estelacarmona
28 September 2026
Creating a private 5G network
Private 5G networks are dedicated cellular systems delivering ultra-reliable low-latency communication, massive IoT connectivity, and customized security for enterprises undergoing digital transformation. Private 5G subsumes advantages of both public and non-public networks, offering unified connectivity, optimized services, and customized security within a defined area. Private 5G empowers enterprises to control and secure their own information, optimize networks for exclusive requirements, and flexibly reconfigure infrastructure as business objectives evolve.
Private 5G has evolved from an experimental concept into critical industrial infrastructure. The success of private 5G hinges on a fundamental architectural shift: replacing heavyweight, legacy virtualization stacks with agile, softwarized infrastructure that slashes operational expenditure (OpEx) and accelerates service delivery.
However, deploying private 5G at the edge introduces additional challenges. The edge is constrained by power, cooling, space, and it is physically vulnerable. And, increasingly, the edge needs to meet high localized AI processing demands. Solving these challenges requires a cohesive software stack spanning from the immutable Operating System (OS) up to model-driven automation, capable of bridging the gaps between telco requirements and cloud-native IT.
The virtualization dilemma
Historically, deploying a mobile core required racks of servers, substantial power consumption, and heavyweight virtualization stacks like traditional OpenStack. In contrast, private 5G scenarios, where the infrastructure might live in a factory closet or an outdoor enclosure, require lightweight virtualization instead. The user plane function (UPF) of the 5G core, and often the virtualized radio access networks (vRAN) and/or open RAN (O-RAN) components, need to run on a fraction of the footprint.
For years, the industry narrative faced a binary choice: either stick with traditional, resource-heavy virtual machines (often managed by enterprise hypervisors), or move entirely to application containers (managed by Kubernetes). At the enterprise edge, neither extreme is perfect on its own. Legacy hypervisors consume too much compute overhead, inflating OpEx and hardware costs. Conversely, although modern 5G Core and vRAN components are shifting to cloud-native network functions (CNFs) that demand near-bare metal performance, many enterprises still rely on legacy virtual network functions (VNFs) or security appliances that require strict kernel-level isolation.
As a consequence, the industry is increasingly embracing lightweight, hybrid edge virtualization. Architectures built on system containers deliver similar packet performance to bare metal for 5G UPFs while using lean KVM virtual machines (VMs) only when strict kernel boundaries are mandated. This hybrid footprint keeps edge compute efficient, low-touch, and cost-effective.
This is where MicroCloud changes the game for private 5G.
MicroCloud is a low-touch, automated private cloud designed specifically for the edge. Instead of deploying a massive, bloated cloud infrastructure, MicroCloud combines lightweight LXD system containers and virtual machines, distributed storage via MicroCeph, and software-defined networking via MicroOVN into a single automated tool. MicroCloud allows you to deploy a lightweight cluster that can run on as few as three to four physical nodes with 32 GB of RAM each for production high availability deployments.
The OS at the core: determinism, hardware acceleration, and immutability
In private 5G deployments, the OS is the foundational layer through which the network stack accesses compute, networking, scheduling, and hardware acceleration capabilities. For latency-sensitive workloads, a real-time kernel such as real-time Ubuntu, which incorporates PREEMPT_RT, can make task execution more predictable by reducing scheduling latency and providing more deterministic response times. High-throughput UPF workloads, meanwhile, can use technologies such as Data Plane Development Kit (DPDK) for efficient userspace packet processing, while Extended Berkeley Packet Filter (eBPF) and Express Data Path (XDP) provide programmable, high-performance processing within the Linux networking stack.
As private 5G architectures increasingly converge radio processing, edge computing, and AI inference, the OS must expose the hardware primitives needed to allow diverse workloads to share infrastructure predictably and securely. Non-Uniform Memory Access (NUMA) awareness, hugepages, CPU affinity and isolation, device passthrough and Single Root Input/Output Virtualization (SR-IOV) can enable network functions and AI workloads to approach bare metal performance while maintaining resource boundaries through the virtualization and container stack.
Where the threat model requires protection from a potentially compromised host or hypervisor, confidential computing technologies can add hardware-assisted isolation for workloads. The OS therefore needs to provide not just hardware abstraction, but the mechanisms through which higher-level platforms can translate application requirements (e.g., latency, throughput, isolation, and locality) into enforceable resource policies.
Security and lifecycle management are equally important at the physically distributed edge. An OS for private 5G should establish a chain of trust from boot through runtime, combining secure boot, hardware-rooted trust, disk encryption, application confinement, and least-privilege controls with transactional software updates and reliable rollback. Ubuntu Core is an example of this security and lifecycle model, with application confinement, full-disk encryption (FDE), and transactional updates designed for devices that may operate remotely for long periods.
For larger private 5G deployments, vulnerability remediation must also minimize operational disruption. Linux Kernel Livepatch can address supported critical and high-severity kernel vulnerabilities without rebooting, reducing maintenance downtime as a result. In addition to this, Ubuntu Pro provides 10 years of security coverage, which can be extended to 15 years with the Legacy add-on. These capabilities are particularly valuable in environments where infrastructure is expected to remain security-maintained, predictable, and supportable for many years.
Edge AI inference in private 5G deployments
Private 5G networks can generate substantial uplink traffic, such as telemetry, video feeds, sensor data, and more. Sending all raw data to a centralized cloud can increase backhaul requirements, latency, and data and security exposure. Localizing AI inference at the edge can enable very low and more predictable end-to-end latency by keeping data close to where it is generated. A local 5G UPF can provide a direct user-plane path into an edge computing environment, allowing applications such as computer vision, defect detection, autonomous mobile robot trajectory planning, or physical AI to process data streams in milliseconds.
In emerging AI-RAN architectures, this convergence can extend to sharing accelerated compute infrastructure between RAN functions and AI workloads. GPUs and other accelerators can potentially support both radio processing and enterprise AI, with spare capacity made available to additional workloads when RAN demand permits. Industry demonstrations have already shown RAN and AI workloads running concurrently on shared GPU-accelerated platforms.
To integrate AI efficiently, the infrastructure must support containerized workloads seamlessly alongside network functions. Ubuntu Server provides the OS foundation for Private 5G and edge AI, with hardware enablement, security maintenance, and long-term enterprise support. For appliance-style edge deployments, Ubuntu Core offers a more minimal, immutable, and transactional model with secure boot, application confinement, and Over-the-Air (OTA) updates. MicroCloud can add lightweight infrastructure virtualization through LXD containers and VMs, while Canonical Kubernetes provides orchestration for cloud-native 5G Core and AI workloads.
This stack enables enterprises to run private 5G alongside edge AI workloads such as computer vision and robotics, while maintaining the performance, security, isolation, and lifecycle management required for industrial environments.
Conclusion
Private 5G can become more than a dedicated connectivity layer: it can provide the connectivity foundation for distributed vertical computing and AI. The strongest architectures will combine trusted OS foundations, efficient infrastructure virtualization, and cloud-native orchestration, so that compute resources can support both network functions and emerging workloads such as computer vision, robotics, and industrial AI. The objective should be to create an infrastructure platform that can evolve as radio, networking, compute, and AI requirements change, while retaining consistent security and lifecycle management across the estate.
Next steps
Learn how Canonical solutions provide a stable, validated, and open foundation for telco workloads.