I Feel Like This Has A Home Here

I Feel Like This Has A Home Here: The Art of Purposeful Infrastructure in Modern Homelabs

Introduction

The Reddit post that inspired this article perfectly captures a common phenomenon in our community: “I just got some Gen 12 DL380 servers filled with H100s. Can it run Crysis? Also what containers run on it?” This sentiment - equal parts enthusiasm and uncertainty - represents a critical junction in modern infrastructure management.

In an era where powerful hardware is increasingly accessible, many skilled professionals find themselves straddling the line between enterprise-grade operations and experimental homelab environments. This tension creates both opportunities for innovation and risks of resource misallocation. The “I don’t know what I’m doing, but I’m doing it!” mentality embodies the hacker spirit that drives our field, but requires proper scaffolding to transform random experimentation into professional growth.

For DevOps engineers and sysadmins, homelabs serve as vital proving grounds for:

• Emerging technologies (GPU-accelerated workloads, AI/ML pipelines)
• Infrastructure-as-Code validation
• Security hardening experiments
• Hybrid cloud implementations

This comprehensive guide will transform ad hoc hardware acquisition into strategic infrastructure deployment. We’ll cover everything from initial hardware evaluation to production-grade container orchestration, focusing on extracting maximum professional value from your homelab investments.

Understanding Modern Homelab Infrastructure

The Evolution of Purpose-Built Labs

Homelabs have evolved from simple virtualization playgrounds to sophisticated micro-datacenters. Modern setups like Gen 12 HPE DL380 servers with NVIDIA H100 GPUs represent enterprise-grade capabilities now accessible to individuals. Key considerations include:

• Hardware/Software Parity: Matching enterprise environments (vSphere vs Proxmox, NVIDIA Enterprise vs Consumer GPUs)
• Energy Efficiency: Power consumption management for 24/7 operation
• Noise/Thermal Management: Enterprise gear in residential environments
• Lifecycle Alignment: Hardware refresh cycles matching learning objectives

Strategic Value Assessment Matrix

Criterion	Homelab Focus	Enterprise Focus	Hybrid Approach
Hardware Selection	Maximize feature exposure	Stability and support	Enterprise-grade refurb
Workload Priority	Learning/experimentation	Business continuity	Staging environments
Scaling Strategy	Vertical expansion	Horizontal scaling	Mixed approach
Failure Tolerance	Embrace break/fix	Five-nines reliability	Isolated failure domains

The Containerization Conundrum

The Reddit poster’s question “what containers run on it?” reveals a common anti-pattern - technology searching for problems. Proper container strategy requires:

1. Workload identification (stateful vs stateless)
2. Orchestration requirements (Kubernetes vs Docker Swarm)
3. Hardware utilization mapping (GPU passthrough, NUMA alignment)
4. Storage architecture (CEPH vs NFS vs direct-attached)

Prerequisites for Professional-Grade Homelabs

Hardware Specifications

For Gen 12 HPE DL380 servers with H100 GPUs:

• Minimum Configuration:
  • Dual Intel Xeon Scalable Processors (Ice Lake or newer)
  • 256GB DDR4 ECC RAM (8x32GB DIMMs)
  • HPE Smart Array Controller (configured in HBA mode for software RAID)
  • 4x 1TB NVMe drives (RAID10 for performance)
  • NVIDIA H100 GPUs with proper cooling solution
• Network Requirements:
  • 10GbE minimum (25GbE recommended)
  • VLAN-capable switch for network segmentation
  • Dedicated BMC/IPMI network

Software Foundation

• Base OS: Ubuntu 22.04 LTS (kernel 5.15+ for H100 support)
• Virtualization: Proxmox VE 7.4+ or VMware ESXi 8.0
• Container Runtime: containerd 1.7+ with NVIDIA Container Toolkit
• Orchestration: Kubernetes 1.27+ or Docker Swarm (for lighter workloads)

Security Pre-Configuration

1. Hardware Security Module (HSM) initialization
2. UEFI Secure Boot configuration
3. TPM 2.0 attestation setup
4. IPMI/BMC hardening (disable default credentials)

Installation & Configuration Walkthrough

Proxmox VE Installation with GPU Passthrough

# Download Proxmox VE installer
wget https://enterprise.proxmox.com/iso/proxmox-ve_7.4-1.iso

# Verify checksum
echo "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6 *proxmox-ve_7.4-1.iso" | md5sum -c

# Create bootable USB (replace /dev/sdX with your device)
dd if=proxmox-ve_7.4-1.iso of=/dev/sdX bs=4M status=progress conv=fdatasync

Post-install GPU configuration:

# Identify GPU PCI addresses
lspci -nn | grep NVIDIA

# Add to kernel modules
echo "vfio" >> /etc/modules
echo "vfio_iommu_type1" >> /etc/modules
echo "vfio_pci" >> /etc/modules

