WPRC KL EP13: Private Distributed Inference on Consumer Hardware
Private Distributed Inference on Consumer Hardware
The Reading
Over 100 million Apple Silicon Macs sit idle most of the day, each capable of running frontier-scale models. Eigen Labs' Darkbloom rents that idle capacity out as an OpenAI-compatible inference network and claims the person who owns the Mac cannot read the prompts their own machine is processing.
That claim is the whole session. Apple Silicon has no SGX-style TEE for third-party code, so the paper builds the guarantee out of Secure Enclave identity, Apple-signed attestation, MDM cross-checks and a hardened process instead. Does it hold?
Paper: Private Distributed Inference on Consumer Hardware (Gajesh Naik, Eigen Labs, April 2026)
What we'll argue about
If your hardware runs someone else's inference, what actually stops you from reading it?
Is a four-layer chain a substitute, or a weaker promise wearing the same words?
Where does plaintext exist, and for how long?
How this compares to Apple's Private Cloud Compute and NEAR's TEE-based approach.
Whether the provider economics survive contact with electricity and depreciation.
This is not a workshop. We're not setting up local inference on your MacBook. We're reading a paper and pressure-testing its trust model.
Paper / source: https://github.com/Layr-Labs/d-inference/blob/master/papers/dginf-private-inference.pdf
Format
Short introduction and context setting to start.
A short walkthrough of the paper and its key ideas.
Questions and discussion grounded in the original source.
Moderator-led discussion with no promotions.
Session Guides:
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