Building a Raspberry Pi cluster used to mean wrestling with USB power supplies, network switches, and a mess of cables. The Turing Pi 2.5 changes everything – four compute modules on a single mini ITX board with integrated networking, centralized power, and a BMC for remote management.

Whether you’re learning Kubernetes, building high-availability services, or just want a power-efficient home lab, these 15 projects will show you what’s possible when you move from a single Pi to a proper cluster.

Why Build on a Cluster Instead of a Single Pi?

High Availability: When one node fails, others keep running Load Distribution: Split workloads across multiple nodes
Learning Platform: Practice real-world distributed systems at home
Power Efficiency: 40-50W total vs 150W+ for x86 servers
Scalability: Start small, add more powerful modules later

What You Need to Get Started

Minimum Setup (~$650):

  • Turing Pi 2.5 board: $279
  • 2x Raspberry Pi CM4 (4GB): ~$140 (estimated)
  • 24-pin PSU + power supply: $90
  • Mini ITX case: $149
  • MicroSD cards or NVMe storage: ~$50

Recommended Setup (~$950):

  • Turing Pi 2.5 board: $279
  • 2x Turing RK1 (8GB): $398
  • 2x Raspberry Pi CM4 (4GB): ~$140
  • 24-pin PSU + power supply: $90
  • Case + storage: ~$200

Kubernetes Learning Lab (k3s)

Difficulty: Intermediate to Advanced
Best Modules: 4x Raspberry Pi CM4 or 3x RK1 + 1x CM4
Power Draw: 30-50W

Learn production Kubernetes without the cloud bills or power consumption of a full x86 cluster.

Setup:

  • Node 1 (control plane): k3s master node
  • Nodes 2-4 (workers): Application workloads

What You’ll Learn:

  • Pod scheduling and orchestration
  • Service discovery and load balancing
  • Persistent storage with Longhorn or Rook-Ceph
  • GitOps with ArgoCD or Flux
  • Ingress controllers (Traefik, nginx)
  • Monitoring with Prometheus and Grafana

Real-World Applications:

  • Deploy containerized applications
  • Practice CI/CD pipelines
  • Test high-availability configurations
  • Learn multi-node networking
  • Experiment with service mesh (Linkerd, Istio)

Why Cluster Matters: Single-node Kubernetes misses the entire point -distributed scheduling, node failure handling, and load balancing only make sense with multiple nodes.

Getting Started:

# On Node 1 (master)
curl -sfL https://get.k3s.io | sh -

# Get token from master
sudo cat /var/lib/rancher/k3s/server/node-token

# On worker nodes
curl -sfL https://get.k3s.io | K3S_URL=https://master-ip:6443 K3S_TOKEN=<token> sh -

Network-Wide Ad Blocking (Pi-hole Cluster)

Difficulty: Beginner
Best Modules: 2-3x Raspberry Pi CM4
Power Draw: 10-15W

Deploy redundant DNS servers so ad blocking never goes down, even during maintenance.

Setup:

  • Nodes 1-2: Pi-hole instances with Gravity Sync
  • Node 3: Unbound recursive DNS for privacy
  • Node 4: Available for other services

Benefits:

  • 99.9% uptime for DNS (no single point of failure)
  • Load balancing across multiple Pi-hole instances
  • Automatic blocklist synchronization
  • Recursive DNS = no queries sent to Google/Cloudflare

Configuration:

# Install Pi-hole on nodes 1 and 2
curl -sSL https://install.pi-hole.net | bash

# Set up Gravity Sync for blocklist synchronization
# Configure router to use both Pi-hole IPs as DNS servers

Performance:

  • Handles 1000+ devices easily
  • <10ms query response time
  • Blocks 30-40% of all DNS requests (ads/trackers)

Minecraft/Game Server Cluster

Difficulty: Intermediate
Best Modules: 2-3x Turing RK1
Power Draw: 30-45W

Run multiple game servers or a high-capacity Minecraft server with automatic failover.

