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NewsTopic · 华为英伟达算力差距3 min read·2026-10-12DisconfirmAI Editorial

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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Original publication time 2026-09-25 00:04 GMT+8

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×.

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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.

Production constraints are equally rigid. Huawei relies on domestic suppliers CXMT and XMC for High Bandwidth Memory, whose ramp-up is slow. The report estimates that even by 2028, Huawei's annual output using domestic HBM would reach around 1.5 million H100e, roughly 1.5% of Nvidia's production. With the stockpile of over 10 million foreign HBM stacks built before December 2024 export controls expected to run out in 2026, the volume ceiling becomes more binding. Even assuming Huawei smuggled ten times its domestic HBM in 2028—an implausibly large flow costing over $40 billion—its output would still reach only about 16% of Nvidia's production that year.

The report concludes that Huawei will likely remain around four years behind in both chip performance and compute production. Export controls hit both volume and performance simultaneously, making the path to parity significantly harder, slower, and more expensive. The authors acknowledge Huawei is one of the world's most capable engineering organizations and may eventually catch up, but available information strongly suggests it will not happen by 2030.

Sources

  • 原文与论证epochai.substack.com↗
Topic

华为英伟达算力差距

【背景】在半导体出口管制持续收紧的背景下,华为与英伟达的AI算力差距成为衡量中美科技竞争的关键指标。尽管华为推出了Atlas 960E等集群方案并制定了激进路线图,但多项权威分析指出,受限于芯片性能与高带宽内存(HBM)供应瓶颈,华为难以在…

4 steps1 piecesUpdated 2026-10-12 23:29

How it got here

  1. 2025-12-15美国外交关系委员会(CFR)报告指出,华为并非上升的竞争者,而是因出口管制进一步落后,建议维持对华芯片限制。
  2. 2026-09-04Epoch AI与Samaritan Research发布联合分析,量化华为2026年总算力仅为英伟达的不到4%,预测其到2030年将保持约4年的技术代差。
  3. 2026-09-20华为在HUAWEI CONNECT 2026大会上发布Atlas 960E SuperPoD,连接4096颗昇腾NPU,宣称提供8 EFLOPS FP8算力,旨在通过系统级互联弥补单芯片性能不足。

What is still unsettled

  • 华为CANN软件栈能否在2027年前实现足够成熟的生态,以显著降低DeepSeek等大模型公司从CUDA迁移的技术成本?
  • 中国本土HBM制造商(如CXMT)在2028年前的实际产能爬坡速度是否足以支撑华为将算力占比提升至10%以上?
  • 华为计划于2030年推出的LogicFolding垂直堆叠技术,能否在良率和成本上达到商业化规模,从而实质性缩小与英伟达Feynman架构的性能差距?
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