Private 5G Networks: Solving the Space and Power Problem

Why It Matters

The NIS model lowers capital and operating expenses while enabling edge AI, making private 5G viable for locations previously blocked by space and power limits.

Key Takeaways

• Samsung's NIS packs core, RAN, transport on one server.
• Uses AMD EPYC 8000 CPU with GPU support.
• Cuts rack space, power consumption, and logistics costs.
• Enables on‑prem AI processing, lowering latency.
• Simplifies support with single vendor point of contact.

Pulse Analysis

Enterprises have long eyed private 5G as a catalyst for automation, yet dense hardware requirements have stalled rollouts in compact environments. Traditional deployments rely on multiple racks of telecom‑grade equipment, demanding significant floor space, cooling capacity, and power budgets. For retail outlets, small manufacturing cells, or remote field sites, these constraints translate into prohibitive capex and ongoing energy costs, forcing many organizations to postpone or abandon private network projects despite clear operational benefits.

Samsung’s Network in a Server (NIS) tackles the dilemma by collapsing the entire private‑5G stack into a single commercial off‑the‑shelf server. Leveraging an AMD EPYC 8000 CPU paired with GPUs, the platform runs containerised network functions—core, RAN, transport and AI agents—via software‑defined virtualization. This shift eliminates the need for specialised telecom hardware, reduces the physical footprint to a standard rack unit, and cuts power consumption dramatically. Integrated AI processing at the edge further trims latency, as data never leaves the local server, satisfying stringent real‑time and compliance requirements for video analytics, ISAC and immersive XR applications.

From a business perspective, the consolidated architecture reshapes the economics of private 5G. Lower equipment counts shrink shipping and installation expenses, while a unified support model simplifies vendor management. Companies can now justify deployments in previously marginal sites, unlocking new revenue streams through AI‑enabled services rather than pure connectivity fees. However, decision‑makers must weigh the trade‑off between single‑server simplicity and the redundancy traditionally offered by multi‑node designs, especially for mission‑critical operations. As edge AI adoption accelerates, solutions like Samsung’s NIS are poised to become a cornerstone of next‑generation industrial networks.