Self-Hosted Kubernetes (K3s) on Bare Metal: A Lightweight [...]

📅 September 11, 2026 - FOR IMMEDIATE RELEASE

LONDON, UK – eServers, a premier provider of high-performance enterprise bare metal infrastructure, has released an architectural technical advisory titled "Self-Hosted Kubernetes (K3s) on Bare Metal: A Lightweight Alternative to Proxmox/VMware." The comprehensive guide challenges the common industry practice of running container orchestrators inside virtualized hypervisor environments, offering systems engineers and DevOps teams a leaner, significantly faster deployment strategy.

"Proxmox and VMware solve a fundamentally different problem than Kubernetes does," the eServers technical report explains. "Hypervisors virtualize hardware into separate, isolated machines. Kubernetes, on the other hand, orchestrates containers that share the host operating system's kernel. The most common architectural mistake we see is engineers installing K3s inside a Proxmox VM or LXC container by default. This adds a 'nested virtualization tax'—a layer of unnecessary overhead between Kubernetes and the hardware—for absolutely no functional benefit."

The Bare Metal K3s Advantage

To maximize compute efficiency, the tutorial advocates for deploying K3s directly onto bare-metal servers. K3s, a certified lightweight Kubernetes distribution originally built by Rancher, ships the control plane, kubelet, and containerd runtime as a single, highly optimized binary. By skipping the hypervisor layer entirely, administrators eliminate storage friction and drastically simplify network routing.

Optimizing for AI/ML Inference Workloads

The newly published engineering guide provides a complete walkthrough for setting up a production-ready cluster on Ubuntu 24.04 LTS. A major focal point of the tutorial is configuring Kubernetes for high-performance Artificial Intelligence and Machine Learning (AI/ML) workloads. The guide provides step-by-step CLI commands for deploying the NVIDIA device plugin, allowing K3s to expose raw GPU resources directly to pods.

"This is the exact underlying mechanism used when containerizing large model inference workloads, such as a multi-GPU DeepSeek-V3 deployment," the tutorial notes. "Running inference workloads under K3s on bare metal delivers rolling updates, automated self-healing, and immense scalability across physical GPU servers without re-architecting your stack."

Storage and High Availability

Furthermore, the guide addresses the ephemeral nature of containers by demonstrating how to deploy Longhorn, a distributed block storage system built specifically for Kubernetes. It also covers initializing a High Availability (HA) cluster utilizing an embedded etcd datastore across multiple physical nodes.



To access the complete CLI commands, YAML pod specifications, and cluster initialization scripts, system administrators are highly encouraged to read the full tutorial on the official eServers website.