The Raspberry Pi 5 brought desktop-class performance to single-board computing. But what if you could pack that same power into a professional cluster? Enter the Compute Module 5 (CM5)—the same BCM2712 chip, same performance, but designed for density and scalability.

If you’re choosing between buying multiple Pi 5 boards or building a CM5 cluster on Turing Pi 2.5, this guide will show you exactly what you gain, what you sacrifice, and which configuration fits your needs.

Quick Answer: CM5 modules give you identical Pi 5 performance in a cluster-optimized form factor. For single projects, buy a Pi 5. For multiple services, high availability, or professional setups, CM5 on Turing Pi 2.5 is the superior choice.

Raspberry Pi 5 Technology: The Foundation

What Makes Pi 5 Different

BCM2712 Processor:

  • CPU: 4x ARM Cortex-A76 cores @ 2.4GHz
  • GPU: VideoCore VII @ 800MHz (2x Pi 4 performance)
  • RAM: LPDDR4X-4267 (higher bandwidth than Pi 4)
  • I/O: PCIe 2.0, USB 3.0, dual HDMI 2.0

Real-World Performance:

  • Compiles code 2.5x faster than Pi 4
  • Handles 4K video transcoding
  • Runs multiple Docker containers smoothly
  • Desktop Linux experience (finally)

Power Requirements:

  • 5V/5A (25W max)
  • Idle: 2.7W
  • Typical usage: 4-7W
  • Under load: 8.8-17W

The Pi 5 was designed for desktop use, education, and standalone projects. It excels at these. But what about clusters?

CM5 8GB vs 16GB: Which RAM Configuration Do You Need?

Memory Requirements by Workload

CM5 8GB: Sweet Spot for Most Projects

Ideal Use Cases:

  • Kubernetes Worker Nodes: 8-12 pods per node
  • Media Streaming: Plex/Jellyfin with 2-3 simultaneous transcodes
  • Web Services: Nginx, Node.js apps, Redis, PostgreSQL
  • Network Services: Pi-hole, WireGuard VPN, reverse proxy
  • Development: VS Code server, Git, Docker builds

Real-World Memory Usage:

Kubernetes worker (typical):
- K3s agent: 1.2GB
- System overhead: 0.8GB
- Available for pods: 6GB
- Comfortable pod count: 8-12

Cost: ~$75-85 per module (estimated 2025 pricing, 16GB available later)

Recommendation: Start here unless you have specific 16GB needs. You can always upgrade individual nodes later.

Memory Performance: 8GB vs 16GB

Both use LPDDR4X-4267 (same as Pi 5):

  • Bandwidth: 34GB/s theoretical
  • Latency: Identical between 8GB and 16GB
  • Performance: No speed difference, only capacity

When 8GB Becomes a Bottleneck:

  • Running out of RAM triggers swap (100x slower)
  • OOM killer starts terminating processes
  • Kubernetes evicts pods under memory pressure

Pro Tip: Monitor memory usage on 8GB nodes for 2-4 weeks. Upgrade to 16GB only if you consistently hit 85%+ usage.

Turing Pi 2.5 + CM5 Cluster

Bill of Materials:

  • Turing Pi 2.5 board: $279
  • 4x CM5 8GB modules: $320
  • 4x NVMe SSDs (256GB): $120
  • 24-pin ATX power supply (300W): $45
  • Mini ITX case: $149
  • Total: ~$913

What You Get:

  • Single, clean enclosure
  • Integrated 1GbE network switch (no external switch)
  • BMC (Baseboard Management Controller):
    • Web UI for node management
    • USB/UART console access to each node
    • Individual node power control
    • Flash nodes without removing modules
    • Remote access over network
  • NVMe storage on all 4 nodes (10x faster than SD cards)
  • Hot-swap capability
  • Consumes 40-50W under load (20% more efficient)
  • Professional appearance

What You Give Up:

  • Higher upfront cost ($913 vs $535)
  • No HDMI output (headless only)
  • Need to buy modules separately (not standalone boards)

Performance: CM5 on Turing Pi vs Standalone Pi 5

Benchmark: Identical CPU/GPU Performance

Since CM5 uses the exact same BCM2712 chip as Pi 5, compute performance is identical:

Sysbench CPU (single-thread):

  • Pi 5 standalone: ~1,245 events/sec
  • CM5 on Turing Pi: ~1,245 events/sec
  • Difference: <1% (within margin of error)

Sysbench CPU (multi-thread):

  • Pi 5 standalone: 4,856 events/sec
  • CM5 on Turing Pi: 4,849 events/sec
  • Difference: <1%

7-Zip Compression:

  • Pi 5 standalone: 6,234 MIPS
  • CM5 on Turing Pi: 6,229 MIPS
  • Difference: <1%

Conclusion: There is no performance penalty using CM5 modules. The chip is identical.

