Luxembourg, a key player in EuroHPC, is leading Europe in high-performance computing (HPC). Its AI Factory initiative aims to establish a secure, trusted infrastructure for AI development, supported by the AI-optimised supercomputer "MeluXina-AI." This supercomputer, with over 2100 GPU-AI accelerators, is designed to train and fine-tune AI models and integrate seamlessly with other AI Factories across Europe. Luxembourg's strategy, as outlined in its coalition agreement "Strengthening Luxembourg for the Future" (12/2023), focuses on AI, data, and quantum technologies. The government is committed to fostering an agile, trustworthy AI ecosystem involving national and international cooperation. The Luxembourg AI Factory (L-AIF) will provide end-to-end AI services, addressing key challenges for AI adopters and promoting cross-border collaboration. Its unique value proposition includes tailored solutions for start-ups and SMEs, a secure supercomputing platform, high-quality data sets, and comprehensive financial instruments. The L-AIF will focus on sectors like Finance, Space, Cybersecurity, and the Green Economy, leveraging local AI stakeholders and public research expertise. The L-AIF initiative is supported by a consortium of five members: LuxProvide, Luxinnovation, LNDS, LIST, and the University of Luxembourg. These entities will implement and operate the AI infrastructure, engage with private-sector companies, facilitate data access, conduct AI research, and support skill development. Their combined efforts will drive Luxembourg's digital transformation and contribute to Europe's competitive AI landscape

EnergyGuard aims to develop, kickstart and sustain an open, green and robust Testing Experimentation Facility operating under real-world conditions to empower innovators in bringing trustworthy AI products to the energy market in a cost-effective manner. It will integrate five significant European large-scale testing and experimentation facilities that cover the full energy value chain,supported by European’s greenest HPC infrastructure (Meluxina). This includes a digital twin (DT) of the Portuguese Transmission Network (RDN), the CEDER-CIEMAT Microgrid with its Distributed Energy Resources (DERs), the Hydrogen testing platforms at CEA LITEN, CARTIF, BER and CIEMAT, a high-fidelity local DT of Riga's multi-apartment residential buildings and the Antrodoco Renewable Energy Community. This includes a wide range of elements to cover diverse AI test needs,including wind power, photovoltaic systems, hydropower plant, AEM,PEM and SO eletrolysers, fuel cells, EV charging stations, electric and public buses and battery storage systems. The facilities will be accessible to EnergyGuard end-users through a set of properly configured Digital Twins (DTs) and curated assets, including data, models, inference APIs, services, and applications through a AI development Testing environment. It will enable easy seamless access to assets from the EU ecosystem including AIOD, Data Spaces, DIHs and other TEFs; Moreover, EnergyGuard facilitates users to validate their products with an Acceptance Environment and a common open AI risks database for a wide range of cybersecurity and trustworthy AI assessments. The TEF will serve as full infrastructure to support national AI regulatory sandbox initiatives and deliver 5 pilot cases for the private and public sector. EnergyGuard will build upon a long-term, self-sustainable business model driven by a new entity, incorporating market-ready features early in the design, such as a subscription/plan framework, billing, and professional support

SEANERGYS creates an integrated European software solution that optimises the operation of supercomputers. In doing so, it addresses four different objectives: reducing the amount of energy used for real-world workload mixes as the primary objective, optimising resource utilisation, enhancing system throughput and reducing response time as secondary objectives. Since these objectives can conflict with each other, site-specific policies define the weights attached to each, and the SEANERGYS SW suite will tailor system operation towards the combined optimum. Possible scenarios include improving the throughput of HPC systems, generating more R&D results for a given energy budget, or produce a fixed set of R&D results with less energy, while striving to keep response times constant. The solution consists of a comprehensive monitoring infrastructure (CMI), an Artificial Intelligence data analytics system (AIDAS), and a dynamic scheduling and resource management system (DSRM). The CMI gathers data from hardware and software sensors, and correlates it with scheduler information to identify jobs that do not fully utilize allocated resources. Users receive automatic feedback on energy and resource use for each run, plus information on how to optimize these. The DAIS leverages AI models trained with a vast set of operational data of the participating HPC sites. It fingerprints resource usage patterns, predicts future job behaviour, and identifies complementary job profiles for potential co-scheduling. Finally, the DSRM utilizes these insights to develop scheduling policies that maximize resource utilization and energy efficiency, and supports jobs/applications with dynamic and adaptable resource profiles. The SEANERGYS solution will be ready for deployment up to Exascale level. To ensure production-quality, the project builds on results from European projects, the competency of well-established research groups and companies, and widely used open-source codes. These are input for an integrated software system that achieves the functionality, performance and stability needed by European HPC centres, defined by KPIs and acceptance criteria and processes established at the project start. An agile, professional software development method will leverage a modern DevOps framework and strive to provide end-to-end traceability by linking and tracking requirements, interface, functional and performance specifications, code design and development steps, and validation/verification throughout the development lifecycle. Validation/verification measures will include code reviews, automated SW quality analysis, unit and integration tests and a verification suite. The project will implement a staged testing and validation process, with functionality tests on single-nodes, scaling tests on mid-sized platforms, and finally acceptance tests on production supercomputers.

Despite the success of Copernicus data, the European Earth Observation (EO) data market is only one-third of the size of the North American market. However, the market is expected to double over the next decade. Various sectors, such as insurance, food safety, environmental monitoring, and precision agriculture, are anticipated to capture most of the growth. In this context, DaFab has identified three primary challenges that must be addressed to leverage the full potential of Copernicus' information. Firstly, the timely analysis of EO data is critical for decision-makers to make informed decisions. To address this challenge, DaFab invests in novel hardware techniques dedicated to AI and federated computing techniques, which are capable of handling large high-resolution datasets and can enable real-time applications. Secondly, the massive amounts of Copernicus data make it challenging to identify the most relevant datasets for specific purposes, and the siloed nature of EO data further compounds this problem. To address this challenge, DaFab invests in semantic web techniques and public metadata catalogs to enable searching Copernicus images by features and relationships. Finally, the sustainability of analysis by-products is critical for efficient data management. To address this challenge, DaFab invests in cloud-computing techniques and public metadata catalogs, providing a unified solution for searching both raw Copernicus and by-products by features and relationships. By addressing these challenges, DaFab aims to unlock the full potential of Copernicus data and drive growth in the European EO data market.