Large-scale pre-training of Foundational Models (FM) constitutes a computationally intensive first phase for enabling AI across diverse scientific and societal applications. This first phase has positioned High-Performance Computing (HPC) facilities as indispensable backbones of "Sovereign AI" initiatives. While the massive throughput requirements of FM pre-training align with the traditional capability-oriented mission of HPC, subsequent phases of the AI lifecycle, typically referred to as fine-tuning and inference, introduce operational paradigms that can conflict with established batch-processing environments. Moreover, these phases are not computationally trivial: they often require substantial high-end compute resources while exhibiting hardware utilization patterns that differ significantly from those of pre-training. This paper addresses the architectural and strategic challenges of operationalizing a complete AI lifecycle within a national supercomputing facility. We present a hybrid cloud-native platform being developed and deployed at the Swiss National Supercomputing Centre (CSCS) that combines diskless GPU-enabled HPE Cray EX compute nodes with virtualized commodity infrastructure. Orchestrated by Kubernetes, this novel service architecture bridges the gap between HPC batch processing and service-oriented workflows. We report our initial investigations into fine-tuning pipelines and highly available inference services, analyzing the associated trade-offs while improving user productivity. Our findings offer a blueprint for enabling supercomputers to integrate "AI Factories" services and workflows, supporting AI innovations into end-to-end scientific and industrial use cases.

翻译：大规模预训练基础模型（FM）是推动人工智能在各类科学与社会应用中落地的计算密集型初始阶段。这一阶段使得高性能计算（HPC）设施成为"主权人工智能"计划不可或缺的基石。尽管FM预训练的海量吞吐需求与HPC传统的能力导向型任务相契合，但AI生命周期的后续阶段（通常称为微调和推理）引入的操作范式可能与既有的批处理环境产生冲突。此外，这些阶段在计算上并非微不足道：它们往往需要大量高端计算资源，同时展现出的硬件利用模式与预训练阶段存在显著差异。本文探讨了在国家超算设施中实现完整AI生命周期运营的架构与战略挑战。我们介绍了正在瑞士国家超算中心（CSCS）开发与部署的混合云原生平台，该平台将无盘GPU加速的HPE Cray EX计算节点与虚拟化通用基础设施相结合。在Kubernetes编排下，这一新型服务架构弥合了HPC批处理与面向服务工作流之间的鸿沟。我们报告了在微调流水线和高可用推理服务方面的初步探索，分析了相关权衡方案并提升了用户生产力。本研究成果为超级计算机集成"AI工厂"服务与工作流提供了蓝图，助力AI创新融入端到端的科学与工业应用场景。