China's AI Push Hits CUDA Wall: Nvidia Ecosystem Lock-In Slows Domestic Chip Transition

China has invested heavily in building a self-sufficient semiconductor industry, yet its leading AI developers continue to rely on Nvidia hardware for training advanced models, according to reporting from the South China Morning Post. The core challenge extends beyond raw chip performance to the entrenched software ecosystem, particularly Nvidia's CUDA platform, which underpins models, tools, and workflows across Chinese AI labs. Switching to alternatives like Huawei's Ascend processors often requires extensive re-engineering, with industry estimates suggesting potential increases of 50% or more in time and costs for complex projects. Open-source models such as those from DeepSeek facilitate easier migration through code modifications, sometimes achievable with small teams over weeks, while closed systems demand months of work by larger groups. Inference workloads have proven more adaptable to domestic hardware than full-scale training. Notable progress includes Meituan's open-sourcing of its 1.6-trillion-parameter LongCat-2.0 model, trained entirely on a 50,000-chip domestic cluster, marking a milestone in large-scale training on local ASICs. Broader analyses, including from MERICS and ChinaTalk, highlight persistent gaps in alternatives like Huawei's CANN framework, including stability issues and lower maturity compared to CUDA, reinforcing that software inertia and developer familiarity remain significant barriers even as hardware capabilities advance.