Darkbloom – Private inference on idle Macs

Eigen Labs Research

Private inference on idle Macs

We present Darkbloom, a decentralized inference network. AI compute today flows through three layers of markup — GPU manufacturers to hyperscalers to API providers to end users. Meanwhile, over 100 million Apple Silicon machines sit idle for most of each day. We built a network that connects them directly to demand. Operators cannot observe inference data. The API is OpenAI-compatible. Our measurements show up to 70% lower costs compared to centralized alternatives. Operators retain 95% of revenue.

For users

Inference at half the cost

Idle hardware has near-zero marginal cost. That saving passes through to price. OpenAI-compatible API for chat, image generation, and speech-to-text. Every request is end-to-end encrypted.

Open Console ↗

For hardware owners

Earn USD from idle Apple Silicon

Your Mac already has the hardware. Operators keep 100% of inference revenue. Electricity cost on Apple Silicon runs $0.01–0.03 per hour depending on workload. The rest is profit.

Start Earning ↗

02 — Motivation

Current supply chain → → API providers → End users

Other decentralized compute networks connect buyers and sellers. That is the easy part.

The hard part is trust. You are sending prompts to a machine you do not own, operated by someone you have never met. Your company's internal data. Your users' conversations. Your competitive advantage, running on hardware in someone else's house.

No enterprise will do this without guarantees stronger than a terms-of-service document.

Without verifiable privacy, decentralized inference does not work.

04 — Our Approach

Access path elimination

We eliminate every software path through which an operator could observe inference data. Four independent layers, each independently verifiable.

The operator runs your inference. They cannot see your data.

Prompts are encrypted before they leave your machine. The coordinator routes traffic it cannot read. The provider decrypts inside a hardened process it cannot inspect. The attestation chain is public.

Read the paper ↗

05 — Implementation

OpenAI-compatible API

Change the base URL. Everything else works. Streaming, function calling, all existing SDKs.

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.darkbloom.dev/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="mlx-community/gemma-4-26b-a4b-it-8bit",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="")

06 — Results

Cost comparison

Idle hardware has near-zero marginal cost, so the savings pass through. No subscriptions or minimums. Per-token pricing compared against OpenRouter equivalents.

07 — Operator Economics

Operator economics

Operators contribute idle Apple Silicon and earn USD. 100% of inference revenue goes to the operator. The only variable cost is electricity.

Install via Terminal

Downloads the provider binary and configures a launchd service.

$ curl -fsSL https://api.darkbloom.dev/install.sh | bash

No dependenciesAuto-updatesRuns as launchd service

Earnings estimate

Select hardware to model projected operator earnings.

Hours per day: 18

Estimates only. Actual results depend on network demand and model popularity. Assumes you own the Mac.

Read the research paper

Architecture specification, threat model, security analysis, and economic model for hardware-verified private inference on distributed Apple Silicon.

Download PDF ↗

Model Catalog

Available models

Curated for quality. Only models worth paying for.