Jacobs to Enhance Data Centers with NVIDIA's AI Digital Twin Blueprint
Jacobs is advancing data center optimization through the use of NVIDIA's Omniverse Blueprint for AI Factory Digital Twins, announced in a press release. This collaboration aims to improve the design, simulation, deployment, and operations of AI factories by creating digital twins that simulate facility equipment efficiency, throughput, and resiliency.
The blueprint will integrate power, cooling, and network ecosystems, allowing engineering teams to design and optimize factories within virtual environments. This approach enables early detection of potential issues and the creation of more reliable facilities. Jacobs has a history of using digital twin technologies in sectors like water and transportation, and this initiative marks a significant step in applying these technologies to AI data centers.
The collaboration with NVIDIA will allow Jacobs to test and enhance the end-to-end blueprint workflow, ensuring accurate simulations and smarter facility management. This initiative is part of Jacobs' broader efforts to address complex production load challenges globally, including projects in Portugal, the U.S., and Australia.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Silicon Brief or Daily AI Brief.
Also, consider following us on social media:
More from Data Centers
Sep 19 Virginia Proposes Data Center Rules and Creates AI Task Force Sep 19 House Passes Bill to Shield Ratepayers From Data Center Power Costs Sep 19 Laminar Joins L'Oreal Sustainability Accelerator Sep 19 Dnotitia Begins Testing VDPU ASIC Samples Sep 19 Nscale Files for US Initial Public OfferingSilicon Brief
Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Lancium and NVIDIA Partner on AI Factory Campuses
Saturn Cloud Integrates NVIDIA Run:ai for GPU Inference Services
Aramco Digital and Avathon Partner on Industrial AI
Huawei Unveils Grid Interactive AI Data Center Solution
SCX.ai and DDN Partner on Australian Sovereign AI Inference Cloud
Daily AI Brief: the AI news that matters, in your inbox.