Why China’s First AI Large‑Model Private‑Deployment Standard Matters
China’s first group standard, the “AI Large Model Private Deployment Technical Implementation and Evaluation Guide,” has been launched to address the technical, security, and cost challenges of privately deploying large AI models, offering a unified framework for model selection, deployment, optimization, and multi‑party collaboration.
Background
Artificial intelligence large models have opened a new stage of AI development. In China, rapid progress and industry penetration make private deployment the core path for efficient model use.
Private deployment involves server configuration, software environment adaptation, hardware procurement, licensing and manpower costs. Without a unified technical framework and evaluation system, enterprises face chaotic technology selection, mismatched compute resources, security vulnerabilities and compliance risks.
To address these issues, the “AI Large Model Private Deployment Technical Implementation and Evaluation Guide” group standard has been initiated.
Why the Standard Is Needed
The standard aims to provide a cross‑domain guideline covering key technical standards and security/governance requirements, supporting high‑quality AI industry development and the “AI+” empowerment demand.
Three Main Highlights
1. End‑to‑End Process “Selection + Deployment + Optimization”
The standard unifies the whole workflow from model selection, resource planning, key steps, quality evaluation to continuous optimization, improving efficiency and reliability of private deployment.
Selection stage: basic principles, industry consensus and selection process to help enterprises choose appropriate models.
Deployment stage: guidance on deployment methods, resource planning, integration strategies to ensure scientific and normative deployment.
Post‑optimization stage: key points for ongoing operation to maintain performance, quality and security.
2. Deep Integration of “Technology + Security + Evaluation + Case Studies”
Technical implementation is the core, security is a prerequisite, quality evaluation ensures assurance, and industry cases provide references.
Technical implementation: comprehensive methods and process guidance for the whole private‑deployment lifecycle.
Security and confidentiality: strict protection of data, model parameters and generated content, complying with national regulations.
Evaluation dimensions: a scientific framework and indicator system to objectively assess functionality, performance, security and compliance.
Industry cases: collected from enterprises and experts to reflect real‑world needs.
3. Three‑Party Collaboration Model
Model users, technical service providers and quality evaluators cooperate to build a foundational framework that reflects industry reality and market demand.
Model users: enterprises and institutions that have deployed private large models in specific sectors share use cases and requirements.
Technical service providers: AI model developers, hardware vendors and cloud providers exchange deployment insights.
Quality evaluators: organizations with AI performance testing, compliance, data‑privacy, legal and sustainability expertise assess the solutions.
Standard Architecture
The guide is organized into sections on model selection, deployment strategy, implementation process, evaluation dimensions, evaluation methods and continuous improvement, each providing detailed steps, criteria and reference metrics.
Call for Drafting Units and Contributors
The Zhihé Standards Center invites enterprises, certification agencies, research institutes, investment institutions and professionals with influence in private deployment to become drafting units or contributors for the guide.
Participating Units (selected)
Public Security Ministry Third Research Institute
China Electronics Information Industry Development Research Institute (CCID)
National Medical Products Administration Information Center
Xinhuaxin (Dalian) Digital Technology Co., Ltd.
Institute of Computing Technology, Chinese Academy of Sciences
Jijia Inheritance Innovation (Beijing) Technology Co., Ltd.
Beijing Yuanshan Intelligent Technology Co., Ltd.
Guangxi Yingxun Logistics Co., Ltd.
Huanwang Technology (Guangzhou) Co., Ltd.
Institute of Aerospace Information Innovation, Chinese Academy of Sciences
Ant Group Co., Ltd.
Inspur Software Technology Co., Ltd.
Hangzhou Daishu Technology Co., Ltd. (Kangaroo Cloud)
Shaanxi XuanShu Chain Network Technology Co., Ltd.
Shanghai Zhihé Network Technology Co., Ltd.
Beijing Zhihé Network Technology Co., Ltd.
More units are being confirmed…
Next Steps
The draft of the standard is completed. Organizations interested in co‑researching AI large‑model private deployment are encouraged to leave contact information for follow‑up.
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