{"article":{"annotation":{"article_id":"dassault-vertiv-modeled-ai-factory-deployment-targets","available_at":"2026-10-25T01:05:49.858401Z","geography_revision":"ef6ee0010dda5fe7b240fd514749b43e214c57da73222d2e40ec83b276dd0977","id":"17caac8c53eab4ccabadd177e98825bd1f7e34d3c79f281b29c15d36efaf3946","input_sha256":"3fbf52e460dc8b0bc7f5a7e339d81d56d22aba1a05216d20a038ecf672e4859a","limitations":["Rule-derived subjects are candidates, not verified editorial labels or calibrated model probabilities.","Coverage is distinct from event location. Unknown country/city/coordinates remain absent; no automatic geocoding.","Article geometries keep their stated precision and methodology; reproducing them is not independent coordinate verification."],"method":"controlled-rules-v1","places":[{"admin1":null,"city":null,"coordinate_note":null,"coordinate_reference_system":"OGC:CRS84","coordinate_source":null,"country":"FR","evidence":[{"field":"country","language":"en","note":"Explicit preserved editorial country/region; no city or national centroid inferred","source_urls":["https://www.3ds.com/newsroom/press-releases/dassault-systemes-and-vertiv-team-accelerate-ai-factory-design-and-deployment"]}],"geometry":null,"id":"iso3166-1:FR","kind":"country-or-area","labels":{"ar":"فرنسا","en":"France","es":"Francia","fr":"France"},"match_status":"candidate","precision":"regional","role":"coverage","uncertainty_m":null}],"rules_sha256":"fb5ad3c27c91cb840f7a0a05f7f64a5cb26ad0374409ff9457f277c7092e22b4","schema_version":1,"subjects":[{"concept_id":"medtop:20001298","confidence":null,"confidence_method":null,"evidence":[{"field":"en.title+en.summary","language":"en","note":"Subject suggested from the title or summary: AI","source_urls":["https://www.3ds.com/newsroom/press-releases/dassault-systemes-and-vertiv-team-accelerate-ai-factory-design-and-deployment"]}],"method":"controlled-rules-v1","rule_id":"artificial-intelligence","status":"candidate"},{"concept_id":"medtop:20000235","confidence":null,"confidence_method":null,"evidence":[{"field":"en.title+en.summary","language":"en","note":"Subject suggested from the title or summary: construction","source_urls":["https://www.3ds.com/newsroom/press-releases/dassault-systemes-and-vertiv-team-accelerate-ai-factory-design-and-deployment"]}],"method":"controlled-rules-v1","rule_id":"construction-infrastructure","status":"candidate"}],"taxonomy_revision":"1f2db31d811fca07e99b16d402c10e2cc2965c9df1c7b41d9be348b40a24e7fb","taxonomy_version":"IPTC Media Topics 2026-Q2"},"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=en","category":"technology","content":{"key_points":[],"paragraphs":["Dassault Systèmes and Vertiv announced the collaboration during NVIDIA GTC Berlin, extending work on Vertiv SmartRun, a configurable virtual twin, to wider AI factory infrastructure including Vertiv OneCore. OneCore is described as a hybrid-built data-centre solution integrating critical power, thermal management and supporting infrastructure. The announcement brings together Dassault Systèmes’ systems-engineering and simulation tools with Vertiv’s power and cooling designs, with the stated aim of assessing an integrated facility before construction.","The stated objective is to reduce complexity at the construction site and shorten the path from design kickoff to productive AI capacity. The release describes AI factories as linked systems in which power, cooling, networking, controls and computing affect one another. A change in one area can have consequences elsewhere, so the partners say engineers need to assess the integrated system before deployment.","The reference designs are based on NVIDIA’s Vera Rubin DSX AI Factory reference design and Omniverse DSX Blueprint. Dassault Systèmes supplies the 3DEXPERIENCE platform, model-based systems engineering, simulation and virtual-twin capabilities; Vertiv contributes configurable SimReady power and cooling assets and infrastructure expertise. The companies say those assets can be represented as system models and adapted to customer requirements. Their collaboration expands from the SmartRun virtual twin to solutions that include OneCore, connecting facility power, thermal systems and compute planning in one design process.","In the described workflow, engineers can compare configurations, propagate design changes across disciplines, run system-level and multiphysics validation, and generate engineering deliverables from a common environment. The model is intended to carry design intent through construction and commissioning and into later technology upgrades. This is relevant because a change to power or cooling can affect other parts of a densely coupled AI facility.","The target