NVIDIA has introduced DSX POD, a reference design for NVIDIA Vera Rubin AI factories integrated and validated in modular containers before delivery. Deployment can take 14 days, compared with six months for a traditional onsite build. The units require a prepared site with power, cooling, networking and other supporting infrastructure.
The design moves computing, networking, cabling, rack-level power and liquid-cooling integration and installation to the manufacturing facility. Partners complete rack-to-rack cabling, wiring, piping and functional validation before shipment, separately from site preparation.
“An AI factory creates value when it starts producing tokens – not when construction is complete,” said Jensen Huang, founder and CEO of NVIDIA. “With NVIDIA DSX POD, we’re bringing factory-built precision and speed to AI infrastructure construction – reducing deployment time from months to weeks so customers can scale capacity and bring revenue-generating production online faster.”
DSX POD follows the design philosophy of NVIDIA’s DSX infrastructure platform, using purpose-built containers. Factory testing reduces exposure to component delays and onsite installation errors, shortening the path to model training and production inference.
NVIDIA is working with Anduril, Armada, Bull, Crusoe, DayOne, Dell Technologies, Delta, Eaton, Foxconn (Ingrasys), GIGABYTE, HPE, Lenovo, Pegatron, Quanta Cloud Technology (QCT), Schneider Electric, Vertiv and Wistron to deliver DSX POD AI factories worldwide in early 2027.
Source: NVIDIA
About NVIDIA
NVIDIA, founded in 1993 and headquartered in Santa Clara, CA, designs and manufactures graphics processing units, systems on chips, networking hardware, and AI intelligence software such as CUDA. Its products serve industries including gaming, data centers, autonomous vehicles, professional visualization, robotics, health care, and energy. The company introduced the GPU in 1999 and later expanded into accelerated computing and AI infrastructure. In gaming, its GPUs support high-performance rendering, while in AI and high-performance computing, its systems provide the infrastructure for training and deploying large-scale models. NVIDIA also develops tools for robotics and autonomous driving.
