NVIDIA Opens Vera Rubin Architecture for Global AI Infrastructure Scaling

NVIDIA has made significant strides in advancing the development of artificial intelligence (AI) infrastructure with its latest announcement at the Open Compute Project (OCP) Global Summit. The company unveiled new partnerships and contributions aimed at creating a common foundation for next-generation AI infrastructure, marking a crucial step towards global scalability.

At the heart of NVIDIA’s efforts is the Vera Rubin architecture, which has been designed to support the growing demands of AI workloads. As AI factories continue to grow from megawatts to gigawatts in power consumption, building them requires more than just advanced computing – it also necessitates open standards for efficient and reliable infrastructure.

The OCP Global Summit provided a platform for NVIDIA to collaborate with industry leaders and contribute key designs to the Open Compute Project through its MGX architecture. This move enables system builders to develop next-generation AI factory infrastructure based on a common foundation, streamlining the process of building large-scale AI systems.

NVIDIA’s contributions span across various aspects of AI infrastructure development, including rack-scale architecture, utility grid design, and software components. The company has made significant strides in advancing open standards for AI factories, publishing new specifications that establish a unified blueprint for next-generation facility design – from power architecture to cooling infrastructure and physical layout.

The Open Data Center Specification v0.7 defines a comprehensive framework for designing AI factory facilities, providing a common foundation for the industry to build upon. This specification is a result of collaboration between NVIDIA, AMD, Google, and Meta, highlighting the growing importance of open standards in advancing AI infrastructure development.

Furthermore, NVIDIA has launched new open-source software components for its DSX AI Factory platform, which applies extreme codesign principles to unify computing, networking, power, cooling, facilities, and software. These components enable cloud providers and software partners to integrate AI factory capabilities directly into their multi-tenant platforms, streamlining the process of building large-scale AI systems.

The NVIDIA Config Manager is one such component that automates switch configurations at scale, allowing customers to deploy, validate, and maintain consistent AI factory network fabrics with minimal manual intervention. The Cluster Readiness Engine helps eliminate costly job crashes by stress-testing GPU clusters with real workload simulations, ensuring hardware health before training begins.

NVIDIA’s contributions also include the development of a comprehensive rack-scale design specification for its MGX architecture. This specification extends the open architecture for AI factories with features such as rack-scale blind-mate liquid cooling, interoperable rack-scale interconnect infrastructure, and a liquid-cooled 5,000A busbar designed for higher-power AI systems.

The updated MGX 1.2 specification further expands modularity by introducing configurable I/O bays, fanless liquid-cooled compute trays, standardized power and cooling interfaces, and a common infrastructure that enables ecosystem partners to build interoperable accelerated computing platforms. This architecture supports rack deployments up to 250kW while simplifying serviceability, deployment, and infrastructure reuse.

NVIDIA’s Vera CPU rack design specification extends the MGX open architecture beyond accelerated computing, providing a purpose-built platform for the growing agentic AI era. The NVIDIA Vera CPU rack is designed to support tool calling, orchestration, and sandboxed execution, enabling AI agents to interact with applications, data, and services in real-time.

The Vera CPU rack shares the same rack-scale design principles as the MGX architecture, uniting compute, networking, power, and cooling into a common modular platform. This approach enables partners to deploy specialized infrastructure while benefiting from a shared ecosystem, simplified integration, and a consistent foundation for next-generation AI factories.

Visionbay has unveiled plans for K-1, a next-generation AI factory platform designed around an 800 VDC architecture and built with future NVIDIA Vera Rubin deployments in mind. The project combines NVIDIA’s 800 VDC reference architecture with power and thermal technologies from Vertiv to create a scalable foundation for high-density AI infrastructure.

By adopting NVIDIA’s 800 VDC architecture, K-1 addresses the growing industry challenge of delivering enough power to accelerated computing systems driving next-generation AI workloads. Traditional low-voltage power distribution becomes increasingly difficult to scale, requiring more copper, cabling, and power-conversion equipment.

The design reflects the system-level philosophy behind NVIDIA AI factories, where power, cooling, and computing are engineered together. With support for liquid-cooled infrastructure, scalable power distribution, and future Vera Rubin-class deployments, K-1 demonstrates how ecosystem partners can translate NVIDIA’s AI factory architecture into deployable infrastructure designed for next-generation accelerated computing.

Meta and NVIDIA have announced a collaboration to advance open AI networking with the introduction of Spectrum-X Ethernet SuperNIC and Spectrum-6 innovations. The partnership combines Meta’s experience operating AI infrastructure at hyperscale with NVIDIA’s networking technology, enabling agentic AI factories to scale beyond 100,000 GPUs with fewer switch tiers and lower infrastructure costs.

The announcement marks a significant step in developing an architecture and operational playbook that other AI infrastructure operators can adopt. This collaboration will help turn lessons from demanding production workloads into more scalable, resilient, and efficient AI factories.

NVIDIA and Cisco have announced plans for the Ultra-Dense Small Form Factor Pluggable (USFP) standard, which provides a new optical pluggable transceiver module form factor designed to scale next-generation AI fabrics while improving bandwidth density and power efficiency. The USFP will deliver four times the bandwidth density of traditional 200Gb/s-per-lane pluggable optics.

The result is the ability to connect more compute resources without adding network tiers that can increase latency, power consumption, and complexity. NVIDIA and Cisco are collaborating with TE Connectivity and Amphenol on cages, connectors, and modules to advance USFP through a community-led effort.

Google has announced Falcon Transport support on future NVIDIA ConnectX-10 SuperNICs and BlueField DPUs, helping improve network efficiency for large-scale AI infrastructure. The collaboration combines NVIDIA hardware acceleration with DOCA software to implement Falcon and extend NVIDIA networking to Google’s data center transport.

Falcon is a transport protocol designed to deliver data reliably over Ethernet networks by distributing traffic across paths, adapting to congestion, recovering dropped packets, and protecting connections. This helps workloads make better use of available bandwidth and maintain predictable performance as Ethernet fabrics scale.