Short Description
This GPU Cluster at the University for Continuing Education Krems (Center for Cultures and Technologies of Collecting; Department for Arts and Cultural Studies) provides, as part of the DHInfra.at project, a novel infrastructure for computationally intensive research in the field of Digital Humanities (DH). The cluster comprises of a high-performance computing node with six H200 as well as a login and storage node with 40 TB of storage.
The facility supports both training and inference for demanding (data-intensive, complex) applications in humanities research, particularly for natural language processing and computer vision. Primary application areas include automatic text recognition (OCR/HTR), text analysis of historical corpora, and AI-supported analysis of cultural artifacts. The nodes are interconnected via InfiniBand network (400 Gb/s) for optimal data transfer.
The central login node enables federated authentication through Shibboleth, providing standardized access for researchers (and students) from the CLARIAH-AT consortium, associated institutions, and external partners. The infrastructure supports distributed computing and container-based workflows for scalable research projects. The infrastructure serves users with varying technical backgrounds, offering both interactive Jupyter environments and batch processing for larger computations.
The system specifications are expandable at both node and cluster level. The cluster is integrated into the distributed DHInfra infrastructure, enabling coordinated resource utilization. The system is integrated into European research infrastructures CLARIN and DARIAH via CLARIAH-AT and operates with professional support.
More information is available at https://www.dhinfra.at
Contact Person
Max Resch
Research Services
GPU computing for ML model training and inference
LLM inference APIs as managed services
Interactive Jupyter environments with GPU access
Container-based environments and batch scheduling
Federated access via eduid.at (SAML2) for CLARIAH-AT partners and associated institutions
Methods & Expertise for Research Infrastructure
The focus of this infrastructure is applying machine learning methods for humanities research. Focus areas include automatic transcription of historical documents, natural language processing and computer vision, to name a few. The Department of Arts and Culture Studies developed expertise in running GPU Cluster operations. The services infrastructure supported by IT Services of the University for Continuing Education Krems and ACOnet.
TU Wien
University of Salzburg
University of Innsbruck
University of Applied Arts Vienna
University of Vienna
Austrian National Library
Austrian Academy of Sciences