Engineer 1 - Field Service

Vertiv shares fell 8.65% to $193.80 at about 11:46 ET on Thursday, even as the data-centre power and cooling supplier said a new deployment model could speed up AI projects. Using NVIDIA DSX, Vertiv modelled up to 30% more cumulative AI token output in a project's first year. Its scenario cuts design and validation from 30 days to three, and halves infrastructure deployment from 90 days to 45. That yields 317 productive days instead of 245. The gains are modelled, and actual results depend on execution. Polar Data Centers is applying the factory-built approach to the first 41-megawatt phase of its Herya campus in Norway. Vertiv's second-quarter adjusted operating margin was 22.6%, up 410 basis points year over year.

Dassault Systèmes and Vertiv Holdings Co. announced a collaboration to let AI factory infrastructure be configured, simulated and validated before construction begins. The companies aim to cut field complexity and speed up deployment of NVIDIA DSX AI factories. Vertiv's SimReady power and cooling designs will run on Dassault Systèmes' 3DEXPERIENCE platform as configurable virtual twins. The work builds on Vertiv's SmartRun twin and extends to solutions such as Vertiv OneCore. The companies target up to 10x faster progress from requirements to a validated design, and up to 50% less deployment time. In Vertiv's model, capacity comes online about 10 weeks sooner, yielding up to 30% more token output within 12 months. The gain comes only from earlier readiness, not a measured customer result. The companies are showing the approach at NVIDIA GTC Berlin, October 20-22.

Vertiv Holdings Co. and NVIDIA introduce AI Factory approach using NVIDIA DSX and Vertiv factory-built infrastructure. Published on 10/21/2026 at 08:50 am EDT S&P Capital IQ Vertiv Holdings Co. and NVIDIA introduced an AI factory approach that combines rapid configuration and virtual validation of pre-engineered reference architectures with factory-built deployment. The approach is designed to move AI factories from project kickoff to productive capacity faster and, under defined assumptions, generate up to 30% more cumulative token output within the first 12 months with the same installed capacity. The Vertiv and NVIDIA approach starts instead with pre-engineered AI factory reference architectures that can be rapidly configured to customer and site requirements, virtually validated before deployment, then translated into factory-built infrastructure. Teams can start with pre-engineered AI factory reference designs and rapidly configure them for specific compute, power, cooling and facility requirements in a shared virtual environment. The approach combines the NVIDIA Vera Rubin DSX AI Factory reference design and NVIDIA Omniverse DSX Blueprint, with Vertiv SimReady power and cooling assets, integrated with Dassault Systèmes' 3DEXPERIENCE agentic platform. Rather than developing an integrated infrastructure design from scratch, teams can configure and virtually validate proven reference designs against project requirements, interfaces and constraints. In the modeled scenario, this reduces the time from requirements to a configured and validated AI factory solution from approximately 30 days to three days, a 10x improvement. Vertiv is also contributing SimReady OpenUSD assets of its power and cooling equipment to SimReady Central Library on Hugging Face. The library helps engineering teams discover and reuse validated equipment assets in AI factory digital twins, reducing asset preparation and helping teams evaluate infrastructure options during design. Once the AI factory configuration is validated, Vertiv prefabricated solutions shift more assembly, integration and testing into the factory before equipment reaches the site. These solutions include Vertiv? OneCore, a hybrid-built data center solution that integrates critical power, thermal management and supporting infrastructure, and Vertiv? SmartRun, a prefabricated overhead infrastructure system for high-density power distribution, liquid cooling, networking and containment. By moving more infrastructure work off-site, teams can reduce field complexity and on-site integration requirements. In the modeled scenario, the factory-built approach reduces the infrastructure deployment period from approximately 90 days to 45 days, a reduction of up to 50%. Combined with the faster configuration and validation enabled by pre-engineered reference designs, the modeled approach reduces these stages from approximately 120 days to 48 days. That can make productive AI capacity available about 10 weeks sooner, generating up to 30% more cumulative token output within 12 months of project kickoff with the same installed capacity. (C) S&P Capital IQ - 2026