https://darkbloom.dev Darkbloom Motivation Approach Implementation Results Operator Economics Research Preview -- An Eigen Labs Initiative 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. Use Darkbloom / Start Earning / Read the Paper / User / App encrypted Coordinator routes Mac Studio verified MacBook Pro verified Mac Mini verified response 01 -- What this enables 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 The AI compute market has three layers of margin. 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. Current supply chain NVIDIA - AWS Google Cloud Azure CoreWeave - API providers - End users This concentrates both wealth and access. A small number of companies control the supply. Everyone else rents. 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. 100M+ Apple Silicon machines shipped since 2020 3x+ markup from silicon to end-user API price 18hrs average daily idle time per machine 100% of revenue goes to the hardware owner 03 -- The Challenge 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. Encryption 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 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. Runtime 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. Output 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. E2E Encryption encrypted before it leaves your device OS Integrity SIP enforced * signed system volume * binary self-hash Memory Isolation Hypervisor.framework * Stage 2 page tables Hardened Process debugger blocked * no shell access Your inference data prompts * responses * model state | operator is here -- every path inward is eliminated 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="") Streaming -- SSE, OpenAI format Image generation -- FLUX.2 on Metal Speech-to-text -- Cohere Transcribe Large MoE -- up to 239B params 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. Model Input Output OpenRouter Savings Gemma 4 26B4B active, fast multimodal $0.03 $0.20 $0.40 50% MoE Qwen3.5 27BDense, frontier reasoning $0.10 $0.78 $1.56 50% Qwen3.5 122B MoE10B active, best $0.13 $1.04 $2.08 50% quality MiniMax M2.5 239B11B active, SOTA $0.06 $0.50 $1.00 50% coding Prices per million tokens Image Generation $0.0015 per image Together.ai: $0.003 Speech-to-Text $0.001 per audio minute AssemblyAI: $0.002 Platform Fee 0% operators keep 100% transparent 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. 100% revenue goes to you ~90% profit margin CLI Mac App Install via Terminal Downloads the provider binary and configures a launchd service. terminal $ curl -fsSL https://api.darkbloom.dev/install.sh | bash No dependenciesAuto-updatesRuns as launchd service Native macOS Menu Bar App In Development One-click install. Runs in your menu bar. Currently in active development -- use the CLI for now. Coming soon Apple Silicon onlymacOS 14+Includes CLI + backend Earnings estimate Select hardware to model projected operator earnings. Machine Chip Memory Hours per day: 18[18 ] Text -- $0 $0 / year Revenue $0 Electricity -$0 Image -- $0 $0 / year Revenue $0 Electricity -$0 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. Gemma 4 26B Google's latest -- fast multimodal MoE, 4B active params text Qwen3.5 27B Dense, frontier-quality reasoning (Claude Opus distilled) text Qwen3.5 122B MoE i 10B active -- best quality per token text MiniMax M2.5 239B i SOTA coding, 11B active, 100 tok/s on Mac Studio text Cohere Transcribe 2B conformer -- best-in-class speech-to-text audio Darkbloom * Eigen Labs * 2026 Paper Console GitHub X LinkedIn