Baidu Intelligent Cloud Illuminates China's First Self-Developed 10,000-GPU Cluster, Ushering in a New Era of AI Computing Power

Tech 2025-02-05 09:36:39 Source: Network

Baidu Intelligent Cloud Illuminates China's First Self-Developed 10,000-GPU Cluster, Ushering in a New Era of AI Computing PowerRecently, Baidu Intelligent Cloud successfully activated China's first officially deployed Kunlun Xin 3rd generation 10,000-GPU cluster, with plans to further expand to a 30,000-GPU cluster. This marks a significant breakthrough for China in the field of artificial intelligence computing power

Recently, Baidu Intelligent Cloud successfully activated China's first officially deployed Kunlun Xin 3rd generation 10,000-GPU cluster, with plans to further expand to a 30,000-GPU cluster. This marks a significant breakthrough for China in the field of artificial intelligence computing power. This achievement not only provides a strong impetus for Baidu's own AI development but also brings new development opportunities to China's scientific and technological community, internet industry, and AI industry.

The establishment of the 10,000-GPU cluster not only provides powerful computing support but also drives down the cost of large model training. This has landmark significance for the entire AI industry, especially for companies that have been actively seeking to reduce the cost of using large models over the past year.

10,000-GPU Cluster: The Key to Computing Power Breakthrough and Cost Optimization

In today's rapidly developing artificial intelligence landscape, computing power has become a key limiting factor in AI applications. The training of large language models requires massive computing resources, and the shortage of computing power directly leads to high costs. Baidu, through its self-developed Kunlun Xin chip and the construction of a large-scale 10,000-GPU cluster, has effectively solved its own computing power supply problem and provided new solutions for the industry.

The advantages of the 10,000-GPU cluster lie in its ultra-large-scale parallel computing capabilities, which significantly improve training efficiency. Compared with traditional computing models, the 10,000-GPU cluster can greatly shorten the training cycle of hundreds of billions of parameter models, meeting the needs of rapid iteration of AI-native applications. More importantly, the 10,000-GPU cluster can support the training of larger-scale, more complex tasks, and multi-modal data, providing the necessary computing foundation for developing advanced AI applications similar to Sora.

Furthermore, the 10,000-GPU cluster boasts powerful multi-task concurrency capabilities. Through dynamic resource allocation, a single cluster can simultaneously train multiple lightweight models and reduce computing power waste through communication optimization and fault tolerance mechanisms, ultimately achieving an exponential decrease in training costs. With the booming rise of domestically produced large models, the application model of the 10,000-GPU cluster has also transitioned from the initial "single-task computing power consumption" to "cluster performance maximization." Through model optimization, improved effective training rate, dynamic resource allocation, and intelligent scheduling and hybrid deployment of training, fine-tuning, and inference tasks, the overall utilization rate of the cluster is further improved, reducing the unit computing power cost.

Baidu Baige Platform: Empowering the Performance and Stability of the 10,000-GPU Cluster

Building a 10,000-GPU cluster is not an easy task. In the past, challenges such as multi-chip mixed training and high failure rates have been major obstacles to the deployment of such clusters. Baidu's independently developed Baige AI heterogeneous computing platform 4.0 (referred to as the "Baige Platform") has played a crucial role in overcoming these challenges.

Baige Platform 4.0 has achieved breakthroughs in several areas: First, it has overcome hardware scalability bottlenecks, such as the topological limitations of inter-card interconnection, effectively preventing communication bandwidth from becoming a bottleneck. Second, to address the high power consumption issue of the 10,000-GPU cluster, the Baige Platform employs an innovative cooling solution, effectively resolving the energy efficiency and heat dissipation challenges of the 10,000-GPU cluster. Conventional solutions can consume tens of megawatts or more, while the Baige Platform's innovation significantly reduces power consumption. Third, the Baige Platform has improved distributed training optimization of models, employing a highly efficient parallel task decomposition strategy, increasing the cluster MFU of mainstream open-source models to 58%. Fourth, in terms of stability, the Baige Platform provides advanced fault tolerance and stability mechanisms, preventing the significant decrease in the effectiveness of the 10,000-GPU cluster due to the exponential increase in single-card failure rates with scale, ensuring an effective training rate of 98%. Finally, for inter-machine communication bandwidth requirements, the Baige Platform has built an ultra-large-scale HPN high-performance network, optimizing the topology and reducing communication bottlenecks, achieving over 90% bandwidth effectiveness.

Baige 4.0 has also built a 100,000-GPU-level ultra-large-scale HPN high-performance network. Addressing high latency issues in cross-regional communication, through optimized topology, multi-path load balancing strategies, and communication strategies, it has achieved cross-regional communication over tens of kilometers. In terms of communication efficiency, the Baige Platform, through advanced congestion control algorithms and collective communication algorithm strategies, has achieved completely non-blocking communication, and through ultra-high-precision network monitoring at the 10ms level, it has ensured network stability.

In multi-chip mixed training, the Baige Platform demonstrates strong resource integration capabilities, enabling unified management of heterogeneous computing power of different locations and scales to build a multi-chip resource pool. When a business submits a workload, the Baige Platform can automatically select chip types, selecting the most cost-effective chip to run tasks, maximizing the utilization of remaining cluster resources, and achieving up to 95% 10,000-GPU multi-chip mixed training efficiency. Furthermore, regarding cluster stability, the Baige Platform provides comprehensive fault diagnosis capabilities, capable of quickly and automatically detecting node failures causing abnormal training tasks. Baidu's self-developed BCCL (Baidu Collective Communication Library) can quickly locate failures and provide automated fault tolerance capabilities, reducing fault recovery time from hours to minutes, significantly improving cluster reliability and availability.

International Recognition: A Reflection of China's AI Technological Strength

Baidu's breakthrough in AI computing power has been recognized by international institutions. A recent research report released by Citibank points out that Chinese models such as DeepSeek and Baidu demonstrate high efficiency and low cost advantages, which will help accelerate global AI application development, trigger more technological innovation globally, and drive the inflection point of AI applications in 2025. Zheng Weimin, an academician of the Chinese Academy of Engineering and professor of computer science at Tsinghua University, also stated that building a domestically produced independent 10,000-GPU system is currently challenging but "crucially important."

The success of Baidu Intelligent Cloud in activating the 10,000-GPU cluster is not only a demonstration of technological strength but also a significant step for China in independent innovation and overtaking in the field of artificial intelligence. This indicates that China will occupy a more advantageous position in future AI competition and contribute to the global development of artificial intelligence. In the future, with Baidu's continued increase in R&D investment and technological innovation, the 10,000-GPU cluster is expected to further play its role, providing strong support for more AI applications, promoting the rapid development of artificial intelligence technology, and ultimately creating greater value for society.

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