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China’s AI companies are using more domestic processors, but they have not stopped relying on Nvidia. The shift is a transition, not a completed swap: export restrictions and strategic concerns are pushing China toward chip self-sufficiency, while supply limits, manufacturing constraints and software compatibility keep Nvidia relevant.
Why China wants to rely less on Nvidia
The push has both a strategic motive and an immediate practical one. U.S. export controls restrict China’s access to advanced chips and semiconductor manufacturing equipment. That makes access to foreign technology uncertain, even for companies that would prefer to keep using it.
Huawei executive Eric Xu described the risk to Reuters this way: “We cannot accept a destiny where we cannot control our fate being determined by others in terms of willingness to sell chips to China or not.” He called full chip self-sufficiency “certainly the way forward” for the Chinese government, Chinese industry and Huawei.
That goal does not mean domestic alternatives can immediately meet every need. Reuters reported in September 2026 that Huawei’s demand for its AI processors already exceeded the company’s available supply in China. A domestic chip can be strategically important and increasingly adopted while still being scarce.
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What Huawei says it has deployed
Huawei’s reported figures indicate that its Ascend ecosystem has moved beyond a purely experimental role. Reuters reported in 2026 that Huawei said:
- More than 1,000 systems using Ascend 910C processors had been deployed.
- More than 5,200 developers were active on Ascend software each month.
- More than 40 AI models had been trained directly on Huawei’s computing platform.
These are company-reported figures, not independently audited totals. Reuters also reported that Ascend 950 systems had entered commercial use. In September 2026, Huawei said testing of the 950DT had produced good results and that it was discussing the chip with Chinese AI developers. Those discussions and test results are not the same as evidence of broad shipments or production capacity.
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Xu also asserted that Ascend’s market share should be larger than Nvidia’s, but Reuters noted Huawei did not provide supporting data. That statement is best understood as an executive’s view, not a verified market-share measurement.
Why Nvidia remains part of Chinese AI development
Domestic adoption and continued Nvidia use can happen at the same time. Companies may have existing systems and software built around Nvidia, while adding domestic chips for workloads that can run on them. The evidence from DeepSeek illustrates this mixed approach.
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Reuters reported that the foundation model behind DeepSeek’s R1 was trained on Nvidia’s H800, while DeepSeek has also used Huawei hardware. In April 2026, DeepSeek released V4 adapted for Huawei Ascend; Huawei said its processors were used in training part of V4-Flash. Adaptation for Ascend is evidence of compatibility work and domestic use, not evidence that Nvidia has disappeared from DeepSeek’s compute mix.
Software matters alongside hardware. Reuters identified Nvidia’s CUDA ecosystem as an advantage. Moving a workload to another processor can involve adapting software, validating performance and supporting developers—not simply installing a different chip. Huawei’s monthly developer and model figures suggest ecosystem activity, but they do not establish that every application can move without engineering work.
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What market-share estimates do—and do not—show
Market-share figures point to a rapid shift, but the available numbers are analyst estimates and a forecast rather than an official census. The Associated Press reported Bernstein estimates that Nvidia held about 40% of China’s AI-chip market in 2025, roughly matching Huawei. Bernstein forecast Nvidia at around 8% and Huawei at around 50% in 2026.
| Period | Nvidia | Huawei | What the figures represent |
|---|---|---|---|
| 2025 | About 40% | Roughly 40% | Bernstein estimate reported by the Associated Press |
| 2026 | Around 8% | Around 50% | Bernstein forecast reported by the Associated Press, not a measured result |
The forecast should not be read as a confirmed outcome. The AP report also described uncertainty and continuing demand for Nvidia technology. A separate Fortune report covered a survey of 60 executives across Chinese software, finance, manufacturing and retail companies, describing local substitution as progressing while Nvidia remained popular. The report does not establish a specific adoption percentage from that survey.
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Manufacturing capacity is a constraint, not a footnote
Designing a chip, announcing a product, shipping systems and producing enough processors for widespread use are different milestones. Huawei’s reported demand exceeding available supply shows why domestic demand alone does not guarantee a rapid substitution.
Reuters’ July 7, 2026 report said DeepSeek was developing an inference chip, citing three people familiar with the matter. It described the effort as early-stage; DeepSeek did not respond to Reuters’ request for comment. Reuters also reported manufacturing access and high-bandwidth memory as constraints relevant to the effort. A reported design project is not a confirmed product launch, shipment or production run.
Huawei has also outlined later Ascend generations, but roadmap dates are plans rather than completed launches. The practical pace of substitution depends on whether companies can manufacture and supply enough usable systems, and whether those systems support the workloads developers need.
How to read the shift
- Domestic use is increasing: Huawei’s reported deployments, software activity and model training show an active Ascend ecosystem, though those numbers come from the company.
- Nvidia remains in the stack: DeepSeek’s reported use of Nvidia and Huawei hardware shows that adoption of domestic chips does not prove Nvidia has been displaced.
- Forecasts are not results: The 2026 market-share figures are Bernstein’s forecast as relayed by the AP, not an official count.
- Supply and software shape the pace: Manufacturing and memory constraints, processor availability and developer support all affect how much work can move to domestic chips.
The clearest description is a constrained transition: Chinese AI firms are adding and adapting domestic compute as China pursues self-sufficiency, but the available reporting does not establish that they have stopped buying or using Nvidia chips.
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