From the NVIDIA edition of October 21, 2026
Hasso Plattner Institute adds NVIDIA GB300 NVL72 to its AI data centre for researchers and students, announced at GTC Berlin
Hasso Plattner Institute (HPI), Potsdam · Higher education and research (privately funded university institute) · Potsdam, Germany
- Publisher
- Hasso Plattner Institute
- Original publication
- 2026-10-21
- Source retrieved
- 2026-10-22
- Event date
- 2026-10-21
What happened
HPI announced at GTC Berlin a major investment in partnership with NVIDIA to expand its AI data centre with GB300 NVL72 technology. Managing Director Daniela Gerd tom Markotten said HPI will be among the first research institutes in Europe to integrate the system, which will be used to analyse large scientific datasets, accelerate simulations and develop new models; researchers and students will have access for training and running large AI models. HPI hosted a keynote watch party where students discussed developments with an NVIDIA team on campus.
Why it matters
A university-level example of rack-scale AI hardware being opened to students as well as researchers, a pattern seen in prior editions (NJIT). Transferability to U.S. public institutions is limited: HPI is privately funded, and the release gives no size, cost, allocation or facility details.
Evidence and measured results
Institute's own short release; no figures on investment size, number of racks, power, cooling, allocation policy or timeline.
Limitations and uncertainty
No quantitative detail; 'partnership with NVIDIA' terms undisclosed; privately funded institution.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-22; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Universities want students and researchers to train large models on current hardware.
- Stakeholders
- CIO or research computing director
- Dean of computing
- Facilities
- Discovery questions
- Can the existing data centre support liquid-cooled rack-scale systems?
- What share of capacity would go to teaching?
- Value hypothesis
- Interpretation: shared research and teaching access may raise utilisation and skills; HPI reports no outcomes.
- Potential engagement
- Facility readiness and research-teaching allocation design for a rack-scale AI system.
- Unsupported claims
- Any cost or funding model
- Research or learning outcomes
Pre-sales engineering
Role takeaway
- Fit
- Institutions with facilities able to host rack-scale liquid-cooled systems.
- Architecture and integration
- Rack-scale system in an existing institute data centre; integration details undisclosed.
- Prerequisites
- Facility power and cooling capacity
- Scheduler supporting teaching and research
- Constraints
- Liquid cooling requirements
- No published sizing
- Security
- Student access requires account isolation and acceptable-use enforcement.
- Proof of value
- Facility assessment before procurement
- Pilot course on the system with usage tracking
Delivery
Role takeaway
- Work
- Install, onboard researchers and students, set allocation and support.
- Dependencies
- Facility upgrades
- Vendor delivery
- Allocation policy
- Ownership
- Institute IT or research computing.
- Skills and adoption
- Students and staff need training for large-model workflows.
- Governance checkpoints
- Allocation policy approval
- Acceptable-use policy for students
- Acceptance criteria
- Proposed: utilisation reported by research and teaching
- Proposed: number of students with completed training runs per term
- Risks
- Facility constraints delaying operation
- Teaching demand crowding research
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
GB300 NVL72 is a liquid-cooled rack-scale system; the release does not describe facility upgrades needed.
Governance
Who approves, reviews and stays accountable for outcomes?
Student access is stated without allocation rules.
Security and privacy
What data, permissions and controls need testing?
Not addressed.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Explicitly includes students, supporting hands-on training at scale.
Procurement
What should contracts, pricing and exit terms secure?
Partnership terms and pricing undisclosed, so not usable as a cost reference.
Operating model
Which teams own the service once it runs?
Institute-run AI data centre serving research and teaching.
Publication history
- 2026-10-21NVIDIA · Issue 469 resources
Stable resource ID: hpi-gb300-nvl72-ai-data-center-expansion-researchers-students-20261021