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Domestic computing power platform supports global AI technol
In depth integration with over 90 global mainstream open source communities, completion of "0day adaptation" and full chain optimization for more than 70 global mainstream models, and opening up 4000 card computing resources to developers throughout the year... The latest progress disclosed by Ascend Computing Ecology recently shows that domestic computing platforms are moving from "independent usability" to "ecological symbiosis". In the current era of rapid development of artificial intelligence, the collaborative evolution of computing power base and developer ecosystem has become a key variable determining industrial competitiveness.
Open source and openness build a solid foundation for AI technology implementation
The competition of artificial intelligence is essentially a competition of ecosystems. In the AI industry landscape of 2026, a single enterprise's technology closed loop is no longer able to meet the rapidly iterating market demand, and open collaboration has become an inevitable choice for industry development.
The technological evolution of Ascend CANN deeply confirms this trend. Zhang Liang, Vice President of Huawei Ascend Computing Business, introduced that since the first version was released in September 2018, CANN has undergone multiple iterations and achieved a leapfrog development from open low-level runtime capabilities to comprehensive open source. In 2023, Ascend C programming language will be officially released, and PyTorch will support Ascend synchronously; In 2024, the underlying runtime capabilities of CANN will be fully opened up, further reducing the entry barriers for developers; In 2025, vLLM will release its upgraded version simultaneously, Triton Ascend will be officially launched, and the pace of ecological adaptation will continue to accelerate; By 2026, with the release of the 950 chip, CANN will implement SIMT programming support, refine Cache Line granularity, and add core features such as SIMT+SIMD hybrid programming, Vector high computing power, and CCU dedicated communication engine to comprehensively improve architecture performance.
These technological upgrades are not isolated parameter stacking, but a systematic response to the computing power demand in the era of large models. Currently, mainstream AI scenarios such as big language models, multimodal generation, and search recommendations have raised higher demands for computational efficiency. ”Zhang Liang revealed that CANN supports low precision data formats such as mxFP4/mxFP8, significantly improving inference efficiency while ensuring model accuracy, and adapting to the core technical features of large models such as "large parameter scale and high computational density".
It is worth noting that CANN adopts a component layered decoupling approach in its architecture design, supporting flexible calling of upper level models and application plugins. This design concept of "making the bottom layer thicker and the experience lighter" not only reserves the ultimate performance tuning space for experienced developers, but also lowers the technical threshold for new entrants. With a complete debugging and tuning tool chain, developers with different abilities can find a suitable development path and achieve efficient innovation.
Industry experts believe that from an infrastructure perspective, Ascend has established a complete computing power base covering modules, cards, servers, supernodes, and supernodes clusters. Its rich product matrix provides solid hardware support for diverse scenarios from edge inference to cluster training, laying a solid foundation for the implementation of AI technology.
'Release and support' highlights ecological collaboration
The prosperity of the open source ecosystem directly determines the market vitality of computing platforms. Ascend always adheres to the principle of "breadth coverage" and "depth optimization" in the layout of the open source ecosystem, and continues to deepen collaborative cooperation with the global open source community.
Up to now, Ascend has achieved deep integration with more than 90 mainstream open source communities such as Triton, PyTorch, and vLLM. ”Zhang Liang revealed. At the level of model support, according to Zhang Liang, Ascend has implemented "zero day adaptation" and full link optimization for more than 70 mainstream large models such as DeepSeek series and Qwen series. '0day adaptation' means that on the day the model is released, the adaptation work for the Ascend platform can be completed, and developers do not need to wait for a long porting cycle, truly achieving mainstream models' out of the box use and optimal performance 'on Ascend. The implementation of this core capability relies on over 1500 basic operators and over 100 fusion operators pre-set by Ascend, covering mainstream AI scenarios such as large language models, multimodal models, and search recommendation models comprehensively, providing strong support for the rapid implementation of models.
The in-depth expansion of industry applications is also worth paying attention to. CANN collaborates with enterprise partners and universities to initiate SIG (Special Interest Group) for industries such as chemical engineering and power, promoting deep adaptation of specialized operators and domain wide model applications. Zhang Liang stated that this extension path from general to specialized is creating a benchmark "model room" for empowering traditional industries with digital transformation through independent computing power, allowing AI technology to truly enter production workshops from the laboratory and empower high-quality development of the industry.
Putting developer experience first
The consensus in the industry is that developers are the true creators of the AI era, and the ultimate test of the technology ecosystem is the actual experience of developers.
Ascend always puts developer experience first, "Zhang Liang emphasized. According to him, Ascend has lowered the entry barrier for the entire process from infrastructure to training and implementation; Supporting multiple operator programming paradigms, developers can flexibly choose based on performance or usability requirements.
The supply of computing power is the most fundamental requirement for developers and a prerequisite for innovation. Zhang Liang revealed that Ascend provides 4000 cards of computing power resources to the community throughout the year, ensuring that developers have sufficient experience space and continuous integration environment. The direct effect of this measure is to significantly reduce the cost of innovation trial and error for developers. For small and medium-sized teams as well as individual developers, this guarantee eliminates the lengthy process of environment deployment and the high cost of hardware procurement.
More forward-looking is the layout of the Ascend Agent system, which allows operator development to move from pure handwriting to automatic generation. Developers only need to describe the operator design intent, call the Agent capability to automatically configure the environment and generate code. ”Zhang Liang pointed out.
Industry insiders say that in the context of increasingly fierce competition in global AI technology, the ecological openness of computing power platforms is reshaping the development pattern of the industry. The practice of Ascend fully demonstrates that domestic computing platforms can not only achieve technological independence, but also integrate into the global innovation network with an open attitude, occupying an active position in collaborative evolution. When more developers stand on this' solid foundation ', grow their own AI applications, and achieve their innovation dreams, the overall competitiveness of China's artificial intelligence industry will be built on a deeper and more solid development foundation.
(Editor in charge: He Xin)
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