Supported Games:

  • Minecraft (Java and Bedrock)
  • Terraria
  • Valheim
  • 7 Days to Die
  • ARK: Survival Evolved (limited)

Minecraft Cluster Benefits:

  • Separate lobby/hub server and game worlds
  • Load balance across multiple instances
  • Dedicated node for world backups
  • BungeeCord for multi-server networking

Setup Example:

  • Node 1 (RK1): Lobby server
  • Node 2 (RK1): Survival world
  • Node 3 (RK1): Creative/minigames world
  • Node 4 (CM4): Automated backups and monitoring

Performance:

  • 20-30 players per RK1 node (Minecraft Java)
  • 40-50 players per RK1 node (Bedrock)
  • Automatic world backups every 30 minutes
  • Rolling restarts without kicking all players

CI/CD Pipeline (GitLab Runner + Registry)

Difficulty: Advanced
Best Modules: 4x Turing RK1 (for serious builds)
Power Draw: 50-60W

Run your own continuous integration and container registry without paying for GitHub Actions or Docker Hub.

Setup:

  • Node 1 (RK1): GitLab CE or Gitea
  • Node 2 (RK1): Docker registry
  • Nodes 3-4 (RK1): GitLab runners for parallel builds

What It Handles:

  • Automated testing on every commit
  • Build ARM64 Docker images natively
  • Deploy to Kubernetes automatically
  • Run code quality checks (linters, security scans)
  • Generate documentation
  • Multi-architecture builds (ARM + x86 with QEMU)

Build Performance:

  • Small Node.js project: 2-3 minutes
  • Medium Go application: 4-6 minutes
  • Large Rust project: 10-15 minutes
  • Docker image build + push: 3-5 minutes

Why Cluster: Parallel runners mean multiple builds run simultaneously instead of queueing. A 4-node cluster can run 8-12 jobs concurrently.

Distributed Computing (BOINC)

Difficulty: Beginner
Best Modules: 4x Raspberry Pi CM4 or 4x Turing RK1
Power Draw: 30-50W (at 100% load)

Contribute to scientific research projects like protein folding, climate modeling, or searching for extraterrestrial intelligence.

Setup: Deploy BOINC clients on all nodes and join projects like:

  • Rosetta@home: Protein structure prediction
  • World Community Grid: Medical research
  • SETI@home: Search for alien signals
  • Einstein@home: Gravitational wave detection
  • Folding@home: Disease research

Cluster Advantages:

  • 4x the computing power of a single Pi
  • Distribute different projects across nodes
  • Better thermal management (spread across nodes)
  • Easy to dedicate specific nodes to specific projects

Performance:

  • CM4 cluster: ~500-800 credits/day per node
  • RK1 cluster: ~1,200-2,000 credits/day per node
  • Total contribution: Equivalent to 2-3 desktop PCs running 24/7

Power Efficiency: Unlike gaming PCs pulling 300-500W, a Turing Pi cluster contributes meaningful compute at 30-50W.

Network Attached Storage (NAS)

Difficulty: Beginner to Intermediate
Best Modules: 4x Raspberry Pi CM4 or 2x RK1 + 2x CM4
Power Draw: 25-40W (plus external drives)

Build a resilient NAS with distributed storage that survives drive failures.

Storage Strategies:

Option 1: Simple NFS/Samba Share

  • Node 1: Primary storage server
  • Node 2: Backup replication with rsync
  • Nodes 3-4: Other services

Option 2: GlusterFS (Distributed Storage)

  • Nodes 1-4: Each contributes storage, data replicated across nodes
  • Survives 1-2 node failures depending on replication factor
  • Scales horizontally as you add nodes

Option 3: MinIO (S3-Compatible)

  • Nodes 1-4: Distributed object storage
  • S3 API compatibility (works with thousands of apps)
  • Erasure coding for redundancy

Use Cases:

  • Time Machine backups (Mac)
  • Photo/video storage
  • Document archive
  • Security camera recordings
  • VM/container image storage

Performance:

  • Read: 100-300 MB/s (limited by Gigabit Ethernet)
  • Write: 80-200 MB/s
  • IOPS: 1,000-3,000 (NVMe), 100-300 (external USB)

Capacity: Limited only by your drives (10TB+ easy)

Development Environment Cluster

Difficulty: Intermediate
Best Modules: 3x Turing RK1 + 1x CM4
Power Draw: 40-50W

Create isolated development environments that mirror production without the cloud bills.

