Can Your Idle Mac Earn Money? A Look at Eigen Labs' Decentralized AI Inference Network Darkbloom

Compiled by: Felix, PANews

AI inference is gradually becoming one of the key layers of internet infrastructure. However, most inference currently still relies on centralized architectures, which are costly, have limited capacity, and are layered with certain security risks. Meanwhile, millions of powerful computers already exist globally but remain idle for most of the day.

Eigen Labs recently launched the AI inference network Darkbloom, exploring distributed AI inference on idle Mac computers. By combining verified nodes, hardware-level privacy protection, and better economic efficiency, it transforms idle Apple Silicon chips into a more efficient, privacy-first computing network.

The project launched around April this year as a research preview, upgraded to a public alpha in May, and is now live on the OpenRouter platform. In the alpha version, the available models are Google's Gemma 4 and OpenAI's GPT-OSS.

Core Architecture and Verifiable Privacy

Darkbloom's network consists of three parts: Users, Coordinators, and Providers.

Users can send inference requests via a chat interface or an OpenAI-compatible API.
Coordinators (operated by Eigen Labs) route these requests to eligible Macs on the network.
Providers (users who own these eligible Macs) run the model and return output results, but they cannot see the request content.

Darkbloom is built on a privacy-first distributed inference model. The provider process is hardened to resist common local inspection paths, including debugger attachment and external memory inspection. The integrity of the running binary is also part of the trust model, helping to ensure that the software serving requests meets network expectations.

The system also uses hardware-backed attestation based on Apple's security architecture. Secure Enclave keys, attestation signals, and periodic challenge-response checks are used to verify that participating nodes are running with the expected protections and software state, truly achieving verifiable privacy.

Economic Model and Daily Earnings

Darkbloom's business model is fundamentally different from the vast majority of projects. In traditional tech stacks, costs include hardware, facilities, cooling, networking, operational overhead, and multiple layers of profit. In Darkbloom's model, the hardware already exists, and marginal costs are primarily driven by electricity. Darkbloom's benchmark pricing is only about 50% of current mainstream API aggregators. Providers (Mac hosts) can retain 100% of the inference revenue. Furthermore, Darkbloom does not follow the path of issuing tokens to subsidize early participants; node earnings come entirely from real AI inference demand.

It is worth noting that, given the project's early stage of development, earnings are relatively modest. Factors such as memory and hardware configuration, uptime, model demand, node health, and network demand will all affect earnings to some extent.

Current leaderboard data shows that the top-ranked provider earns less than $6 per day, and the fifth-ranked provider earns less than $2. However, this situation is expected to improve as the network opens up to large language models with high memory demands and real user usage increases.

Regarding how to set up an idle Mac, the steps are as follows:

Obtain a Mac with an Apple Silicon chip
Ensure it runs macOS 14 or later
Install the Darkbloom provider
Keep the Mac online and connected to a stable internet connection
Let the network route supported AI tasks