For years, Nvidia’s CUDA software platform has functioned as the invisible infrastructure of the global AI industry, a proprietary ecosystem so deeply embedded in the workflows of researchers and engineers that switching away from it seemed practically impossible. However, at the recent Alibaba Cloud Summit, Chinese technology companies delivered a compelling demonstration that this dependency is finally being broken. The event showcased a maturing domestic AI software stack that is increasingly capable of supporting complex, production-grade workloads without relying on American technology, marking a critical milestone in China’s drive for technological self-sufficiency.
The Strategic Necessity of Breaking CUDA Dependence
The transition away from CUDA is not a matter of preference but of strategic necessity. With U.S. export controls blocking access to Nvidia’s most advanced hardware and the Chinese government formally ordering the removal of foreign AI chips from state-funded data centers, Chinese developers have had no choice but to invest heavily in alternative ecosystems. The Alibaba Cloud Summit provided the most comprehensive public showcase yet of how far these efforts have progressed.
Presentations at the summit featured seamless integrations of major Chinese large language models running entirely on domestic hardware and software architectures. Developers demonstrated workflows that would previously have required CUDA, now executing efficiently on alternative platforms. The breadth of the demonstrations, spanning training, inference, fine-tuning, and deployment, suggested that the domestic stack has moved beyond proof-of-concept and into genuine production readiness.
Huawei’s Ascend and the CANN Ecosystem
A key driver of this shift is the increasing adoption of Huawei’s Ascend chip platform and its accompanying CANN (Compute Architecture for Neural Networks) software stack. As demand for the Huawei Ascend 910C has surged, developers across China have been investing the necessary time and resources to optimize their models for the CANN environment. This collective effort is generating a network effect: improved software tooling attracts more developers, which drives further refinement of the tools, which in turn attracts more developers.
The Alibaba Cloud Summit demonstrated that this flywheel is now spinning at meaningful speed. Multiple companies presented case studies of successful migrations from CUDA-based workflows to CANN-based alternatives, with performance benchmarks that, while not always matching Nvidia’s flagship hardware, are increasingly competitive for a wide range of production workloads.
Alibaba’s Open-Source Software Strategy
Alibaba Cloud is playing a central and distinctive role in this ecosystem transformation. The company recently unveiled its Zhenwu M890 AI chip, which delivers a 3x performance leap over its predecessor. Crucially, Alibaba is pairing this hardware with a robust, open-source software toolkit designed to lower the barrier to entry for developers migrating away from CUDA.
By open-sourcing key components of its AI infrastructure, Alibaba is fostering a collaborative development environment where startups, research institutions, and independent developers can contribute to and benefit from the domestic software stack. This approach mirrors the strategy that has made the company’s Qwen model family so successful: the Qwen family has surpassed 1 billion downloads globally by making high-quality AI accessible to a broad developer community.
The Broader Implications for Global AI Competition
The maturation of China’s domestic AI software stack has implications that extend well beyond China’s borders. As Chinese developers become less reliant on CUDA, the effectiveness of U.S. export controls as a tool for maintaining technological advantage diminishes. A developer who can achieve comparable results using domestic hardware and software has little need for restricted American products, regardless of how stringent the export regime becomes.
Furthermore, a robust and well-documented alternative software ecosystem could eventually attract developers from outside China, particularly in emerging markets across Southeast Asia, the Middle East, and Africa that are seeking cost-effective and geopolitically neutral AI infrastructure. As Chinese cloud firms are already reshaping Southeast Asia’s AI infrastructure landscape, the software layer could become the next frontier of this expansion.
While Nvidia’s CUDA remains the global standard for AI development, the Alibaba Cloud Summit provided clear evidence that its monopoly is no longer absolute. The concerted, well-funded effort by Chinese tech giants to build a viable, independent software stack is yielding tangible results, accelerating the bifurcation of the global AI ecosystem into distinct and increasingly self-sufficient technological spheres.
The pace of this bifurcation is being driven not only by regulatory pressure but by genuine technical progress. Chinese developers who have spent the past two years porting their workflows to domestic platforms report that the performance gap has narrowed substantially, particularly for inference workloads. As the domestic software stack matures and the developer community around it grows, the switching costs of returning to CUDA-based workflows will increase, making the decoupling increasingly irreversible. For Nvidia, the long-term risk is not merely losing the Chinese market—it is losing the Chinese developer community’s institutional knowledge and contribution to the broader AI software ecosystem, a loss that could have compounding effects on its global competitive position over time.