A week after Huawei announced its decision to open-source the CANN (Compute Architecture for Neural Networks) software toolkit, the tech industry is still processing what this move means for the future of AI development.
By making its Huawei CANN open source alternative to CUDA freely available to developers worldwide, the Chinese tech giant has fired what many consider a significant shot in the battle against NVIDIA’s two-decade and continuing dominance over AI computing.
While it’s a notable challenge to the status quo, the real question is whether Huawei can overcome the substantial technical and systemic barriers that have kept CUDA virtually unchallenged for nearly twenty years.
What is CANN and why does it matter?
CANN is Huawei’s heterogeneous computing architecture that offers multi-level programming interfaces to help developers build AI applications optimised for Huawei’s Ascend AI GPUs. First introduced in 2018 as part of Huawei’s AI strategy, CANN serves as the company’s equivalent to NVIDIA’s CUDA platform.
CANN provides APIs for AI applications on Ascend, giving developers several options for building high-level and performance-intensive applications. The architecture represents years of development aimed at creating a comprehensive software ecosystem around Huawei’s AI hardware.
The strategic timing behind the open-source decision
Huawei’s decision to make CANN open-source comes at a particularly tense moment in US-China technology relations. Huawei’s rotating chairman Eric Xu Zhijun said the move would help “speed up innovation from developers” and “make Ascend easier to use” during the company’s developer conference in Beijing.
The announcement follows closely after the Cyberspace Administration of China (CAC) launched an inquiry into NVIDIA, based on what it called “serious security issues” involving Nvidia’s processors and demands from US lawmakers to add tracking features to chips’ hardware.
The regulatory scrutiny adds another layer of complexity to an already strained relationship between the two superpowers.
CUDA’s monopolistic grip on AI development
To understand the significance of Huawei’s move, it’s important to examine NVIDIA’s CUDA dominance. CUDA, often described as a closed-off “moat” or, on occasion, “swamp,” has been viewed by some as a barrier for developers seeking cross-platform compatibility.
Its tight integration with Nvidia hardware has locked developers into a single vendor ecosystem for the last two decades, with all efforts to bring CUDA to other GPU architectures through translation layers being blocked by the company. It’s added provisions to its CUDA licence agreement that prevent developers from running CUDA on third-party GPUs via translation layers.
Many Chinese AI developers use Nvidia’s GPUs partly because of the CUDA platform, which has been the default development platform for years. This situation highlights the challenge Huawei faces in convincing developers to migrate to its ecosystem.
Industry analysis and market implications
Technology analysts have offered mixed assessments of Huawei’s open-source strategy. While open-sourcing CANN could help Huawei accelerate adoption of its in-house software toolkit and thereby its hardware, it will likely take years for CANN to match the ecosystem support of CUDA, which has been maintained continuously and refined over nearly two decades.
The competitive landscape reveals the magnitude of Huawei’s challenge. Even with open-source status, adoption may depend on how well CANN supports existing AI frameworks, particularly for emerging workloads in large language models and AI writer tools. The software ecosystem around CUDA includes thousands of optimised libraries and extensive documentation that took years to develop.
However, there are signs of progress in Huawei’s hardware, with several claims that certain Ascend chips can outperform Nvidia processors under specific conditions. Reports suggest that CloudMatrix 384’s benchmark results against Nvidia running DeepSeek R1 suggest that Huawei’s performance trajectory is closing the performance gap.
Building an alternative ecosystem
Huawei has, according to the South China Morning Post, begun discussions with major Chinese AI users, universities, research institutions, and business partners about contributing to an open-sourced Ascend development community. The collaborative approach mirrors successful open-source initiatives in other technology sectors, where community contributions accelerate development and adoption.
Global chip war context
The open-source CANN initiative fits into China’s technology independence. The country’s open-source drive is gaining momentum, with more domestic tech companies working to make their proprietary technologies publicly accessible. Recent examples include Xiaomi’s open-sourcing of its MiDashengLM-7B audio large language model and Alibaba’s release of the Qwen3-Coder AI coding model.
This is all happening against the backdrop of ongoing US export restrictions targeting Chinese technology companies. In the current environment, where US restrictions affect Huawei’s hardware exports, building a robust domestic software stack for AI tools becomes as important as improving chip performance.
Expert scepticism and challenges ahead
Raw performance alone will not guarantee developer migration without equivalent software stability and support. The challenge extends beyond technical capabilities to include documentation quality, community activity, and integration into development workflows.
The road ahead
The implications for the global semiconductor industry remain significant. As the US-China technology competition intensifies, Huawei’s open-source strategy represents a shift from competing on proprietary platforms to building collaborative ecosystems that could reshape how AI software development evolves globally.
Whether this initiative will successfully challenge NVIDIA’s dominance remains to be seen, but it certainly marks a new chapter in the ongoing battle for control over the AI computing infrastructure that powers the next generation of technological innovation.
See also: Alan Turing Institute: Humanities are key to the future of AI
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