Baidu recently introduced Baige 4.0, a new version of its AI Heterogeneous Computing Platform, which focuses on enhancing cluster stability and efficiency.
One of the standout features of Baige 4.0 is its ability to monitor GPU clusters, automatically detecting failures and migrating workloads to prevent disruptions. Furthermore, this system improves fault detection and localisation to address issues quickly and effectively, minimizing costly downtime.
Baige 4.0 also boasts an impressive 99.5% training efficiency for LLMs across tens of thousands of GPUs. This efficiency was achieved through improvements in cluster design, job scheduling, and VRAM optimization, leading to a 30% performance boost compared to industry averages.
The platform is now equipped to handle clusters of up to 100,000 GPUs, pushing the boundaries of AI training infrastructure.
Improved Model Inference and Use Cases
Baige 4.0 has made significant strides in model inference, particularly in long-text inference, where its efficiency has more than doubled. This improvement stems from advanced techniques such as architecture decoupling and load distribution. In addition, Baige’s application in real-world scenarios is gaining traction.
China Postal Savings Bank uses Baige to reduce model iteration time from 1.5 months to just half a day, accelerating AI implementation across its core business systems. Similarly, Changan Automobile employs Baige for autonomous driving model training, boosting computing power utilization and enhancing resource management.
Startups such as Shengshu Tech have developed its video generation tool, Vidu, based on Baige, positioning it as a local alternative to popular international models like Sora.
Qianfan 3.0 and Enterprise AI Advancements
In parallel with Baige 4.0, Baidu has upgraded its Qianfan Foundation Model Platform to version 3.0. This platform enables access to nearly a hundred large models, including ERNIE, and has significantly reduced model invocation costs, with price drops exceeding 90% for flagship models.
Qianfan 3.0 supports both large and small model development in domains such as computer vision, natural language processing, and speech, providing a comprehensive toolchain for enterprises.
Qianfan 3.0 also brings advancements in Retrieval-Augmented Generation (RAG), improving effectiveness and performance in enterprise-level applications. In addition, Baidu has launched “AI Suda,” a low-code development platform, allowing businesses to build applications through natural language dialogue, making enterprise-level AI accessible even for non-technical users.
Baidu continues to push forward in AI innovation, with Baige 4.0 and Qianfan 3.0 setting new standards for efficiency, model training, and enterprise application development.
Recently, Baidu rebranded its mobile ERNIE bot app to a new search smart assistant, Wenxiaoyan.
Baidu’s Chairman, Co-founder, and CEO, Robin Li, mentioned during the company’s Q2 earnings call that approximately 18% of its online search results are currently powered by AI, with expectations for this percentage to increase in the coming months.
Baidu’s advancements with Baige 4.0 and Qianfan 3.0 signal China’s growing influence in the global AI race, especially in terms of efficiency and large-scale infrastructure. By optimising GPU clusters and reducing costs for enterprises, Baidu is positioning itself as a formidable competitor to U.S. tech giants. As China pushes for AI dominance, Baidu’s innovations could reshape not only domestic industries but also challenge the established players in the international AI arena.
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