AI 中文总结
本文介绍奥地利 DHinfra.at 项目中一个跨站点联邦 GPU 集群的设计与运行，其核心控制平面支持身份联盟、配额管理和两层模型服务，并在有限预算下为数字人文学科提供多种计算接口。
AI 中文摘要
我们描述了一个为奥地利 DHinfra.at 项目中的数字人文学科（DH）研究而构建的小型联邦 GPU 集群的设计、实现和运行。该系统跨越两个大学站点，通过国家身份联盟代理登录，并通过三种接口提供计算服务：交互式笔记本、SSH 和兼容 OpenAI 的推理 API。该平台的核心是一个控制平面，它将联邦身份、每个项目的配额和模型目录映射到自助服务接口。我们详细记录了该控制平面，包括调解每个特权集群效应的请求和审批队列，以及两层模型服务设置，该设置将常驻服务与按需模型交换相结合，使研究人员能够通过 HTTPS 使用大型语言模型，而无需持有集群账户。我们报告了影响构建的约束条件：固定的功率预算、欧盟范围内的采购、约 1.5 个全职等效人员的团队，以及优先访问与利用率之间的关系。我们还记录了主要的构建决策，以及我们研究过但未采用的替代方案。该平台在 Apache 2.0 许可证下发布。最后，我们为在有限预算下构建领域特定 GPU 集群的团队提供了一个操作手册。这是一份持续更新的技术报告，随着平台的发展而进行版本控制和更新。
英文摘要
We describe the design, implementation, and operation of a small federated GPU cluster built for Digital Humanities (DH) research within the Austrian DHinfra.at project. The system spans two university sites, brokers logins from a national identity federation, and exposes compute through three interfaces: interactive notebooks, SSH, and an OpenAI-compatible inference API. The core of the platform is a control plane that maps federated identity, per-project quotas, and a model catalogue onto self-service interfaces. We document this control plane in detail, including the request-and-approval queue that mediates every privileged cluster effect and the two-tier model-serving setup, which combines always-on services with on-demand model swapping and lets researchers consume large language models over HTTPS without holding a cluster account. We report the constraints that shaped the build: a fixed power budget, EU-wide procurement, a team of about 1.5 full-time equivalents, and the relationship between prioritized access and utilization. We also record the main build decisions against the alternatives we researched and did not adopt. The platform is released under the Apache License 2.0. We close with a playbook for teams building a domain-specific GPU cluster on a limited budget. This is a living technical report, versioned and updated as the platform evolves.
CommentsThis is an extended version of our contribution to ASHPC'26, Vienna, Austria