# Configure GPU passthrough
nano /etc/modprobe.d/vfio.conf
options vfio-pci ids=10de:20b5,10de:20f3 disable_vga=1

Kubernetes Cluster Setup with GPU Support

# Install kubeadm, kubelet and kubectl
apt-get update && apt-get install -y apt-transport-https ca-certificates curl
curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo gpg --dearmor -o /etc/apt/keyrings/kubernetes-archive-keyring.gpg
echo "deb [signed-by=/etc/apt/keyrings/kubernetes-archive-keyring.gpg] https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee /etc/apt/sources.list.d/kubernetes.list
apt-get update && apt-get install -y kubelet=1.27.4-00 kubeadm=1.27.4-00 kubectl=1.27.4-00

# Initialize cluster
kubeadm init --pod-network-cidr=192.168.0.0/16 --apiserver-advertise-address=10.0.0.100

# Install NVIDIA device plugin
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.0/nvidia-device-plugin.yml

Workload Optimization Strategies

GPU-Accelerated Container Configuration

# nvidia-device-plugin-daemonset.yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nvidia-device-plugin-daemonset
  namespace: kube-system
spec:
  selector:
    matchLabels:
      name: nvidia-device-plugin-ds
  template:
    metadata:
      labels:
        name: nvidia-device-plugin-ds
    spec:
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      containers:
      - image: nvcr.io/nvidia/k8s-device-plugin:v0.14.0
        name: nvidia-device-plugin-ctr
        securityContext:
          allowPrivilegeEscalation: false
          capabilities:
            drop: ["ALL"]
        volumeMounts:
          - name: device-plugin
            mountPath: /var/lib/kubelet/device-plugins
      volumes:
        - name: device-plugin
          hostPath:
            path: /var/lib/kubelet/device-plugins

Performance Tuning Parameters

Critical /etc/sysctl.conf optimizations:

# Network optimizations
net.core.rmem_max=268435456
net.core.wmem_max=268435456
net.ipv4.tcp_rmem=4096 87380 268435456
net.ipv4.tcp_wmem=4096 65536 268435456

# Virtualization improvements
vm.swappiness=10
vm.dirty_ratio=40
vm.dirty_background_ratio=10
vm.max_map_count=262144

# GPU-specific
nvidia.nvidia_mem_limit=95

Operational Management

Monitoring Stack Deployment

GPU-specific Prometheus exporter setup:

# Install NVIDIA DCGM exporter
docker run -d --gpus all --restart=always -p 9400:9400 nvcr.io/nvidia/k8s/dcgm-exporter:3.3.0-3.1.0-ubuntu20.04

# Prometheus configuration
scrape_configs:
  - job_name: 'dcgm'
    static_configs:
      - targets: ['10.0.0.100:9400']

Automated Maintenance Script

#!/bin/bash
# homelab-maintenance.sh
export KUBECONFIG=/etc/kubernetes/admin.conf

# Drain worker nodes sequentially
for node in $(kubectl get nodes -o jsonpath='{.items[?(@.metadata.labels.node-role\.kubernetes\.io/worker=="true")].metadata.name}'); do
  kubectl drain $node --ignore-daemonsets --delete-emptydir-data
  ssh $node "apt update && apt upgrade -y && reboot"
  while ! nc -z $node 22; do sleep 5; done
  kubectl uncordon $node
done

# Proxmox host maintenance
pvecm update
pveam update
apt dist-upgrade -y
reboot

Troubleshooting Guide

Common Issues and Solutions

Problem: GPU not recognized in containers
Diagnosis:

# Check device plugin status
kubectl describe node | grep -A10 Capacity

# Verify NVIDIA drivers
nvidia-smi -q | grep "Driver Version"

Solution: Ensure consistent driver versions across hosts and container runtime

Problem: High power consumption
Diagnosis:

# Check power caps
nvidia-smi -pl
ipmitool dcmi power reading

Solution: Set GPU power limits:

nvidia-smi -pl 250

Performance Benchmarking

Validating GPU performance with DCGM:

# Run diagnostic mode
dcgmi diag -r 1

Conclusion

The journey from “Can it run Crysis?” to purposeful infrastructure embodies the professional growth possible in modern homelabs. By applying enterprise-grade practices to personal hardware, we transform random experimentation into career-accelerating experience.

Key takeaways:

1. Align hardware acquisitions with specific learning objectives
2. Implement production-grade security from day one
3. Use infrastructure-as-code principles even for personal projects
4. Maintain rigorous monitoring and documentation
5. Participate in communities (r/homelab, DevOps forums) for knowledge sharing

For continued learning:

• NVIDIA Container Toolkit Documentation
• Proxmox VE Administration Guide
• Kubernetes GPU Scheduling

The true value of homelabs lies not in raw computational power, but in the professional rigor we apply to managing these systems. When approached methodically, even the most whimsical “can it run?” question becomes a gateway to deeper infrastructure mastery.