figures refer to different stages and measures: up to 10 times faster configuration and validation; up to a 50% reduction in the defined infrastructure deployment period; roughly 10 weeks earlier capacity readiness against Vertiv’s modeled conventional baseline; and up to 30% more cumulative token output in the first 12 months from project kickoff at the same installed capacity. Vertiv says the last difference comes only from earlier facility readiness, not from improved operating efficiency. These are scenario outputs based on the model and its assumptions, not measured customer results or guaranteed savings.","The release explicitly says the token-output difference comes solely from earlier facility readiness and does not represent improved operating efficiency or a measured customer result. The comparison therefore depends on the modeled baseline, project assumptions and definition of the deployment period. The percentages and timing should be read as scenario outputs supplied by Vertiv, not as independently validated forecasts or guaranteed savings.","At NVIDIA GTC Berlin, held from 20 to 22 October, the partners said they would demonstrate elements of the process, including how facility requirements inform infrastructure configuration and how teams can assess power and cooling constraints virtually."],"sections":[]},"corrections":[],"country":"FR","data":{"blocks":[{"description":{"ar":"أهداف واردة في إعلان الشركتين وتستند إلى سيناريو Vertiv النموذجي؛ وليست نتائج مقاسة لدى العملاء.","de":"Zielwerte aus der Mitteilung auf Grundlage des Vertiv-Szenarios; keine gemessenen Kundenergebnisse.","en":"Target figures in the companies’ announcement, based on Vertiv’s modeled scenario; not measured customer outcomes.","es":"Objetivos del comunicado basados en el escenario modelizado de Vertiv; no son resultados medidos en clientes.","fr":"Objectifs du communiqué, fondés sur le scénario modélisé de Vertiv; il ne s’agit pas de résultats clients mesurés.","pt":"Metas do comunicado baseadas no cenário modelado da Vertiv; não são resultados medidos em clientes.","tr":"Şirketlerin duyurusundaki, Vertiv’in modellenmiş senaryosuna dayanan hedef rakamlar; ölçülmüş müşteri sonuçları değildir."},"id":"dassault-vertiv-modeled-targets","items":[{"basis":"target","label":{"ar":"الحد الأقصى للانخفاض النموذجي في فترة نشر البنية التحتية","de":"Maximale modellierte Verkürzung des Bereitstellungszeitraums","en":"Maximum modeled reduction in infrastructure deployment period","es":"Reducción modelizada máxima del periodo de despliegue","fr":"Réduction modélisée maximale de la période de déploiement","pt":"Redução modelada máxima do período de implementação","tr":"Altyapı kurulum dönemindeki modellenmiş en yüksek azalma"},"measure":{"aggregation":"unspecified","frequency":"irregular","geography":"world","id":"ds-vertiv-modeled-deployment-period-reduction","periodRole":"announcement-date","unitCode":"percent"},"period":"2026-10-22","unit":"percent","value":50,"valueQualifier":"upper-bound"},{"basis":"target","label":{"ar":"الحد الأقصى للزيادة النموذجية في إجمالي الرموز خلال أول 12 شهراً","de":"Maximaler modellierter Anstieg kumulierter Token-Ausgabe in den ersten 12 Monaten","en":"Maximum modeled cumulative token-output increase in first 12 months","es":"Aumento modelizado máximo de tokens acumulados en los primeros 12 meses","fr":"Hausse modélisée maximale des jetons cumulés sur les 12 premiers mois","pt":"Aumento modelado máximo de tokens acumulados nos primeiros 12 meses","tr":"İlk 12 ayda modellenen en yüksek toplam token çıktısı artışı"},"measure":{"aggregation":"unspecified","frequency":"irregular","geography":"world","id":"ds-vertiv-modeled-token-output-increase","periodRole":"announcement-date","unitCode":"percent"},"period":"2026-10-22","unit":"percent","value":30,"valueQualifier":"upper-bound"}],"methodology":{"ar":"تنقل القيم أهداف الحد الأعلى الواردة في بيان 22 أكتوبر. وهي مخرجات سيناريو وليست نتائج مرصودة أو ضمانات. ويُنسب تقدير الرموز إلى نموذج Vertiv ويعكس الجاهزية المبكرة للمنشأة فقط، لا كفاءة التشغيل.","de":"Die Werte geben Obergrenzen aus der Mitteilung vom 22. Oktober wieder. Sie sind Szenarioausgaben, keine beobachteten Ergebnisse oder Garantien. Die Token-Schätzung wird Vertivs Modell zugeschrieben und beruht nur auf früherer Anlagenbereitschaft, nicht auf Betriebseffizienz.","en":"Values reproduce upper-bound targets stated in the 22 October issuer release. They are scenario outputs, not observed results or guarantees. The token estimate is attributed to Vertiv’s model and reflects earlier facility readiness only, not operating efficiency.","es":"Los valores reproducen objetivos máximos del comunicado del 22 de octubre. Son resultados