reach the Ascend line until 2030, while Nvidia is expected to deploy superior stacking in its Feynman generation by 2028, potentially compounding its transistor density advantage from 2× to 4×.
At the system level, Huawei plans to connect 8,192 chips into SuperPoDs to compensate for weaker individual units. Yet maintaining high-bandwidth communication across thousands of chips presents steep engineering trade-offs. Furthermore, Huawei's software stack CANN remains far less mature than Nvidia's CUDA. DeepSeek attempted to train its R2 model on Ascend chips, Huawei even sent its own engineers in, and still couldn't complete a training run, prompting DeepSeek to revert to Nvidia hardware.