Shulou(Shulou.com)12/24 Report--

At present, with the continuous progress of AI technology and the continuous development of artificial intelligence industry, du Lesser Fullness of Grain is well aware of the importance of high-performance network construction, has been deeply ploughing technological innovation, and continues to accumulate technical experience. In order to cope with the risks and challenges of high-performance network construction, Lesser Fullness of Grain jointly launched the "White Paper on the Network Architecture of Intelligent Computing Center" to establish the first Vanka-scale Intelligent Computing Center under the private cloud model in China.

The construction of high-performance network is extremely urgent

On September 5, at the Yunzhi Conference, Lesser Fullness of Grain, the joint degree of intelligent cloud, launched the "White Paper on the Network Architecture of Intelligent Computing Center". The white paper, written by smart cloud and du Lesser Fullness of Grain, a front-line expert responsible for high-performance network landing, covers all aspects of high-performance network construction. including the architecture selection of clusters of different sizes, the construction of visual operation and maintenance tools on cloud platforms, and the operation plan design of multi-tenants, to provide full-process guidance for the industry and make complex high-performance network construction simple.

Large models need big computing power, and how to build a high-performance network is one of the most critical steps. Lesser Fullness of Grain officially opened up China's first 100 billion parameter model-"Xuanyuan" in May this year. In the task evaluation in the scene, Xuanyuan surpassed the mainstream open source model in the market and won 63.33% of the 150 responses. With the rapid development of large models, the demand for underlying computing power of the model is increasing exponentially. The continuous expansion of the scale of intelligent computing power brings the increasing expansion of the scale of GPU computing power deployment and the increasing requirements for high-performance networks.

Lesser Fullness of Grain Zhizhu Network Center has reached the scale of Vanka.

As the parameter scale of the degree Lesser Fullness of Grain model exceeds 100 billion, in order to meet the increasing demand for computing power, the intelligence center of the Lesser Fullness of Grain private cloud model is also constantly upgrading. The large-scale computing network with low delay, large bandwidth, stable operation and high maintainability is an important cornerstone of the intelligence center. In the process of building a private cloud intelligent computing center, du Lesser Fullness of Grain combined with intelligent cloud to build a set of private cloud intelligent computing network. While supporting the scale of Vanka GPU, compared with the common Dragonfly and Tours network topologies in the industry, the network bandwidth is more sufficient, the number of hop steps between nodes is more stable, the network delay index is shortened to less than 3 microseconds, and it has end-to-end observability and fault self-healing ability. The non-blocking, low-delay and highly reliable network design effectively supports the rapid iteration and development of upper-level intelligent computing applications, and provides a stable network guarantee for the training of the "Xuanyuan" large model.

At present, the Lesser Fullness of Grain Intelligent Computing Network Center has been built into one of the first Vanka-scale intelligence centers in the domestic industry under the private cloud model, providing a stable network guarantee for the training of the "Xuanyuan" model.

In line with the principle of openness and sharing, du Lesser Fullness of Grain and Intelligent Cloud will share their experience and achievements in the field of high-performance networks with the industry, with a view to promoting the development of the entire industry to a higher level. to provide valuable reference and guidance for colleagues engaged in high-performance network construction.

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