国产AI算力硬件开源技术手册(V1.0)

摘要 当前全球AI算力发展受限于芯片热密度、存储带宽、芯粒集成、集群互联四大物理工程瓶颈,海外算力生态形成较强的技术与产业链壁垒。国内芯片、存储、封装、高速互联各领域已经积累大量成熟技术基础,但各主体技术分散,缺少一套统一的顶层工程方案完成体系整合。本手册立足于现有国产产业底座,提出一套开放共享的硬件优化技术框架,分别从芯片热管理、存储同步架构、大规模芯粒集成封装、集群高速场互联四个模块给出设计思路、适配方案、预期技术指标与开源交付内容,提出国内多方产业主体协同合作体的组织模式与分工路径。本手册不绑定特定基础物理概念,只输出可用于工程研讨、原型验证的实施方案,旨在助力国内大模型训练硬件支撑体系建设,推动算力硬件技术自主迭代。 Abstract The development of global AI computing power is currently restricted by four major physical and engineering bottlenecks: chip thermal density, memory bandwidth, chiplet integration, and cluster interconnection. Overseas computing ecosystems have formed strong technical and industrial chain barriers. Although China has accumulated mature technologies in chips, storage, advanced packaging, and high-speed interconnection, technical resources are scattered among different enterprises and lack a unified top-level engineering system for integration. Based on the existing domestic industrial foundation, this manual proposes an open hardware optimization framework covering chip thermal management, memory synchronization architecture, large-scale chiplet integration packaging, and high-speed cluster field interconnection. It provides design principles, domestic adaptation schemes, expected technical indicators, and open-source deliverables, and establishes a collaborative mechanism for domestic industrial participants. Without relying on specific basic physical theories, this manual offers implementable engineering solutions and prototype verification directions, aiming to support the construction of domestic large-model training hardware systems and promote independent iteration of domestic AI computing infrastructure.