Mini PC vs Raspberry Pi Cluster for a Home AI Lab

Pros

• x86 compatibility: runs any Docker image, Ubuntu, Debian — full software compatibility
• 32–64 GB DDR5: Llama 3 8B runs comfortably in RAM; some 16B models fit with quantization
• NVMe SSD: 2+ GB/s model load speeds vs Pi's microSD or USB SSD bottleneck
• Single-device simplicity: no networking overhead, cluster management, or distributed debugging
• NPU on Ryzen AI models: on-device inference acceleration for sub-7B models

Cons

• Integrated GPU only: no CUDA — LLM inference is CPU/NPU-bound, not GPU-accelerated at data-center level
• Single failure point: one device failure takes down all services
• Limited GPIO: no hardware peripherals without USB adapters — not suited for IoT sensor projects
• Higher idle power: 10–25W vs Pi's 2–5W — minor at home scale but relevant 24/7

Pros

• Distributed systems learning: excellent platform for k3s, k8s, MPI, or Spark hands-on education
• GPIO on every node: each Pi has 40-pin GPIO — ideal for sensor arrays and hardware integration
• Low power: 4-node cluster at ~15–20W total vs mini PC's 25W — comparable at cluster scale
• Fault tolerance: one node can fail without taking down the entire cluster
• Community resources: massive Pi cluster tutorials — Kubernetes The Hard Way, kubeadm guides

Cons

• ARM64 compatibility gaps: some Docker images lack arm64 builds — causes headaches with older ML tools
• Limited per-node RAM: Pi 5 max 8 GB — 32 GB total for a 4-node cluster vs 32 GB on a single mini PC
• microSD bottleneck: boot from SD card unless all nodes use USB SSD — extra cost and setup
• LLM inference: Llama 3 8B distributed across 4 ARM cores is slower and more complex than a single x86 CPU
• Setup overhead: cluster networking, node provisioning, and SSH key management add hours before first workload