de escenarios, no resultados observados ni garantías. La estimación de tokens se atribuye al modelo de Vertiv y refleja solo una disponibilidad anticipada, no eficiencia operativa.","fr":"Les valeurs reprennent les objectifs plafonds du communiqué du 22 octobre. Ce sont des sorties de scénario, pas des résultats observés ni des garanties. L’estimation de jetons est attribuée au modèle de Vertiv et reflète uniquement une disponibilité anticipée, non l’efficacité opérationnelle.","pt":"Os valores reproduzem metas máximas do comunicado de 22 de outubro. São resultados de cenário, não resultados observados ou garantias. A estimativa de tokens é atribuída ao modelo da Vertiv e reflete apenas a prontidão antecipada da instalação, não eficiência operacional.","tr":"Değerler 22 Ekim yayımlayıcı açıklamasında verilen üst sınır hedeflerini aktarır. Senaryo çıktılarıdır; gözlenmiş sonuç veya garanti değildir. Token tahmini Vertiv’in modeline atfedilir ve işletme verimliliğine değil yalnızca tesisin daha erken hazır olmasına dayanır."},"sourceUrls":["https://www.3ds.com/newsroom/press-releases/dassault-systemes-and-vertiv-team-accelerate-ai-factory-design-and-deployment"],"title":{"ar":"نتائج نمذجة مصانع الذكاء الاصطناعي","de":"Modellierte Ergebnisse für KI-Fabriken","en":"Modeled AI factory outcomes","es":"Resultados modelizados de fábricas de IA","fr":"Résultats modélisés des usines d’IA","pt":"Resultados modelados de fábricas de IA","tr":"Modellenmiş yapay zekâ fabrikası sonuçları"},"type":"metrics","updatedAt":"2026-10-25T00:41:26Z"}],"version":1},"data_downloads":[{"formats":{"csv":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets/data/dassault-vertiv-modeled-targets?lang=en&format=csv","json":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets/data/dassault-vertiv-modeled-targets?lang=en&format=json"},"id":"dassault-vertiv-modeled-targets"}],"data_types":["metrics"],"editorial_illustration":{"alt":"Conceptual illustration of technology, processors and connected information.","asset_id":"newsverum-v1-technology","avif_variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.avif","width":480},{"height":480,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-720.avif","width":720},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.avif","width":960},{"height":800,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1200.avif","width":1200},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.avif","width":1536}],"credit":"Newsverum","documentary":false,"height":1024,"kind":"symbolic-editorial-illustration","language":"en","scope":"topic","url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.webp","width":480},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.webp","width":960},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","width":1536}],"width":1536},"event_id":"event-0593dd8c-9223-55f6-b9e7-b9b012ec4922","event_period":"2026-10-22","event_period_basis":"event-or-reference-period","id":"dassault-vertiv-modeled-ai-factory-deployment-targets","image":null,"importance":0.6,"language":"en","modified_at":"2026-10-25T01:05:38+00:00","publication_basis":"publication-record","published_at":"2026-10-25T01:05:38+00:00","reading_minutes":3,"region":"world","section_illustration":{"alt":"Conceptual illustration of technology, processors and connected information.","asset_id":"newsverum-v1-technology","avif_variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.avif","width":480},{"height":480,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-720.avif","width":720},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.avif","width":960},{"height":800,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1200.avif","width":1200},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.avif","width":1536}],"credit":"Newsverum","documentary":false,"height":1024,"kind":"symbolic-editorial-illustration","language":"en","scope":"section","url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","variants":[{"height":320,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-480.webp","width":480},{"height":640,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-960.webp","width":960},{"height":1024,"url":"https://newsverum.com/assets/newsverum/illustrations/v1/technology-1536.webp","width":1536}],"width":1536},"slug":"dassault-vertiv-modeled-ai-factory-deployment-targets","sources":[{"checkedAt":"2026-10-25","name":"Dassault Systèmes","publishedAt":"2026-10-22","url":"https://www.3ds.com/newsroom/press-releases/dassault-systemes-and-vertiv-team-accelerate-ai-factory-design-and-deployment"}],"summary":"The collaboration will use virtual twins to configure and validate infrastructure before construction; outcome figures are scenario targets.","text":"The collaboration will use virtual twins to configure and validate infrastructure before construction; outcome figures are scenario targets.