Where CM5 on Turing Pi Wins: Networking

Turing Pi 2.5 Network Architecture:

  • Integrated Gigabit Ethernet switch
  • Direct board-to-board communication (no cable latency)
  • Each node: 940 Mbps throughput
  • Inter-node latency: 0.1-0.2ms

Pi 5 Cluster Network:

  • External 5-port switch
  • Cable length adds latency
  • Switch backplane varies by model
  • Inter-node latency: 0.3-0.5ms

Real-World Impact:

  • Kubernetes pod networking: 2x lower latency
  • Distributed databases: Better consistency
  • Ceph/GlusterFS: Faster replication

Winner: CM5 on Turing Pi (marginal but measurable)

Best Turing Pi 2.5 Cluster Configurations

Configuration 1: Budget Starter ($700)

Hardware:

  • Turing Pi 2.5 board: $279
  • 2x CM5 8GB: $160
  • 2x CM4 4GB (if you have them): $80
  • 2x NVMe SSDs (256GB): $60
  • ATX PSU: $45
  • Mini ITX case: $75

Total: ~$700

Use Cases:

  • Learning Kubernetes
  • Home media server (Plex/Jellyfin)
  • Network services (Pi-hole, DNS, VPN)
  • Development environment

Performance:

  • Mix CM5 and CM4 nodes
  • CM5 for workloads needing Pi 5 performance
  • CM4 for lighter services
  • Upgrade CM4 slots to CM5 later

Configuration 3: Power User ($1,200)

Hardware:

  • Turing Pi 2.5 board: $279
  • 2x CM5 16GB (control plane): $220
  • 2x CM5 8GB (workers): $160
  • 4x NVMe SSDs (1TB): $280
  • ATX PSU (550W): $65
  • Mini ITX case: $149
  • USB 10GbE adapter for BMC: $50

Total: ~$1,220

Use Cases:

  • AI/ML model inference
  • Heavy transcoding (4K HDR content)
  • Large databases (Elasticsearch, PostgreSQL)
  • CI/CD build farm
  • Multi-tenant hosting

Performance:

  • 48GB total RAM
  • 16GB nodes for stateful workloads
  • 8GB nodes for stateless services
  • 4TB total storage

Node Allocation:

  • Node 1 (CM5 16GB): Kubernetes control plane + etcd
  • Node 2 (CM5 16GB): Stateful services (databases, AI models)
  • Node 3 (CM5 8GB): Kubernetes worker (web apps)
  • Node 4 (CM5 8GB): Kubernetes worker (background jobs)

When to Choose Each Option

Choose 4x Raspberry Pi 5 Boards If…

✅ Learning/Education

  • First time with Raspberry Pi or Linux
  • Need hands-on GPIO access
  • Want to see HDMI output for troubleshooting
  • Budget under $500

✅ Portable Demos

  • Need to transport cluster to events/meetups
  • Want to show off individual boards
  • Require quick disassembly

✅ Flexible Projects

  • Might repurpose boards for non-cluster use
  • Need USB peripherals (cameras, sensors)
  • Want to experiment with different OS distributions

✅ Single-Project Focus

  • Running one service that needs high availability
  • Don’t need centralized management
  • Cable management doesn’t bother you

The Hybrid Approach: Start Pi 5, Upgrade to CM5

Many users start with Pi 5 boards to learn, then migrate to CM5 clusters for production:

Phase 1: Buy 2-4 Pi 5 boards

  • Learn Kubernetes, Docker, Linux
  • Experiment with services
  • Figure out what you actually need

Phase 2: Move to Turing Pi 2.5 + CM5

  • Keep Pi 5 boards for development/testing
  • Deploy production services to CM5 cluster
  • Enjoy better performance, management, and reliability

This path spreads cost over time and lets you learn before committing to cluster infrastructure.

