{"resourceId":"hyperion-scientific-computing-cost-value-roi-model-202610","versions":[{"version":"external-d4902eb0918c6156a6426258d33959d95a0e02e0db00eb6460b954fa0398eaaa","resource":{"id":"hyperion-scientific-computing-cost-value-roi-model-202610","title":"Hyperion Research previews a cost, value and ROI model to compare on-premises, cloud and hybrid scientific computing","organization":"Hyperion Research (HPC User Forum, HLRS)","sector":"Higher education and research computing","geography":"International (HPC User Forum, October 2026)","publishedAt":"October 2026","publicationDate":null,"eventDate":null,"sourceName":"HPC User Forum","sourceLabel":"Hyperion Research conference slides (PDF)","sourceUrl":"https://www.hpcuserforum.com/wp-content/uploads/2026/10/Mark_Nossokoff_Scientific_Computing_Cost_Value_and_ROI_Model_Hyperion_Research_Oct_2026_HPC_UF_dbe708aabd.pdf","evidenceClass":"independent-research","outcomeClass":"emerging","topics":["infrastructure","governance-procurement"],"finding":"Slides by Jaclyn Ludema and Mark Nossokoff describe an Excel-based decision-support model intended to compare TCO, societal value and ROI for on-premises, cloud and hybrid compute environments. They list hidden costs (software and licensing, power and cooling, networking, data egress, staff labour, training, floor space, overhead) and say early-stage projects lack precise workload or value figures. The model is not yet released: next steps are a name, a global survey of default costs and a spreadsheet for alpha and beta testing within 3–6 months.","sledRelevance":"Research computing directors and CIOs face AI-driven GPU demand and need a defensible way to compare environments. This signals an analyst-built tool in development, with no validation data yet.","evidence":"Opened the 11-slide PDF on 18 Oct 2026. Slides describe the model's structure and inputs; no results, case studies or default cost values are shown. The exact slide date is not stated beyond October 2026.","architectureImplications":"Frames on-premises versus cloud versus hybrid as a project-level decision depending on workload, software and data access, rather than a single institutional answer.","governanceImplications":"Slides note technical, budget and governance teams view TCO differently; a shared model is proposed as a common basis. Cost value claims remain unvalidated.","securityPrivacyImplications":"Not covered; data egress and data access appear only as cost or requirement items.","caveats":"Conference slides from an analyst firm that sells research to the HPC market; no outcomes, no method detail beyond a diagram, tool not yet available, default costs unspecified. Publication day unknown so publicationDate is null.","streamIds":["research"],"roles":{"sales":"Use this as a conversation opener, not proof. Hyperion says costs are fragmented and hidden across budgets, and that teams view TCO differently. The tool compares on-premises, cloud and hybrid options but is unreleased, with a survey and alpha spreadsheet still ahead. Interpretation: ask buyers how they currently justify GPU and cloud spending. These slides cannot support any claim about savings, ROI results or the model's accuracy.","engineering":"Useful as a checklist of cost categories to include in an architecture comparison: software, power and cooling, networking, egress, staff, training and space. Interpretation: build a project-level comparison for AI workloads using the customer's own numbers rather than waiting for the tool. Limits: slides give no default costs, formulas or validation, and the model's release is months away.","delivery":"The slides imply a delivery task: gather cost inputs held in different budgets and get technical, finance and governance teams to agree on one set of assumptions. Interpretation: assign a finance-and-research-computing owner and a review cadence for cost inputs. Limited applicability: this is an announced tool, not a deployed method, and no outcome or adoption data exist."},"roleDetails":{"sales":{"customerProblem":"Research computing costs sit in separate budgets, so on-premises, cloud and hybrid options are hard to compare and justify.","stakeholders":["Chief information officer","Research computing director","Vice president for research","Finance and budget officers"],"discoveryQuestions":["Which costs for research computing are tracked, and where?","How do you value time-to-solution for AI projects?","Who signs off on an on-premises versus cloud decision?"],"valueHypothesis":"A shared cost-and-value model may reduce disagreement among technical, budget and governance teams; the slides give no evidence of that effect yet.","potentialEngagement":"A short cost-baselining workshop using the customer's own CPU, GPU and storage figures.","unsupportedClaims":["That the Hyperion model improves decisions","Any savings or ROI figure","That the tool is available or validated"]},"engineering":{"fit":"Fits early-stage AI infrastructure planning where workload estimates are uncertain; the tool itself is not yet usable.","architectureIntegration":"Use the slide's cost categories as the template for a project-level TCO comparison across on-premises, cloud and hybrid.","prerequisites":["Workload estimates in CPU and GPU hours and storage","Software licensing and support costs","Power, networking and egress figures"],"constraints":["Default cost values not published","Early-stage workload uncertainty","Model release months away"],"security":"Not covered by the source; add data residency and access requirements separately when comparing environments.","proofOfValue":["Rebuild one recent project's total cost with the listed hidden-cost categories and compare to the budget as booked","Test sensitivity of the comparison to workload uncertainty","Check whether hidden costs change the preferred environment"]},"delivery":{"work":"Collect cost inputs across budgets and agree a single comparison method among technical, finance and governance teams.","dependencies":["Finance data access","Workload estimates","Executive sponsor"],"ownership":"Research computing director with a finance partner.","skillsAdoption":"Researchers and project managers need to describe workload characteristics and value in terms the model can use.","governanceCheckpoints":["Agree the cost categories and assumptions","Review inputs at project milestones","Record how value was estimated"],"acceptanceCriteria":["Proposed: cost comparison includes all listed hidden-cost categories","Proposed: assumptions are documented and signed off by finance","Proposed: workload uncertainty ranges are stated"],"risks":["Treating an unvalidated tool as authoritative","Subjective societal value figures dominate the result","Cost inputs left stale"]}},"retrievedAt":"2026-10-18T05:24:22-05:00","enrichedAt":"2026-10-18T05:24:22-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Staff labour, help desk and training are listed as hidden costs, which affects how research-support workloads are budgeted.","procurementImplications":"Supports comparing hardware purchase, cloud and managed services on a full-cost basis; no price data supplied.","operatingModelImplications":"Positions the compute-environment choice as a shared technical, budget and governance decision.","sourceVerification":{"openedUrl":"https://www.hpcuserforum.com/wp-content/uploads/2026/10/Mark_Nossokoff_Scientific_Computing_Cost_Value_and_ROI_Model_Hyperion_Research_Oct_2026_HPC_UF_dbe708aabd.pdf","referenceExcerpt":"Early-stage projects lack precise workload or value figures","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}