\n\nDassault Systèmes and Vertiv announced the collaboration during NVIDIA GTC Berlin, extending work on Vertiv SmartRun, a configurable virtual twin, to wider AI factory infrastructure including Vertiv OneCore. OneCore is described as a hybrid-built data-centre solution integrating critical power, thermal management and supporting infrastructure. The announcement brings together Dassault Systèmes’ systems-engineering and simulation tools with Vertiv’s power and cooling designs, with the stated aim of assessing an integrated facility before construction.\n\nThe stated objective is to reduce complexity at the construction site and shorten the path from design kickoff to productive AI capacity. The release describes AI factories as linked systems in which power, cooling, networking, controls and computing affect one another. A change in one area can have consequences elsewhere, so the partners say engineers need to assess the integrated system before deployment.\n\nThe reference designs are based on NVIDIA’s Vera Rubin DSX AI Factory reference design and Omniverse DSX Blueprint. Dassault Systèmes supplies the 3DEXPERIENCE platform, model-based systems engineering, simulation and virtual-twin capabilities; Vertiv contributes configurable SimReady power and cooling assets and infrastructure expertise. The companies say those assets can be represented as system models and adapted to customer requirements. Their collaboration expands from the SmartRun virtual twin to solutions that include OneCore, connecting facility power, thermal systems and compute planning in one design process.\n\nIn the described workflow, engineers can compare configurations, propagate design changes across disciplines, run system-level and multiphysics validation, and generate engineering deliverables from a common environment. The model is intended to carry design intent through construction and commissioning and into later technology upgrades. This is relevant because a change to power or cooling can affect other parts of a densely coupled AI facility.\n\nThe target figures refer to different stages and measures: up to 10 times faster configuration and validation; up to a 50% reduction in the defined infrastructure deployment period; roughly 10 weeks earlier capacity readiness against Vertiv’s modeled conventional baseline; and up to 30% more cumulative token output in the first 12 months from project kickoff at the same installed capacity. Vertiv says the last difference comes only from earlier facility readiness, not from improved operating efficiency. These are scenario outputs based on the model and its assumptions, not measured customer results or guaranteed savings.\n\nThe release explicitly says the token-output difference comes solely from earlier facility readiness and does not represent improved operating efficiency or a measured customer result. The comparison therefore depends on the modeled baseline, project assumptions and definition of the deployment period. The percentages and timing should be read as scenario outputs supplied by Vertiv, not as independently validated forecasts or guaranteed savings.\n\nAt NVIDIA GTC Berlin, held from 20 to 22 October, the partners said they would demonstrate elements of the process, including how facility requirements inform infrastructure configuration and how teams can assess power and cooling constraints virtually.","title":"Dassault Systèmes and Vertiv set modeled targets for AI factory design","topics":[],"translations":[{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=tr","language":"tr","url":"https://newsverum.com/tr/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=en","language":"en","url":"https://newsverum.com/en/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=ar","language":"ar","url":"https://newsverum.com/ar/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=fr","language":"fr","url":"https://newsverum.com/fr/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=es","language":"es","url":"https://newsverum.com/es/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=de","language":"de","url":"https://newsverum.com/de/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},{"api_url":"https://newsverum.com/api/v1/articles/dassault-vertiv-modeled-ai-factory-deployment-targets?lang=pt","language":"pt","url":"https://newsverum.com/pt/news/dassault-vertiv-modeled-ai-factory-deployment-targets"}],"url":"https://newsverum.com/en/news/dassault-vertiv-modeled-ai-factory-deployment-targets"},"version":"1"}