Setup:

  • Node 1 (RK1): Development databases (PostgreSQL, MySQL, Redis)
  • Node 2 (RK1): Web application testing environment
  • Node 3 (RK1): Build server and code-server (VS Code in browser)
  • Node 4 (CM4): Git server (Gitea or GitLab)

Use Cases:

Web Development:

  • Test applications in distributed environment
  • Simulate load balancing and failover
  • Database replication testing
  • Microservices development

Mobile/IoT Development:

  • Test API backends
  • Simulate real-world network conditions
  • MQTT broker for IoT device testing
  • Local crash reporting server

Learning Platform:

  • Practice DevOps tools
  • Learn infrastructure-as-code (Terraform, Ansible)
  • Test deployment strategies
  • Database administration practice

Why Cluster Beats Single Pi: Real production environments are distributed. Testing on a cluster catches issues that single-node testing misses – network partitions, race conditions, split-brain scenarios.

Hardware Recommendations by Use Case

Budget Build ($650 – Learning Focus):

  • 4x Raspberry Pi CM4 (4GB)
  • Perfect for: Kubernetes, Docker Swarm, Pi-hole, BOINC

Balanced Build ($950 – Best All-Around):

  • 2x Turing RK1 (8GB)
  • 2x Raspberry Pi CM4 (4GB)
  • Perfect for: Media server, NAS, CI/CD, Home Assistant

Performance Build ($1,400 – Maximum Power):

  • 4x Turing RK1 (8GB)
  • Perfect for: AI/ML, heavy transcoding, large-scale CI/CD

Specialized Build ($1,600 – ML/AI Focus):

  • 3x Turing RK1 (16GB)
  • 1x NVIDIA Jetson Orin Nano
  • Perfect for: Computer vision, large model inference

Power Consumption Comparison

Turing Pi Clusters:

  • 4x CM4: 20-30W idle, 35-45W load
  • 2x RK1 + 2x CM4: 30-40W idle, 50-60W load
  • 4x RK1: 35-50W idle, 60-80W load

Equivalent x86 Servers:

  • Entry-level tower: 80-150W idle, 200-300W load
  • Rackmount server: 100-200W idle, 300-500W load
  • Workstation: 100-250W idle, 400-600W load

Annual Power Cost (at $0.16/kWh, 24/7 operation):

  • Turing Pi (50W avg): $70/year
  • Entry x86 (150W avg): $210/year
  • Rackmount (300W avg): $420/year

3-Year Savings: $420-1,050 with Turing Pi vs x86

Community Resources

Documentation:

Forums & Communities:

  • Reddit: r/homelab, r/selfhosted, r/TuringPi
  • YouTube: NetworkChuck, Jeff Geerling, TechnoTim

Configuration Examples:

Conclusion

These 15 projects represent just the beginning of what’s possible with a Turing Pi cluster. The beauty of the platform is that you can run multiple projects simultaneously – host a media server while learning Kubernetes, run Pi-hole while contributing to BOINC, or create a development environment that mirrors production.

The combination of low power consumption, manageable complexity, and real-world applicability makes the Turing Pi 2.5 the perfect platform for anyone serious about homelab, edge computing, or distributed systems.

Start simple with Docker Swarm or Pi-hole, scale gradually to Kubernetes or CI/CD, and experiment constantly. Every project you build teaches skills that apply directly to production cloud environments – but without the bills.