AI Computing Continuum

The OCP AI Computing Continuum (AICC) Project accelerates adoption of scalable, interoperable, and sustainable AI systems beyond hyperscale data centers. It defines open, modular infrastructure standards for hyperscale-adjacent environments such as regional colocation, enterprise on-premises sites, telco points of presence, and industrial sites, addressing real-world limits on power, thermal capacity, and physical footprint. Its specifications span five domains: AI Open Server Architectures, AI Open Solution Architectures, AI Building Block Architectures, Autonomous AI Wireless Access, and Connectivity. The project was launched in collaboration with the IOWN Global Forum.

AI Computing Continuum

Get involved

Steering Committee Representative

Sean Varley

Project Leads

Subscribe to mailing list

About This Project

Delivering on the Next Wave of AI: Inference

AI Computing Continuum aims to accelerate the adoption of scalable, interoperable, and sustainable AI systems beyond traditional hyperscale data centers by defining open, modular infrastructure standards that work across diverse hyperscale-adjacent environments — including regional colocation, enterprise on-premises, telco points of presence, and industrial sites. Guided by OCP principles of openness, efficiency, and sustainability, the project leverages hyperscale data center innovations to develop community-driven, hardware-focused specifications that enable hyperscale-adjacent-scale AI deployments covering scale-up, scale-out, scale-across, and wireless access communications.

By standardizing core platform building blocks, bridging the gap between hyperscale and enterprise needs, and addressing real-world constraints in power, thermal, and physical footprints, the AI Computing Continuum initiative supports repeatable, cost-effective deployment of advanced AI compute systems wherever they are needed outside of centralized hyperscale data centers.

We thank the IOWN Global Forum for its collaboration with OCP in launching the AI Computing Continuum Project.

Scope

To enable interoperable, scalable, and sustainable AI system deployment across hyperscale-adjacent installations, the project defines open, modular, and extensible specifications in five domains:

  • AI Open Server Architectures : AI-optimized server and sled form factors, expansion modules, and the mechanical, electrical, and thermal interfaces for server-level integration
  • AI Open Solution Architectures : rack- and facility-aware system context: rack and sub-rack designs, cooling and power frameworks, and deployment and site considerations
  • AI Building Block Architectures : reusable enclosure, cooling, networking, and power modules forming a standardized toolkit for scale-up and scale-out designs
  • Autonomous AI Wireless Access : an AI-accelerated autonomous open hardware radio unit for xG or WiFi: AuRU, RAI, and RU-SCM
  • Connectivity : scale-across communications between data centers and AI fabrics

Focus Domains

1. AI Open Server Architectures

This area defines the foundational building blocks used within AI-capable hyperscale-adjacent racks and multi-node systems. It includes:

  • AI-optimized server and sled form factors designed for accelerated compute configurations
  • Expansion modules that support heterogeneous accelerators and fabric topologies
  • Mechanical, electrical, and thermal interfaces required to standardize server-level integration within diverse rack environments
  • Design guidance for hyperscale-adjacent GPU/accelerator deployments, including airflow, power distribution, and serviceability

This domain ensures that server-level components remain interoperable, interchangeable, and portable across varied hyperscale-adjacent deployment environments.

2. AI Open Solution Architectures

This area defines the hyperscale-adjacent rack- and facility-aware system context required to deploy AI infrastructure beyond hyperscale data centers. It includes:

  • Rack and system-level architecture — AI-specific rack enclosures and sub-rack form factors; rack and partial-rack AI system designs suited for modular deployment; in-rack networking and interconnect topologies supporting accelerator fabrics
  • Cooling and power — air-cooled, closed-loop liquid cooling (CLLC), and hybrid cooling solutions ready for heterogeneous hyperscale-adjacent site conditions; power delivery, budgeting, and distribution frameworks aligned with modern AI workloads
  • Deployment and facility considerations — accommodation of site constraints (power availability, cooling capacity, floor loading, acoustic and environmental restrictions); industrial and retrofit deployments, including sealed or ruggedized IP-rated enclosures; modular deployment models such as “pay as you grow,” rack-and-stack, and non-greenfield integration

This domain ensures that AI systems can be deployed consistently across colocation, enterprise, industrial, telco points of presence, and remote environments.

