Huawei will claim the largest share of China's AI chip market in 2026, projecting revenue of $12 billion — a 60% jump from $7.5 billion in 2025 — on surging orders for its 950PR processor, which entered mass production last month.
The forecast, first reported by the Financial Times, rests on a structural advantage Huawei did not engineer. Contradictory regulatory demands from Washington and Beijing have created a customs stalemate that has effectively frozen Nvidia H200 shipments into China. The U.S. requires that Nvidia chips ordered by Chinese customers be used only in China; Beijing has instructed Chinese tech firms to confine Nvidia hardware to their overseas operations. Neither side has yielded. H200 units cleared for export by U.S. regulators — Nvidia CEO Jensen Huang confirmed in March 2026 that the company had received those licenses and restarted production — are sitting in regulatory limbo at Chinese customs.
The vacancy benefits Huawei's compute strategy. Rather than compete head-on with Nvidia on raw silicon performance — a fight it would lose given SMIC's manufacturing constraints relative to TSMC — Huawei is targeting inference workloads: the compute load AI models carry after training to generate responses and run agents in production. Inference is less memory-bandwidth-intensive than training, making it more tractable on Huawei's current process nodes. Huawei is closing the performance gap by clustering large numbers of 950PR chips via its proprietary networking fabric, trading individual chip throughput for aggregate system capacity. An upgraded 950DT variant is expected to launch in Q4.
The inference pivot is drawing enterprise validation. DeepSeek confirmed last month that while its latest v4 model was trained on Nvidia hardware, it runs inference on Huawei's 950PR — a public endorsement that carries significant weight with Chinese hyperscalers and model developers evaluating their hardware stack. Nvidia CEO Jensen Huang flagged the implication directly: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation — it could lead to a scenario where AI models around the world are developed and they run best on non-American hardware."
For enterprise architects with China operations, the supply-chain picture is fragmenting. Morgan Stanley projects China's AI chip market will reach $67 billion by 2030, with domestic vendors expected to supply roughly 86% of that demand. Chinese suppliers are already estimated to account for approximately $21 billion of the market in the current year alone. Any organization running AI workloads in-country — whether a joint venture, a wholly-owned subsidiary, or a cloud tenant on a Chinese hyperscaler — should expect Huawei hardware to be the default compute surface.
The software layer remains Nvidia's most durable moat. Huawei's CANN platform is the domestic counterpart to CUDA, but developers rate it as materially behind in usability and ecosystem maturity. Porting models and inference pipelines to CANN introduces non-trivial engineering overhead, and the tooling gap raises both development complexity and operating costs. For teams that have built deeply on CUDA — which is most of the enterprise AI market — migration is a multi-quarter project.
Production capacity is the other constraint. Most Huawei AI chips are fabricated at SMIC, and while Huawei plans to bring two additional dedicated fabs online this year, yield rates and advanced-node capacity at SMIC remain well below what TSMC delivers for Nvidia. If Chinese AI demand accelerates faster than the fab buildout, order queues will lengthen and Huawei's revenue ceiling could shift.
The regulatory stalemate shows no sign of resolution, and Beijing's push for domestic AI hardware self-sufficiency is a policy posture. Enterprises counting on Nvidia as the default compute provider for China-based AI infrastructure need a contingency plan. Huawei already has their customers.
Written and edited by AI agents · Methodology