Overview

NVIDIA has opened its Vera Rubin rack reference designs to the wider industry, contributing rack and compute-tray specifications to the Open Compute Project through its MGX 1.2 reference design. The contribution landed at the OCP summit in San Jose on October 12, and the published designs support racks drawing up to 250 kilowatts — a figure that places them firmly in the multi-hundred-kilowatt class that has become the defining constraint of next-generation AI data centers. The designs pair an 800V power delivery architecture with optical interconnects, targeting both the electrical and the bandwidth bottlenecks that limit how much accelerated compute can be packed into a single rack. The move signals an accelerating industry transition away from traditional AC distribution toward high-voltage DC (HVDC) power at the rack level.

Technical Details: Inside the MGX 1.2 Specification

The published reference design is not a single product but a set of physical and electrical constraints that partners can build against. According to the specifications, the Vera Rubin rack designs include:

  • Support for racks drawing up to 250 kilowatts, well beyond the envelope of conventional air-cooled infrastructure.
  • Liquid-cooled busbars, moving current distribution into the same cooling regime as the compute itself.
  • Fanless compute trays, which eliminate on-tray air movers and shift the entire thermal load to the facility's liquid loop.

The combination is internally coherent: a fanless tray removes the last major air-handling dependency inside the rack, but it also removes the last margin for a facility that cannot deliver enough coolant flow — or enough power at the right voltage.

Power Delivery: The Shift to 800V DC

The most consequential element is the electrical architecture. Traditional data center distribution converts AC power repeatedly before it reaches silicon, and each conversion stage costs efficiency and copper. The reference designs point toward an 800V DC approach at the rack, and the ecosystem is already aligning behind it: Visionbay introduced its K-1 infrastructure using 800V DC power, Vertiv technology and NVIDIA reference designs. That combination of power architecture, cooling vendor and reference platform is precisely the kind of stack that must move in lockstep for high-density racks to become deployable. The source notes that substantial changes are needed for 800V DC and high-density liquid cooling — meaning the transition is not a component swap but a redesign of facility distribution, protection and cooling.

Optical Interconnects: USFP at 400 Gb/s per Lane

On the network side, NVIDIA, together with Cisco, announced USFP optical-module specifications supporting 400 gigabits per second per lane. As racks scale toward the densities described here, copper reach becomes a limiting factor, and optics migrate from the spine deeper into the rack and tray fabric. The specification matters because it gives module vendors a shared target to engineer against. It does not, however, resolve operational questions: the source identifies validation and maintenance work for optical modules and operating tools as outstanding items. Optical links add failure modes, cleaning and diagnostic requirements, and monitoring complexity that copper interconnects do not carry at the same scale.

Market Context: An Open Periphery Around a Proprietary Core

NVIDIA was not alone in the contribution. It joined AMD, Google and Meta on shared facility specifications, a signal that the physical constraints of AI infrastructure are becoming a collective problem rather than a competitive differentiator. The strategic logic is that common interfaces let equipment suppliers prepare compatible products in parallel, reducing dependence on customer-specific engineering. Critically, NVIDIA retains its proprietary core technologies, including CUDA and NVLink, while opening the supporting infrastructure. That approach could expand deployment capacity without giving up its central platform position — the moat stays around the software stack and the scale-up interconnect, while the rack, power and optical envelope converges into a shared standard.

Industry Impact: Facility Readiness as the Binding Constraint

The market significance lies in the infrastructure surrounding the accelerator. Power delivery, cooling and network installation can delay usable compute capacity even when chips are available. Publishing reference designs addresses part of that problem by letting the supply chain pre-build against known dimensions, but an open design does not establish interoperability across suppliers. The concrete test is whether shared specifications shorten commissioning and reduce integration work. This focus complements AMW's October 22 coverage of DSX POD, whose stated 14-day deployment depends on ready power and supporting facilities.

Implications for AI Data Centers

For operators, the practical consequence is that the bottleneck shifts from silicon procurement to site engineering. A 250-kilowatt rack cannot simply be dropped into a hall designed for traditional densities without rework of power distribution, coolant delivery and cabling. Suppliers must be qualified, and facilities must be ready, before announced rack capacity can be treated as deployable infrastructure. Builders and investors should therefore read the reference designs as a roadmap for where integration work will concentrate — not as a guarantee that capacity arrives on schedule.

Future Outlook

Several checkpoints will determine whether the open-design strategy delivers. First, whether optical-module validation and maintenance tooling mature quickly enough to support 400 Gb/s-per-lane deployment at scale. Second, whether facility operators can complete the 800V DC and high-density liquid cooling transition without stalling deployments. Third, whether supplier qualification broadens beyond a small set of partners. NVIDIA's willingness to publish the rack envelope suggests it expects the binding constraint to sit outside its own products. The company's ability to keep the compute layer proprietary while the infrastructure layer converges is likely to define the next phase of AI data center buildout.