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How I avoided cloud GPU costs:

I needed ML inference for text similarity. Cloud GPU pricing is $700-1000/mo for instances that would sit idle most of the time. "Scale to zero" sounds great until you want a generous free tier.

My solution: the RTX 3080 I already own.

The stack (Elixir/Phoenix + Bumblebee):

  - Phoenix app on Fly.io

  - Bumblebee ML node on my home server (RTX 3080)

  - Tailscale + libcluster to connect them

From the code's perspective, they're one Erlang cluster. Where I can call the ML service like a local function. Tailscale tunnels the traffic, libcluster handles discovery. Capacity: ~1M comparisons/hour. I currently need a fraction of that.

Scaling: If I need more, I spin up cloud GPU nodes, connect them to my Tailscale network, and libcluster auto-discovers them. Zero code changes.

Happy to answer questions about any of this!