As digital methods advance rapidly, quantitative empirical research requires greater computing power. This includes complex statistical analyses, model training, and operating generative AI models. The necessary hardware is expensive and challenging to maintain, particularly at the institutional level (e.g., due to high temperatures in HPC clusters). Since not every institution can (or should) set up such hardware independently, resources are shared through collaborations with other institutions.
Against this background, the Weizenbaum Institute is exploring options to enable both low-demand computing tasks (e.g., a virtual machine for background web scraping) and high-demand tasks (e.g., running current large language models) for its researchers. Recently, the following options have been identified (some of which are only available to Berlin scientists):
Datenzentrum Berlin (Berlin University Alliance)
- Not established yet, but planned
de.NBI Cloud (Deutsches Kompetenzzentrum Clouddienste)
- free to use with scientific project proposal: https://datenkompetenz.cloud/
- “Self service” virtual machines
- currently trying to support digital humanities and social science
FUB-IT (Freie Universität Berlin)
- Free to use (FU account required; guest accounts are available)
- High-performance cluster (HPC)
- Software partially included, some can be requested
- Up to very large scale projects (both HPC and AI) on proposal in different calls: https://www.gauss-centre.eu/for-users/hpc-access
- Several equipment up to supercomputers SuperMUC I and II usable
Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG):
- Very huge HPC and AI resources on project proposal: https://docs.hpc.gwdg.de/start_here/nhr_application_process/index.html
- Free 75k CPU/h (300k CPU/h on request) per quarter
- AI resources by KISSKI, up to 4 nodes on project prosposal: https://docs.hpc.gwdg.de/start_here/kisski_application_process/index.html
- Community-driven web-based analysis platform for life science research
- Free to use batch system with many tools usable by browser, huge data sizes are no problem
- Although coming from Bioinformatics, there are many tools for text processing, audio and image analyses, as well as statistics can be put together in pipelines.
HPI (Hasso Plattner Institut) UP Potsdam
- Good AI resources: https://aisc.hpi.de/portal/cfp/, usable with project proposals in specific calls: https://hpi.de/forschung/hpi-datacenter/ki-servicezentrum/#c2889
- Consulting/help can be gained on complicated setups
Zentraleinrichtung Campusmanagement (TU Berlin)
- Free to use (TUB account required)
- High-performance cluster
- Software partially included, some can be requested
- Support from the state of Berlin, linked to BUA, e.g. BI-FOLD also calculates there
- Provides tailor-made solutions, e.g. can make own instances of HAWKI, LibreChat or similar available with WI licenses
- Resources available only by collaboration.
Apart from these external offers, the Weizenbaum Institute itself already provides some services to its researchers:
- Virtual machines
- Jupyterhub
- Gitlab
Researchers who are interested in using any of these services can follow the instructions on the according websites for external services. For internal services, instructions are provided in the internal WI Wiki.
Disclaimer: This list will be updated regularly. If you know of any other resources that are available to members of the Weizenbaum Institute, please let us know.