I regret building this $3000 Pi AI cluster

Original Article ↗ Hacker News Discussion ↗

Pi clusters: fun toy vs serious tool

  • Many see Raspberry Pi clusters as a “nerd indulgence”: fun and educational, but rarely a sensible way to get real work done.
  • As single nodes, Pis are praised for low idle power and simplicity (Pi-hole, tiny web servers, NAS, Home Assistant, k8s control planes).
  • Once you start clustering them, most argue you’re almost always better off with a single purpose‑built machine for the same or lower cost.

Cost, performance, and better alternatives

  • Repeated theme: if Pi clusters were cost‑competitive, data centers would be full of them; they aren’t.
  • For homelab/server use, cheap mini‑PCs, used corporate desktops, or N100/Ryzen boxes often beat Pi 5 on perf/$, IO, and features (RTC, proper NICs, SSDs).
  • Old Xeon/Epyc servers give huge core/RAM counts very cheaply, but are loud and power‑hungry; power costs and noise are a major concern.
  • For learning clusters, many recommend: one multi‑core box + VMs or containers instead of a pile of SBCs.

AI/LLM workloads and GPUs

  • Commenters are unsurprised the Pi AI cluster is slow: RAM bandwidth is low, NICs are 1 Gbit, GPUs are effectively unusable, and clustering overhead dominates.
  • LLM clustering in llama.cpp is described as naïve (round‑robin across nodes) rather than true parallelization; interconnect latency would still bite even if improved.
  • Consensus: for AI:
    • Use a single GPU box (e.g., consumer RTX, Mac Studio, Ryzen AI, small “AI NUC”) or rent cloud GPUs.
    • Pi clusters are the wrong architecture for modern LLMs, even at large node counts.

Use cases, pedagogy, and nostalgia

  • Some defend Pi clusters for:
    • Learning distributed systems, networking topologies, MPI, k8s HA, etc.
    • University teaching/research clusters and hobby experiments.
  • Others say the same learning is cheaper and easier with cloud VMs or one big machine with many VMs.
  • Thread is full of Beowulf‑cluster nostalgia; the Pi build is often framed as the modern equivalent—about learning, not winning benchmarks.

YouTube economics and “regret” framing

  • Several note the project makes sense as content: a $3,000 cluster can pay for itself in views and sponsorships.
  • The “I regret…” title is widely called clickbait but also seen as necessary in the YouTube attention economy.
  • Multiple commenters stress: the author’s economics (sponsorships, Patreon, large audience) are not those of a typical hobbyist, so the “regret” lesson is mainly about practicality, not that the build wasn’t “worth it” to him professionally.