OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks

At the 2026 Open Compute Project Global Summit, Meta is sharing the next generation of open networking infrastructure, including: Our first liquid-cooled network rack (IcePack), and 102.4T switch platform (IceCube). Flattened Planar Fabric (FPF), a multiplane backend scale-out fabric that fundamentally changes how GPUs connect to the network MetaRoCE, our open-source AI transport protocol. Launching the Open Switch Platform Architecture (OSPA) initiative within OCP. We’re inviting engineers, silicon vendors, system integrators, and operators to join us and the OCP community in meeting the challenges of building open, efficient, and sustainable AI infrastructure. At Meta, we believe that open networking is critical for building high-performance, scalable, and sustainable infrastructure, and we’ve acted on that conviction since co-founding the Open Compute Project (OCP) in 2011. Throughout the ongoing AI revolution, Meta has pushed the boundaries of what’s possible in data center networking and shared our advances with the industry. At OCP 2025, we introduced our latest AI scale-out fabrics and drove the creation of the Ethernet for Scale-Up Networking (ESUN) initiative. This year at OCP 2026, we’re sharing the networking innovations that directly support our latest AI workloads such as Meta’s Muse Wonder and Muse Spark models, and Muse, our personal AI agent built for everyone . Here’s some of what’s new: The 102.4T scale-out switch : Meta’s first liquid-cooled network rack (IcePack) and 102.4T switch blades (IceCube), designed to support high power density, deliver higher thermal efficiency, and enable gigawatt-scale AI clusters. The scale-out Flattened Planar Fabric (FPF) : Meta’s multiplane fabric, enabling larger, highly efficient GPU clusters with lower latency — with learnings shared with our cloud partners. Scale-across for multi-site clusters : Meta’s petabit-scale backend aggregation (BAG) super-spine and how large-scale training jobs deal with failures at this scale — with an overview of how we’ve built SRv6 regional backbones using FBOSS and SAI to handle even longer distances Ethernet for Scale-Up Networking: Meta’s MTIA 300 system uses Tahan, an Ethernet-based 51.2T scale-up switch, delivering 6.4 Tbps per accelerator — with a preview of next-gen 72-accelerator-per-rack design on 102.4T silicon and moving toward an ESUN-based implementation with future chips. Open Switch Platform Architecture (OSPA) : a new OCP initiative, co-led by Meta, Microsoft, and Alibaba, that standardizes the switch platform layer — with specification v1.0 now submitted to OCP. MetaRoCE : Meta’s RDMA transport protocol for AI networks — now with open-source implementations. The 102.4T Generation: Liquid-Cooled Networking at AI Scale As AI clusters grow to gigawatt scale, network switches must deliver dramatically more bandwidth while fitting within increasingly dense power and cooling envelopes. Our answer is the IcePack rack, Meta’s first-generation liquid-cooled network rack. IcePack is based on OCP’s ORV3-HPRv1 rack standard . IceCube switch blades — 102.4-Tbps fabric switches that deliver 128×800G non-blocking switching capacity — sit inside each rack. IceCube comes in two variants , one built on Broadcom’s Tomahawk6C switch ASIC and one on NVIDIA Spectrum-6 Switch ASIC. Both run Meta’s open-source FBOSS network operating system that leverages OCP SAI (Switch Abstraction Interface). This new generation represents a 2x bandwidth increase over our 51.2T platforms while achieving the thermal density required for next-generation AI data centers. Scaling AI Fabrics From Building to Region As seen at Meta and across the industry, AI clusters now span buildings, campuses, and multiple regions . This demands fabric architectures that are not only massive in scale but also resilient and flexible. Flattened Planar Fabric (FPF): More Resilient, Larger Clusters We’re introducing Flattened Planar Fabric (FPF) , Meta’s multiplane backend scale-out fabric that fundamentally changes how GPUs connect to the network and the network control plane of the fabric. With FPF, each GPU connects via eight independent 100G links to eight different switches (one per network plane) . This multiplane approach replaces the traditional single-plane uplink architecture and delivers four key advantages: Larger non-blocking GPU clusters compared to single-plane topologies, which enables the scale required for our largest AI training workloads A flattened topology , moving from the 3-tier Clos fabric of our NSF fabric to a 2-tier Clos fabric. Removing this tier of switches reduces network hops, latency, and infrastructure cost. NIC-level hardware load balancing: Load balancing, traditionally done by network switches, now starts at the NIC, which sprays every packet across all eight planes in hardware. A combined control plane for fault tolerance: The control plane of FPF includes both the switches and the hosts, allowing the host to steer traffic away from downed planes. Meta collaborated closely with NVIDIA on developing FPF running inside Meta’s infrastructure, leveraging NVIDIA Spectrum-X Multiplane innovations to ensure FPF has high performance even under assorted failure conditions and operational lifecycle scenarios. Going beyond our own infrastructure, Meta and NVIDIA have been working closely with our joint cloud partners to help standardize multiplane fabrics implementations within those partners based on our FPF learnings. Bridging On-Prem Data Centers and Public Cloud This work with cloud providers on FPF is just one example of how we at Meta are not just building our own infrastructure, but are helping our partners’ infrastructure. Our ambitions for scaling our AI capacity cannot be met by building data centers alone, because construction runs on multi-year lead times while demand moves in months. So we use leased facilities and public cloud capacity , alongside our own campuses, to close the gap. But it only works if the network behaves the same way no matter where the GPUs sit. That is genuinely difficult, because cloud networking is not the same as owning the rack . Fabric topologies differ from provider to provider rather than following one uniform design. Buffering is often shallower, which changes congestion and loss behavior. RDMA transports are provider-specific, and some clouds still run InfiniBand rather than Ethernet and RoCE. What works for a handful of cloud instances also tends to break at fleet scale. At OCP we’re sharing how we’re converging these environments into a single operational model. That includes a standardized performance validation framework — cloud partners run a common suite of point-to-point and collective benchmarks, scaling from tens to over a thousand GPUs, before a cluster is ever handed over, so performance is proven rather than assumed. It includes normalizing heterogeneous, often multiplane fabrics, mapping divergent traffic-class schemes and congestion-control parameters onto our lossless RDMA classes so end-to-end paths behave predictably across different vendors’ NICs and switches. The principle we keep applying is that heterogeneity is manageable but standardization is what makes it affordable. Testing and qualification, RDMA transport and congestion behavior, and telemetry all still diverge across providers, and every divergence becomes engineering work. BAG: Scale-Across Aggregation The scale of AI capacity Meta has committed to this decade — Prometheus at roughly 1 gigawatt in 2026, and Hyperion scaling toward 5 gigawatts — means a single cluster now outgrows any single region. Our backend aggregation (BAG) layer is a centralized, Ethernet-based super-spine that interconnects many spine-layer fabrics into one mega cluster spanning buildings, campuses, and regions. It operates at petabit scale — inter-BAG capacity between a pair of regions reaches tens of petabits per second. BAG is what allows Prometheus to function as a single cluster e