China's AI Developers Struggle to Break Free from Nvidia Dependency as Migration to Domestic Chips Proves Costly
Nashnova编辑部
Chinese AI firms still lean heavily on Nvidia chips for training complex models. The real barrier has shifted from hardware performance to the entire software ecosystem built around CUDA — migrating to domestic platforms can add at least 50% to project time and cost.
What is actually holding developers back?
Nvidia's CUDA — a software platform that lets developers write AI programs on GPUs — is deeply embedded in Chinese AI labs' daily operations. Models, training tools, and internal workflows are all built around it.
This means → switching to a domestic chip is not "swapping a machine." It is rebuilding part of the factory: rewriting code, replacing tools, re-validating pipelines.
In plain terms = the hardware gap is narrowing, but developers' ingrained habits and supporting software form Nvidia's deepest moat.
How much does migrating to Huawei Ascend cost?
Huawei's Ascend processor is one of the leading domestic alternatives, but a researcher estimates migration can increase project time and cost by at least 50%.
Open-source models such as DeepSeek are easier: engineers can modify source code and leverage community work — a few developers and a few weeks may suffice.
Closed-source systems are a different story. Without access to underlying code, engineers must rebuild training pipelines from scratch — industry estimates put a difficult migration at roughly 10 engineers and over six months.
Inference works — why is training harder?
Inference — using a trained model to answer everyday queries — adapts more easily to different hardware. China has made faster progress on this front.
Training is catching up too: Meituan says its large language model LongCat-2.0 was developed on a cluster of 50,000 domestic chips.
This reflects a split: domestic chips' raw computing power can already support large-scale training, but the software toolchain and developer familiarity around training remain weak links.
Where will this substitution race be won or lost?
Domestic processors keep closing the performance gap, but Nvidia's years of accumulated software ecosystem, developer familiarity, and infrastructure are hard to replicate quickly.
This means → in the near term, Chinese AI firms face not the question of "can we use domestic chips" but the economic calculation of "is the migration cost worth bearing."
In plain terms = chips can be caught up on, but the inertia moat built from habit and ecosystem is harder to breach than the hardware itself.
Content is for reference only, not financial advice.