Dell PowerEdge XR8720t compute sled

The Dell PowerEdge XR8720t compute sled is designed to transform edge and telecom infrastructure by delivering high performance and connectivity, especially for Open RAN and Cloud RAN deployments.

The product integrates seamlessly with the Dell PowerEdge XR8000 platform, and is purpose-built to address the performance challenges that have historically hindered infrastructure adoption in challenging environments. As a single-server solution for Cloud RAN, it helps to streamline infrastructure, boost performance and efficiency, and reduce total cost of ownership for modern network and edge deployments.

The device eliminates the performance gaps between Cloud and traditional RAN architecture, offering more than double the processing power compared to the previous generation — supporting up to 72 cores and 24 SFP28 connectivity ports in a compact 2U configuration. The single-server consolidation reduces deployment time, operational complexity and maintenance requirements compared to multi-server architectures.

With PTP, PTM and SyncE support and a compact 430 mm depth designed for space-constrained cell site deployments, the device delivers the processing power, port density (24x SFP28) and network bandwidth (600 GbE) required for demanding Cloud RAN workloads. Engineered for extreme conditions, it operates in temperatures ranging from -5 to 55°C, with a modular design that allows for easy maintenance and upgrades no matter its environment.

The Network Equipment-Building System (NEBS) Level 3-compliant server also includes features like front-accessible I/O that reduce downtime and operational complexity. Built for extended durability for telecom, edge and military applications, the system’s intelligent cooling design optimises cooling performance in tight spaces.

The product makes it easy for telecommunications providers to support performance-intensive applications, reduce costs and conduct AI at the edge. Enterprises across telecom, retail, defence and manufacturing will find this useful to drive AI, ML and other compute-intensive workloads, as well as precision-driven tasks that require advanced synchronisation.