The University private cloud provides faculty members and researchers with advanced virtualization services to support research, development, and innovative teaching activities. In particular, the service offers:
Customizable virtual machines (VMs), which can be used as dedicated computing environments for running scientific applications, numerical simulations, project-specific web services, experimental software, or development and testing environments;
Kubernetes clusters designed for use cases that require scalability and orchestration of containerized applications, such as data analysis and machine learning pipelines, virtual laboratories for teaching, distributed services, and complex scientific workflows.
The goal of the service is to enable the rapid deployment of reliable and secure IT infrastructures, eliminating the costs and constraints associated with managing dedicated hardware, while ensuring full data sovereignty and compliance with University policies.
The University private cloud infrastructure makes it possible to:
- Significantly reduce the startup time of research projects;
- Ensure high levels of data security, reliability, and compliance;
- Optimize costs compared to commercial solutions or public cloud services;
- Scale resources on demand, adapting virtual machines to evolving project requirements over time;
- Reduce the need for departmental workstations and servers, simplifying local IT management;
- Experiment with new technologies (AI/ML, containers, advanced storage) in a controlled and supported environment.
The service is positioned as an enabling infrastructure for research, designed to support the evolving scientific needs of the Politecnico di Torino.
New resources are being activated to support advanced research activities. In particular, starting from the first quarter of 2026 (Q1 2026), next-generation GPUs will be available, including:
- NVIDIA L40S
- Intel Data Center GPU Max 1100
These resources will be integrated into the private cloud platform and made available for selected projects, based on scientific and computational requirements.