3. AI Building Block Architectures

This area defines the reusable hardware and infrastructure modules that serve as the basis for scalable AI system construction. It includes:

  • Rack and sub-rack enclosure building blocks for consistent physical and mechanical integration
  • Cooling modules (air, liquid, hybrid) with standardized interfaces
  • Networking and interconnect modules for both intra-rack and inter-rack connectivity
  • Power distribution and conversion modules tailored for inference AI loads

These building blocks form a standardized toolkit that supports both hyperscale-adjacent scale-up and scale-out AI designs while maintaining interoperability across vendors and facility types.

4. Autonomous AI Wireless Access

This area defines an AI-accelerated autonomous wireless access open hardware radio unit for xG or WiFi. It includes:

  • Autonomous Radio (AuRU) design and implementation, including augments to manufacturing processes
  • Radio Abstraction Interface (RAI) and the containerization of apps that can run in the radio unit environment
  • Security Control Module (RU-SCM) and the application of security best practices to a radio unit of aggregated parts

This domain ensures that AI-accelerated wireless access systems can be deployed consistently across OpenRAN environments.

5. Connectivity

  • Scale-across communications between data centers
  • AI fabrics

Resources

This Project is open to the public and we want to welcome all those who would like to be involved.

Disclaimer: Please do not submit any confidential information to the Project Community. All presentation materials, proposals, meeting minutes and/or supporting documents are published by OCP and are open to the public in accordance to OCP’s Bylaws and IP Policy. This can be found on the OCP OCP Policies page. If you have any questions please contact OCP.

Documents

  • Charter

AI Computing Continuum aims to accelerate the adoption of scalable, interoperable, and sustainable AI systems beyond traditional hyperscale data centers by defining open, modular infrastructure standards that work across diverse hyperscale adjacent environments—including regional colocation, enterprise on premises, telco points of presence, and industrial sites. Guided by OCP principles of openness, efficiency, and sustainability, the project focuses on leveraging hyperscale data center innovations to develop community-driven, hardware-focused specifications that enable hyperscale adjacent scale AI deployments covering scale up, scale out, scale across, and wireless access communications. By standardizing core platform building blocks, bridging the gap between hyperscale and enterprise needs, and addressing real-world constraints in power, thermal, and physical footprints, the AI Computing Continuum initiative supports repeatable, cost-effective deployment of advanced AI compute systems wherever they are needed outside of the centralized hyperscale data centers.

Recordings from Past Calls

To be added as the new project begins

Specs and Designs

Link to the Specs and Designs page (none yet)

Regular Project Calls

This project meets the 3rd Tuesday of every month at 12PM Pacific Standard Time. This call is open to the public (please check the call calendar).

Call Calendar

These meeting  are recorded via audio and video. By participating you consent that these recordings may be made publicly available. Any presentation materials, proposals and meeting minutes are published on th respective project’s wiki page and are open to the public in accordance to OCP’s Bylaws and IP Policy. This can be found at http://www.opencompute.org/about/ocp-policies/. If you have any questions please contact OCP.

AI Computing Continuum calendar

Sub-Projects

AI Native Edge MHS

AI Native Edge-MHS is a Sub-Project of the OCP AI Computing Continuum (AICC) Project. It specifies AI-optimized modular hardware for hyperscale-adjacent environments, defining standardized mechanical, electrical, and thermal interfaces so interoperable multi-node AI systems, servers, and racks can be built for scale-up, scale-out, scale-across, and wireless access architectures. Its specifications also set the technical requirements suppliers must meet for OCP recognition. Compute cores, GPUs, cooling, chassis, software, firmware, security, and environmental requirements are addressed by complementary MHS, design, or product specifications.

AI-Native Wireless Access

AI-Native Wireless Access is a Sub-Project of the OCP AI Computing Continuum Project. Its vision is to make wireless access an inherently AI-native, autonomous, and distributed platform that embeds accelerated computing into the network edge, and its mission is to accelerate adoption of AI-native wireless access by defining open, common, and sustainable specifications, interfaces, and best practices under OCP. The sub-project connects the wireless last mile into the continuum through three interdependent activities: the Autonomous AI-Native Radio (AuRU), the Radio Abstraction Interface (RAI) with its containerization framework, and the Security Control Module (RU-SCM), each delivered as an open specification or reference design.