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Baidu AI Cloud Public Sector China Translated from Chinese

Huangpu District Central Sci-Tech Innovation Zone

Huangpu District needed a practical public platform to support local large-model innovation, lower the barrier for enterprises to build vertical AI applications, supply domestic compute, and connect application incubation with talent development.

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The platform uses Kunlun P800 with 8-bit inference that preserves precision while improving efficiency.

Reported impact

Business outcome

Huangpu established a shared large-model service environment that helps local enterprises build applications faster, supports industrial clustering, and links application incubation with demonstration and AI talent cultivation.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Huangpu District's Central Sci-Tech Innovation Zone partnered with Baidu AI Cloud to build a vertical large-model enablement platform around a 'one platform, four centers' design. The technical base combines Qianfan AppBuilder, Qianfan ModelBuilder, Baidu's BaiGe platform, and a Kunlun P800-based domestic compute pool so local enterprises can access model-development tools, training services, and inference capacity without building their own stacks from scratch. Qianfan contributes the end-to-end toolchain for data management, annotation, training configuration, model governance, and service release, while the compute layer offers controllable domestic training and 8-bit inference. On top of that foundation, the district is incubating applications in fintech, healthcare, and city governance, while also running a public experience center and training programs.

Data flow

District enterprises access the public large-model platform, use Qianfan tools to manage data and training, run workloads on the Kunlun-backed compute pool, and publish services or applications back into vertical finance, healthcare, and city-governance scenarios. Experience-center and training outputs feed the same ecosystem by showcasing results and developing talent.

Solution architecture

4 components · 2 layers
  1. Compute
    • Qianfan ModelBuilder Provides the model-development toolchain covering data management, training, governance, and service publication.
    • BaiGe Platform Supplies the AI infrastructure layer that coordinates large-model platform resources for the district program.
    • Kunlun P800 Provides the domestic compute pool used for controllable training and high-efficiency inference services.
  2. Orchestration
    • Qianfan AppBuilder Supports the application-development workflows for local enterprises building vertical large-model services.

Architecture clues

  • Qianfan provides the full workflow from data management through model governance to service release
  • The district built a domestic compute pool around Kunlun P800 for controllable public AI infrastructure
  • The initiative follows a 'one platform, four centers' architecture

Evidence from the source

Huangpu District and Baidu AI Cloud built a 'vertical large-model enablement platform' with a 'one platform, four centers' service system to drive AI applications in finance, healthcare, and city governance (original: 黄浦区中央科创区联合百度智能云打造"垂域大模型赋能平台"，构建"一平台四中心"服务体系，推动金融、医疗、城市治理等领域AI应用落地).
Kunlun P800 is used to build a domestic compute pool, shorten training cycles, and provide 8-bit inference with no loss of precision (original: 基于昆仑芯P800构建国产化算力池…大幅缩短训练周期，提高训练效率；8bit推理，可提供精度无损的推理服务).
Qianfan offers an end-to-end toolchain from data management and annotation through training configuration, model governance, and service release (original: 千帆大模型平台提供从数据管理、数据标注、训练配置、模型纳管到服务发布的全流程工具链).
The solution uses the Qianfan large-model platform and Baidu BaiGe platform to build a domestic compute foundation, application-development capability, and ecosystem integration (original: 百度智能云千帆大模型平台、百度百舸平台，通过算力基础设施创新、大模型应用开发及生态资源整合).
Transparency Evidence & methodology 85/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Huangpu District Central Sci-Tech Innovation Zone customer story

    First-party
    https://cloud.baidu.com/customer/case/huangpu.html
    Source type
    Vendor-published customer story
    Published
    Jun 19, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jun 19, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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