Step 1: BMC Configuration

Initial BMC Setup:

# Change default password
Settings → Security → Change Password

# Set static IP (optional)
Settings → Network → Static IP: 192.168.1.100

# Update BMC firmware
Settings → Firmware → Check for Updates → Update

# Verify all 4 node slots detected
Dashboard → Node Status → All 4 slots should show "Empty"

Step 2: Network Configuration

Turing Pi Network Architecture:

  • Each CM5 has Gigabit Ethernet via onboard switch
  • Nodes communicate via eth0
  • BMC is separate network interface

Option A: Bridge to External Network (Recommended):

# On BMC web interface
Network → Bridge Mode → Enable
External Network: 192.168.1.0/24
Node IPs will be assigned by your router DHCP

# Result:
Node 1: 192.168.1.101 (accessible from LAN)
Node 2: 192.168.1.102
Node 3: 192.168.1.103
Node 4: 192.168.1.104
BMC: 192.168.1.100

Option B: Internal Network with NAT:

# On BMC
Network → NAT Mode → Enable
Internal subnet: 10.0.0.0/24
Node 1: 10.0.0.11
Node 2: 10.0.0.12
Node 3: 10.0.0.13
Node 4: 10.0.0.14
BMC gateway: 10.0.0.1

Step 3: Install Kubernetes (k3s)

On Node 1 (Control Plane):

# Install k3s server
curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="server \
  --disable traefik \
  --write-kubeconfig-mode 644 \
  --node-name node1 \
  --bind-address 192.168.1.101" sh -

# Get join token
sudo cat /var/lib/rancher/k3s/server/node-token
# Save this token for worker nodes

On Nodes 2-4 (Workers):

# Replace <TOKEN> with token from node1
# Replace <NODE1_IP> with 192.168.1.101

curl -sfL https://get.k3s.io | K3S_URL=https://<NODE1_IP>:6443 \
  K3S_TOKEN=<TOKEN> \
  INSTALL_K3S_EXEC="agent --node-name node2" sh -

# Repeat for node3, node4 (change --node-name)

Verify Cluster:

# On node1
kubectl get nodes

# Expected output:
NAME    STATUS   ROLES                  AGE   VERSION
node1   Ready    control-plane,master   5m    v1.28.4+k3s1
node2   Ready    <none>                 2m    v1.28.4+k3s1
node3   Ready    <none>                 2m    v1.28.4+k3s1
node4   Ready    <none>                 2m    v1.28.4+k3s1

Real Projects: Pi 5 Power in Cluster Form

Project 1: High-Availability Media Server

Hardware:

  • 2x CM5 16GB (for transcoding)
  • 2x CM5 8GB (for metadata, reverse proxy)

Software Stack:

  • Node 1 (CM5 16GB): Jellyfin primary (hardware transcoding)
  • Node 2 (CM5 16GB): Jellyfin replica (load balanced)
  • Node 3 (CM5 8GB): Caddy reverse proxy (HTTPS, auto SSL)
  • Node 4 (CM5 8GB): Sonarr, Radarr, qBittorrent

Performance:

  • 4-6 simultaneous 1080p→720p transcodes
  • Zero downtime during updates (update nodes one at a time)
  • Automatic failover if node fails

Project 3: AI/ML Inference Cluster

Hardware:

  • 4x CM5 16GB

Models You Can Run:

  • Whisper (speech-to-text): Base model fits in 1.5GB
  • Llama 7B (text generation): Quantized 4-bit fits in 4GB
  • YOLO (object detection): Real-time at 15-20 FPS
  • Stable Diffusion: 512×512 images in ~30 seconds

Example: Whisper Transcription Service:

# Deploy Whisper on 2 nodes for redundancy
kubectl apply -f whisper-deployment.yaml

# Submit audio file for transcription
curl -X POST -F "audio=@podcast.mp3" http://whisper.local/transcribe

# Load balanced across nodes automatically

Realistic Expectations:

  • Not for training (too slow)
  • Inference only, smaller models
  • Great for personal use, not production scale
  • Plan 16GB per node for AI workloads

More Project Ideas

Network Lab:

  • Run pfSense, VyOS, or OpenWRT in VMs
  • Simulate multi-site networks
  • Practice routing, firewalling, VLANs

Game Servers:

  • Minecraft Java (1GB RAM each)
  • Terraria, Valheim, 7 Days to Die
  • Deploy with one command, scale up/down

CI/CD Build Farm:

  • GitLab Runner on each node
  • Parallel builds (4 concurrent jobs)
  • ARM-native Docker image builds

BOINC Distributed Computing:

  • Contribute to science projects
  • Use idle CPU cycles
  • Kubernetes DaemonSet for automatic scaling

Is CM5 faster than Raspberry Pi 5?

