Kubernetes Hardware Requirements for On-Prem Clusters
- Dec 20, 2025
- 3 min read
Running Kubernetes on-premises is very different from running it in the cloud. In the cloud, hardware is abstracted and elastic. On-prem, your physical servers define what Kubernetes can and cannot do.
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The table below shows a practical hardware baseline that works reliably for most on-prem Kubernetes clusters:
Component | Control Plane (per node) | Worker Node (per node) |
CPU | 8 cores | 16–32 cores |
Memory (RAM) | 32 GB | 64–128 GB |
Storage | SSD or NVMe | Enterprise SSD or NVMe |
Network | 10 GbE | 10–25 GbE |
Notes | Fast storage is critical for etcd | Balance CPU & RAM, avoid small nodes |
Core Hardware Requirements for On-Prem Kubernetes
Kubernetes is excellent at managing workloads, but it does not magically create resources. It can only work with the CPU, memory, storage, and network capacity you provide.
When hardware is undersized or unbalanced, Kubernetes will still run — but problems appear quickly:
pods get evicted
scaling feels ineffective
performance becomes unpredictable
troubleshooting gets harder over time
In most on-prem environments, these issues are hardware-driven, not software-driven.
Kubernetes Control Plane: Hardware Requirements
The control plane is responsible for:
scheduling decisions
cluster state (etcd)
API requests
coordination between nodes
Even though it does not run application workloads, it is performance-sensitive.
Typical production sizing per control plane node:
8 CPU cores
32 GB RAM
fast SSD or NVMe storage
Running the control plane on slow disks or minimal memory often leads to:
slow deployments
delayed scaling
unstable cluster behavior
Kubernetes Worker Nodes: CPU Hardware Requirements
CPU is one of the most common bottlenecks in on-prem Kubernetes clusters.
Containers are lightweight, but:
the operating system needs CPU
Kubernetes services consume CPU
monitoring, logging, and security agents add overhead
Practical guidance for worker nodes:
16–32 CPU cores per node as a baseline
avoid very small CPUs with many pods
prefer fewer, well-balanced nodes over many weak ones
When CPU is insufficient, Kubernetes throttles workloads instead of failing loudly, which makes performance issues harder to diagnose.
Kubernetes Clusters: Memory (RAM) Hardware Requirements
Kubernetes is strict with memory. When a node runs out of RAM, pods are evicted immediately.
Realistic RAM sizing:
64 GB per worker node: minimum for production
128 GB or more: common in stable clusters
Low memory leads to:
frequent pod restarts
unstable services
“random” failures that are difficult to trace
These symptoms are often mistaken for application bugs.
Kubernetes Clusters: Storage Hardware Requirements and Latency
Storage affects more than databases. It impacts:
container startup times
image pulls
logs and metrics
persistent volumes
What usually causes problems:
HDD-only setups
overloaded shared SAN
high-latency storage
Recommended approach:
enterprise SSDs at minimum
NVMe for IO-heavy workloads
predictable latency is more important than peak throughput
On-prem Kubernetes benefits more from consistent storage performance than from raw capacity.
Kubernetes Clusters: Network Hardware Requirements
Kubernetes generates significant east-west traffic:
pod-to-pod communication
service routing
storage and replication traffic
Practical baseline:
10 GbE networking minimum
25 GbE recommended for clusters expected to grow
Insufficient network bandwidth or poor NICs often cause:
intermittent latency
timeouts
hard-to-debug service issues
These problems usually appear only under load, not during testing.
Kubernetes Hardware Requirements in Practice for On-Prem Clusters
Most on-prem Kubernetes clusters begin small, often with three control plane nodes and three worker nodes. This setup is easy to justify and works well in the beginning.
Problems usually appear later, when the cluster needs to handle:
upgrades
maintenance windows
unexpected failures
At that point, many teams realize that Kubernetes is easier to operate and scale with more standard worker nodes, rather than trying to push more capacity into a few large servers. This typically leads to decisions around adding new nodes or refreshing existing hardware to support future growth.
At the same time, hardware requirements differ depending on how Kubernetes is deployed:
Deployment model | Typical characteristics | Hardware impact |
Bare metal | Best performance, fewer abstraction layers | Requires well-balanced servers |
Virtualized (VMware, Proxmox) | Easier lifecycle management, more flexibility | Needs more CPU and RAM due to overhead |
Minimum hardware requirements often look fine on paper but rarely work on-prem, because they usually assume:
test or lab environments
short-lived workloads
no monitoring, logging, or security tools
In real on-prem clusters, workloads grow over time, teams add more services, and operational tooling continuously consumes resources. As a result, clusters that start small often hit hardware limits within months.
In practice, stable on-prem Kubernetes environments depend on:
balanced hardware across CPU, RAM, and storage
scalable designs that allow easy node expansion
predictable components without weak links
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