Best Server for Kubernetes in India

Find the best server configuration for Kubernetes clusters in India. CPU, RAM, storage, and networking recommendations for production K8s deployments.

Resource Profile for Kubernetes Container Orchestration

CPU

AMD EPYC 9004 (64-128 cores) or Intel Xeon 5th Gen (32-60 cores): high core count for running many containers; per-core performance matters for control plane nodes

RAM

256 GB - 1 TB DDR5 ECC. Kubernetes pods consume memory dynamically; overcommitting RAM leads to OOMKills and pod evictions

Storage

Boot: 2 x 960 GB NVMe RAID 1; Container storage: 4-8 x 3.84 TB NVMe for local PVs, or networked storage (Ceph, Longhorn) for persistent volumes

Network

Dual 25GbE minimum for production clusters; 100GbE for high-throughput microservices; dedicated management network for control plane traffic

Recommended Server Family

2U dual-socket servers (Supermicro SYS-222H-TN24R, Dell PowerEdge R760, or equivalent) for worker nodes; 1U servers for control plane and etcd nodes

Enquire about 2U dual-socket servers (Supermicro SYS-222H-TN24R, Dell PowerEdge R760, or equivalent) for worker nodes; 1U servers for control plane and etcd nodes servers →

Common Mistakes

  • Running etcd on spinning disks. The etcd service requires low-latency storage. Always use NVMe SSDs for etcd data directories. Slow etcd causes cluster-wide instability.
  • Insufficient control plane resources. Kubernetes control plane nodes need at least 8 cores, 32 GB RAM, and fast NVMe storage. Under-provisioning causes API server timeouts in large clusters.
  • Using a flat network without CNI optimisation. Choose a performant CNI (Cilium, Calico with eBPF) and ensure adequate network bandwidth between nodes for pod-to-pod communication.
  • Not separating control plane and worker node hardware. Control plane nodes should be dedicated. Mixing control plane and workload containers risks cluster stability.
  • Ignoring node density planning. Packing too many pods per node without adequate CPU and memory leads to resource contention and unpredictable performance.

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