HEARTS

HElvetic AI Resources, Technologies and Services (HEARTS) is an AI Factory Antenna envisioning Switzerland as a leading contributor to European AI innovation. The core vision is to democratise access to AI-optimised compute and data on the Alps research infrastructure for diverse national and European users, including academia, startups, SMEs, and the public sector. This effort is enabled by the expertise of the ETHZ AI and EPFL AI Centres, and the Swiss AI Initiative, leveraging a newly developed and fully open LLM. It addresses two high-impact use cases: AI-driven weather forecasting and sensitive data applications in healthcare. The strategy aligns with Switzerland's "Digital Switzerland Strategy 2025", establishing secure, tiered data facilities. It fosters trustworthy and responsible AI, ensuring compliance with the EU AI Act. The SME engagement and talent development plan includes tailored training and fostering collaboration with universities to cultivate a skilled AI workforce by delivering specialised training courses to 100s of participants, growing the AI talent pool. The Antenna an integral node within the EuroHPC AI Factory ecosystem, maintaining a close partnership with the LUMI AI Factory (MoU signed) and collaborates (LoI) with the BSC, MIMER and IT4LIA AI Factories, promoting joint technical development and knowledge exchange for interoperability.

ETHZ CSCS

The Swiss National Supercomputing Centre (CSCS), has a strong track record in participating and coordinating large projects with multiple partners and has been a work package/task leader in major EU projects including ICEIFenix, HBP and PRACE-6 As a national HPC centre, CSCS is a pioneer in European Supercomputing. It hosts the Alps supercomputer that is at the forefront of the convergence of HPC and cloud technology, and is a driving force behind innovation in computational research in Switzerland. CSCS has a long-standing collaboration with MeteoSwiss, the national weather agency in Switzerland, and in addition to hosting and operating the infrastructure that is used for their weather forecasts, is actively exploring the concept of geodistribution and georedundancy for their operational workflows.

EPFL LiGHT

EPFL-LiGHT, the Laboratory for intelligent Global Health and Humanitarian Response Technologies at EPFL led by Prof Mary-Anne Hartley, develops responsible and contextualised AI solutions for underrepresented populations and healthcare systems. With a focus on foundation model safety, clinical trial integration, and open-source platform design (e.g., MOOVE, DISCO, Meditron), the lab hosts a global network of collaborators, including WHO, MSF, and ICRC. It brings deep expertise in multilingual and multimodal LLM evaluation and deployment in low-resource and real-world settings. LiGHT specialises in scaling the implementation of science approaches to ensure adoption through evidence engagement and clinical trials.

EPFL SCITAS

EPFL-SCITAS, Scientific IT & Application Support, provides the high-performance computing infrastructure at EPFL, supporting large-scale, secure, and privacy-sensitive workloads across scientific disciplines. As a central technology platform, SCITAS enables compute consolidation, petabyte-scale parallel filesystems, and scalable hybrid CPU/GPU clusters. SCITAS also supports new ML research communities with tools such as containerised environments and notebook-based access, in addition to facilitating integration with public cloud resources. It ensures professional-grade data protection and computational reliability.

EPFL MLO

EPFL-MLO, the Machine Learning and Optimisation Laboratory led by Prof. Martin Jaggi at EPFL, is internationally recognised for foundational work in optimisation, federated learning, and the development of open-access large language models (e.g., the Swiss Apertus LLM). MLO contributes expertise in scalable model training, optimisation and methodological innovation for interpretability, personalisation, and data-efficient model scaling.

ETHZ EASL

ETHZ Efficient Architectures and Systems Lab (EASL) led by Prof. Ana Klimovic designs and builds computer systems for large-scale applications, such as cloud computing services, data analytics, and machine learning. Their aim is to improve the performance and resource efficiency of cloud computing while making it easier for users to deploy and manage their applications. Their research work spans operating systems, computer architecture, and their intersection with machine learning.