Scaling AI Infrastructure With PCIe Switches And Retimers
As AI workloads grow in size and complexity, data center architects face a foundational challenge: connecting ever more accelerators, memory pools, and storage without strangling the interconnect that ties them together. PCIe switches and retimers have emerged as essential building blocks for scaling AI infrastructure—extending reach, multiplying fan-out, and preserving signal integrity across increasingly elaborate topologies.
Why PCIe Remains Central To AI Systems
PCIe is the ubiquitous host interconnect for GPUs, TPUs, NICs, NVMe storage, and increasingly CXL-attached memory. In 2026, PCIe 6.0 is moving into mainstream deployment with 64 GT/s per lane and PAM4 signaling, while PCIe 7.0 (128 GT/s) is progressing through specification and early silicon development. Each generation doubles bandwidth but also shrinks the signal budget, making passive traces alone inadequate for the distances and lane counts that modern AI racks demand.
Two device classes address this:
- Retimers regenerate the signal, restoring full PCIe timing and voltage margins over longer channels—essential for backplanes, risers, and cabled connections between hosts and accelerator trays.
- Switches provide fan-out, enabling multiple endpoints to share a host root complex, or enabling peer-to-peer paths between accelerators, NICs, and storage.
Together, they let designers build systems that would be impossible with direct host-to-device links.
Typical AI Topologies
Scale-up within a node. A single server may host 4–8 accelerators. Switches allow flexible lane allocation—for example, splitting a x16 host link into multiple x8 or x4 links to different devices, or aggregating multiple switches for higher radix. Retimers bridge the physical distance between the CPU complex and accelerator sleds.
Scale-out across nodes. In rack-scale and pod-scale designs, PCIe switches create a fabric that connects many hosts to shared pools of accelerators, NVMe storage, or CXL memory. Peer-to-peer transfers between GPUs and NICs reduce host CPU involvement and latency.
Composable/disaggregated systems. Switches underpin resource pooling, where compute, memory, and storage are disaggregated and reassigned dynamically. CXL, which runs over the PCIe physical layer, extends this to memory semantic sharing.
Key Design Considerations
Signal integrity and loss budget. At 64 GT/s and beyond, channel loss from PCB traces, connectors, and cables must be managed carefully. Retimers reset the loss budget at the midpoint of a link, but they add latency and power. Designers must decide where retimers are truly required versus where a switch can absorb the function.
Latency and jitter. Every switch hop and retimer adds nanoseconds. For tightly coupled AI training, even small latencies compound across collective operations. Switch architectures vary in cut-through versus store-and-forward behavior, and in how they handle congestion.
Power and thermal density. High-radix switches and retimers dissipate significant power in already-dense accelerator trays. Thermal design and airflow must account for these devices.
Firmware and manageability. PCIe switches require configuration, partitioning, and error handling. Robust management interfaces and telemetry are essential for fleet-scale deployments.
Interoperability and compliance. With PCIe 6.0/7.0, CXL 3.x, and emerging UALink and other scale-up interconnects, ensuring that switches and retimers interoperate with hosts and endpoints is a nontrivial validation task.
The Road Ahead
As AI clusters scale toward tens of thousands of accelerators, the interconnect hierarchy is being rethought. PCIe switches and retimers sit at the center of this evolution, bridging the gap between the raw lane count of a host and the sprawling, disaggregated fabric that modern AI demands. With PCIe 7.0 on the horizon and CXL gaining momentum, expect these components to become even more central—and more sophisticated—in the years ahead.