Darkbloom – Private inference on idle Macs

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.

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.

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.

NVIDIA sells GPUs to hyperscalers. AWS, Google, Azure, and CoreWeave mark them up and rent capacity to AI companies. AI companies mark them up again and charge end users per token. Each layer takes a cut. End users pay multiples of what the silicon actually costs to run.

Meanwhile, Apple has shipped over 100 million machines with serious ML hardware. Unified memory architectures. 273 to 819 GB/s memory bandwidth. Neural Engines. Machines capable of running 235-billion-parameter models. Most sit idle 18 or more hours a day. Their owners earn nothing from this compute.

That is not a technology problem. It is a marketplace problem.

The pattern is familiar. Airbnb connected idle rooms to travelers. Uber connected idle cars to riders. Rooftop solar turned idle rooftops into energy assets. In each case, distributed idle capacity undercut centralized incumbents on price because the marginal cost was near zero.

Darkbloom does this for AI compute. Idle Macs serve inference. Users pay less because there is no hyperscaler in the middle. Operators earn from hardware they already own. Unlike those other networks, the operator cannot see the user's data.

Access path elimination

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

Encrypted end-to-end

Requests are encrypted on the user's device before transmission. The coordinator routes ciphertext. Only the target node's hardware-bound key can decrypt.

Hardware-verified

Each node holds a key generated inside Apple's tamper-resistant secure hardware. The attestation chain traces back to Apple's root certificate authority.

Hardened runtime

The inference process is locked at the OS level. Debugger attachment is blocked. Memory inspection is blocked. The operator cannot extract data from a running process.

Traceable to hardware

Every response is signed by the specific machine that produced it. The full attestation chain is published. Anyone can verify it independently.

OpenAI-compatible API

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

Streaming— SSE, OpenAI format Image generation— FLUX.2 on Metal Speech-to-text— Cohere Transcribe Large MoE— up to 239B params

Cost comparison

| Model | Input | Output | OpenRouter | Savings | |---|---|---|---|---| | Gemma 4 26B | $0.03 | $0.20 | $0.40 | 50% | | Qwen3.5 27B | $0.10 | $0.78 | $1.56 | 50% | | Qwen3.5 122B MoE | $0.13 | $1.04 | $2.08 | 50% | | MiniMax M2.5 239B | $0.06 | $0.50 | $1.00 | 50% |