GB/Z 251-2026Artificial intelligence - Technical requirements of heterogeneous AI accelerating processor collaborative training in intelligent computing cluster (English PDF)
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Issued by
State Administration for Market Regulation; Standardization Administration of China
Level / Type
National · Mandatory
Issue date
August 27, 2026
Implementation date
January 1, 1800
Scope
GB/Z 251-2026 (Artificial intelligence - Technical requirements of heterogeneous AI accelerating processor collaborative training in intelligent computing cluster) is available as an English-translated PDF.
GB/Z 251-2026 — This document establishes the overall architecture for hybrid training of heterogeneous AI accelerators in intelligent computing clusters, and specifies the functions and performance of hybrid training. Require. This document applies to heterogeneous intelligent computing clusters composed of AI accelerators from different vendors. It also applies to AI accelerators from the same vendor but different models. This is a reference for heterogeneous intelligent computing clusters built with speed generators.
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Document preview — GB/Z 251-2026
National Standard of the People's Republic of China
- ICS
- 35.020
- Classification
- L 70
Issued by: State Administration for Market Regulation; Standardization Administration of China
Contents
- Preface
- 1.Scope1
- 2 Normative References1
- 3.Terms and Definitions1
- 4.Abbreviations1
- 5 Overall Architecture2
- 6.Functional Requirements of the Unified Heterogeneous Computing Network Collaborative Management and Scheduling Platform3
- 7.Functional and Performance Requirements of the Unified Heterogeneous Artificial Intelligence Accelerator Communication Library4
Foreword
This document is a standard or guiding technical document.
This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents".
Drafting.
Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents.
This document was proposed and is under the jurisdiction of the National Information Technology Standardization Technical Committee (SAC/TC28).
1 Scope
This document establishes the overall architecture for hybrid training of heterogeneous AI accelerators in intelligent computing clusters, and specifies the functions and performance of hybrid training.
Require.
This document applies to heterogeneous intelligent computing clusters composed of AI accelerators from different vendors. It also applies to AI accelerators from the same vendor but different models.
This is a reference for heterogeneous intelligent computing clusters built with speed generators.
2 Normative references
The contents of the following documents, through normative references within the text, constitute essential provisions of this document. Dated citations are not included.
For references to documents, only the version corresponding to that date applies to this document; for undated references, the latest version (including all amendments) applies.
This document.
GB/T 41867-2022 Terminology for Information Technology and Artificial Intelligence
3 Terms and Definitions
The terms and definitions defined in GB/T 41867-2022, as well as the following terms and definitions, apply to this document.
3.1
Note. In this document, without causing misunderstanding, the term "AI accelerator" will be used as a shorthand for "accelerator".
3.2
AI accelerators from different manufacturers.
3.3
In heterogeneous intelligent computing clusters, heterogeneous artificial intelligence accelerators are used to collaboratively complete the training task of the same model.
4 Abbreviations
The following abbreviations apply to this document.
DP. Data Parallel
EP. Expert Parallel
GDR. GPU Direct Remote Memory Access (GPUDirectRDMA)
END: Draft Version (GBZ251-2026)
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This preview omits tables, figures, formulas and parts of the technical clauses. The complete document — all pages — is available in the English PDF.
Referenced standards
Normative references
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