Private inference is becoming a builder problem, not just an infra thesis.
If your app handles prompts, memory, keys, or user data, the question is simple:
Can users verify runtime without exposing requests?
Thatβs the design space 0G builders should test now.
Eigen Labs knows private AI inference needs a bigger supply base.
Most private inference leans on TEEs. These work, but they usually do not run on the machines people already own, rather theyβre gated by specialized hardware.
Darkbloomβs bet is cleaner: use idle Apple Silicon.
Users send requests through chat or an OpenAI-compatible API. Eigenβs coordinator routes them to eligible Macs. Providers run the model without seeing the prompt, with Apple-style attestation checking hardware, security settings, and software integrity before work gets routed.
Early alpha numbers are real enough to watch:
> 600M tokens served
> 250 live providers at peak
> leading models priced at 50% below typical API providers
The hard part is provider economics. The calculator says some Macs COULD earn hundreds per month. The live leaderboard says the top earner made about $6 over 30 days.
That is not a supply-side incentive yet.
But it is also what makes Darkbloom interesting. There are no token subsidies inflating demand. The network ONLY works if real inference demand shows up, and stalls if earnings never pull in the high-memory Macs needed for better models.
Worth watching because itβs an honest test of consumer private AI infra, not just another DePIN flywheel.