Shenyang needed a shared AI and digital-infrastructure foundation that could support regional digital-government goals and help local industries adopt compute, large models, and AI services without every organization building isolated capabilities on its own.
Reported impact
Shenyang established a reusable public AI base that can support regional industrial transformation, attract ecosystem partners, and accelerate adoption of large-model services across multiple strategic sectors.
Baidu AI Cloud and Shenyang's local development authorities built the region's intelligent computing center as a public AI foundation rather than a single application system. The program combines one industrial park, four centers, and multiple downstream scenarios so the city can support infrastructure, innovation, talent development, and industrial enablement from one coordinated base. At the technical layer, the solution brings together the data center, AI equipment, BaiGe heterogeneous compute capabilities, and the Qianfan large-model platform into an end-to-end stack. Those resources back an AI center cluster and feed the innovation, talent, and enablement centers that package the infrastructure into usable public services. In practice, the center gives Shenyang a common AI substrate for sectors such as the metaverse, industrial internet, cultural tourism, biomedicine, and aerospace, while also helping attract ecosystem partners into the regional digital economy.
Regional industry and public-service workloads enter the intelligent computing center, where shared data-center and AI-equipment resources are scheduled through BaiGe. Qianfan then supports large-model development and service delivery, and the resulting capabilities are exposed through the center's innovation, talent, and enablement programs to downstream sector applications.
Baidu AI Cloud and Shenyang's development zone co-built Northeast China's first intelligent computing center (original: 沈阳首府经开区携手百度智能云共建东北首个智能计算中心).
The center will provide a public AI large-model foundation for key local industries such as the metaverse, industrial internet, cultural tourism, biomedicine, and aerospace (original: 为元宇宙、工业互联网、文化旅游、生物医药、航空航天等沈阳特色重点产业的数字化转型和智能化升级提供 AI 大模型公共服务底座).
The four centers are the AI center, innovation center, talent center, and enablement center (original: 构建四大中心即为：AI 中心、创新中心、人才中心、赋能中心).
The project delivers '1 industrial park + 4 centers + N scenarios' (original: 落地“1 产业园 +4 中心 +N 个场景”).
The solution ultimately covers the data center, AI equipment, AI foundation, and Qianfan large model in one end-to-end product solution (original: 最终完成数据中心、AI 设备、人工智能底座、千帆大模型等端到端的产品解决方案).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Baidu AI Cloud Sample Oct 12, 2025 Guiyang Public Security Traffic Management Bureau (贵阳市公安交通管理局) Guiyang needed a more systematic way to reduce congestion in a mountainous city where road geometry, rapid growth, and fragmented traffic-management methods made it hard to optimize signal timing and respond to changing… Average vehicle delay at target intersections fell 15%. AI Traffic Signal Control Large Model · Qianfan Application Development Platform · Qianfan Model Development Platform Baidu AI Cloud Sample Sep 24, 2025 All-China Federation of Trade Unions The All-China Federation of Trade Unions needed a unified AI foundation that could support legal aid, rights protection, and other worker services at national scale instead of leaving union service digitization… Delivery cycle fell from 3-6 months to under 1 month. Qianfan AppBuilder · Qianfan ModelBuilder · Union Industry Model Baidu AI Cloud Sample Jun 23, 2025 Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) AIRS needed scalable compute, storage and scheduling infrastructure to tackle the algorithm, data and compute bottlenecks that were limiting embodied-intelligence research and deployment. End-cloud coordination latency was reduced to within 50 ms. BaiGe AI Heterogeneous Compute Platform · GPU Cloud Servers · High-Performance Computing Platform 