No, they have identical performance. Both use the BCM2712 chip at 2.4GHz.

What IS faster on CM5:

  • Storage (NVMe vs MicroSD): 10-20x faster
  • Network latency (integrated switch vs external): 2x lower
  • Boot time (NVMe): 3x faster

What about Raspberry Pi 5 Compute Module Lite (no eMMC)?

CM5 Lite exists (no onboard eMMC storage).

Why Use CM5 Lite:

  • $20-30 cheaper per module
  • Boot from NVMe (faster than eMMC anyway)
  • More storage flexibility

Recommendation: For Turing Pi 2.5, CM5 Lite is the better choice. Use NVMe for storage.

Can I run Windows on CM5?

Windows on ARM64 is possible but not practical:

  • Windows 11 ARM64 exists
  • No official support for Raspberry Pi hardware
  • Poor driver support
  • Better to run Windows in a VM on Linux

Better Option: Use Linux + QEMU/KVM to run Windows VMs if needed.

Do I need cooling for CM5 modules?

Passive cooling is sufficient for most workloads.

Turing Pi 2.5 includes:

  • Heatsink mounting for each module
  • Case fan mounts (40mm or 80mm)

When You Need Active Cooling:

  • Sustained 100% CPU load (compiling, transcoding)
  • Ambient temperature >25°C
  • Running at 2.4GHz continuously

Simple Test:

# Monitor temperature
watch -n 1 'vcgencmd measure_temp'

# Throttling starts at 80°C
# Add fan if you hit 75°C+ under normal load

Conclusion: Which Should You Choose?

TL;DR Decision Matrix

Your SituationRecommendation
First Raspberry Pi, learning LinuxBuy 1x Pi 5 board
Learning Kubernetes, budget <$500Buy 2-3x Pi 5 boards
Running 3-5 services, want HATuring Pi 2.5 + 4x CM5 8GB
Production home lab, 10+ servicesTuring Pi 2.5 + 2x CM5 16GB + 2x CM5 8GB
AI/ML, heavy transcodingTuring Pi 2.5 + 4x CM5 16GB
Maximum performance, budget >$1.5kTuring Pi 2.5 + 2x CM5 16GB + 2x RK1 32GB

Start Small, Scale Smart

You don’t need to go all-in immediately:

Phase 1 ($80): Buy 1x Pi 5 board

  • Learn Linux, Docker, Kubernetes basics
  • Run single services
  • Figure out what you actually need

Phase 2 ($300): Add 2-3 more Pi 5 boards

  • Build a makeshift cluster
  • Experience the pain of cable management
  • Realize you want something better

Phase 3 ($900): Migrate to Turing Pi 2.5 + CM5

  • Keep Pi 5 boards for dev/testing
  • Deploy production to CM5 cluster
  • Never look back

Hardware Purchasing Guide

Where to Buy CM5 Modules

Official Sources:

  • Raspberry Pi Approved Resellers: rpilocator.com
  • CanaKit (US): Often has stock
  • Pimoroni (UK): Fast shipping to Europe
  • Adafruit (US): Reliable, good support

Price Watch (2025 estimates):

  • CM5 8GB: $75-85
  • CM5 16GB: $100-115
  • Expect stock shortages at launch

Budget (256GB, $30 each):

  • WD Blue SN570
  • Crucial P3

Balanced (512GB, $40-50 each):

  • Samsung 980
  • WD Blue SN580

Performance (1TB, $70-80 each):

  • Samsung 990 Pro
  • WD Black SN850X

Note: Avoid QLC drives (cheap, wear out quickly). Stick to TLC.

Ready to build? Start with the Home Lab Standard configuration (4x CM5 8GB) and scale from there. You won’t regret it.