In the fierce competition for global AI computing power, the Vanka cluster technology of domestic GPU manufacturer Moore Threads not only represents a leap forward in technology, but also a powerful challenge to the monopoly pattern of international high-end GPUs. In this article, China Exportsemi Net will try to analyze this technological breakthrough in depth and explore its far-reaching impact on the era of AI large models.
Moore Threads: The breakthrough of domestic GPUs
In the face of the shortage of NVIDIA A100 GPU, Moore Threads' Vanka cluster technology, with its advanced design concept and excellent performance indicators, provides new possibilities for the independent and controllable domestic computing power. According to the official data of Moore Threads, the total computing power of the Kua'e intelligent computing cluster has exceeded 10,000 P(PetaFLOPs), and the effective computing efficiency target has exceeded 60%, which is in a leading position in the world.
Vanka Cluster: A New Benchmark for AI Computing Power
The release of Moore Threads' Kuawanka Intelligent Computing Cluster marks a new benchmark for AI computing power. Compared with traditional GPU clusters, Moore Threads' clusters have achieved breakthroughs in multiple dimensions:
- Scale: The scale of a single cluster exceeds 10,000 cards, and the total computing power exceeds 10,000 pets.
- Efficiency: The effective computing efficiency of a cluster can exceed 60%, which is much higher than that of traditional clusters.
- Stability: The average weekly training efficiency can reach more than 99%, the average trouble-free operation is more than 15 days, and the longest stable training is more than 30 days.
Figure: Moore Threads Kwa Evanka cluster has achieved a further upgrade of the computing power of domestic clusters
The dual innovation of technology and ecology
Moore Threads' Vanka cluster technology not only achieves innovation at the hardware level, but also shows its far-reaching layout in the software ecosystem. According to Zhang Jianzhong, founder and CEO of Moore Threads, the Kua'e cluster has good CUDA compatibility capabilities, ecological adaptation to Instant On, and accelerates the Day0 migration of new models, which provides developers with an almost seamless migration experience.
Ecological construction of domestic computing power
Under the leadership of Moore Threads, the ecological construction of domestic computing power is accelerating. Domestic large-scale model companies such as Zhipu AI and Peking University Rabbit Exhibition have successfully run on the Kua'e cluster, which not only verifies the maturity of Moore Threads technology, but also provides a strong demonstration for the wide application of domestic computing power.
Challenges and opportunities coexist
The construction of Vanka cluster is a super complex system project, which involves technical problems such as ultra-large-scale network interconnection, high stability and high availability, and rapid fault location. The technological innovation and industrial collaboration capabilities demonstrated by Moore Threads in this process provide new ideas for the development of domestic computing power.
Epilogue
The release of Moore Threads Kua'e Intelligent Computing Cluster is not only a major breakthrough in the computing power level of domestic GPUs, but also a profound response to the computing power demand in the era of AI large models. In the global AI competition, the rise of domestic computing power will provide a solid foundation for the development of the domestic AI industry and promote China's transformation from following to leading in the global AI track.
With the continuous progress of domestic computing power manufacturers such as Moore Threads, we have reason to believe that domestic computing power will play an increasingly important role in the global AI field and contribute to the development of human intelligence. This technological breakthrough is not only a transcendence of the A100, but also a reshaping of the future AI computing power landscape.
