原文 · 未翻译
Cost-efficient private AI inference
Darkbloom routes encrypted requests to hardware-verified Apple Silicon providers, delivering comparable model performance at about 50% lower cost than typical API providers. Prompts stay hidden from operators, and Mac owners earn from compute they already own.
Private inference without a new SDK
Change the base URL and keep your existing OpenAI client. Requests are encrypted before they leave your app and routed to verified Apple Silicon providers.
Turn idle Apple Silicon into earnings
Run a provider on hardware you already own. Darkbloom matches your Mac with inference demand, and operators keep 100% of inference revenue during the public alpha.
Operator-blind by design
Darkbloom removes the practical software paths an operator could use to observe inference data. Four layers work together, each independently verifiable.
Encrypted end-to-end
Requests are encrypted before transmission. The coordinator routes ciphertext, and only the matched provider's hardware-bound key can decrypt the request.
Hardware-verified
Each provider uses 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 down at the OS level. Debugger attachment and memory inspection are blocked so the operator cannot inspect a running request.
Traceable to hardware
Responses are signed by the specific machine that produced them. The attestation chain is public, so users can verify the hardware behind the result.
The operator contributes compute, not visibility.
Your prompt is encrypted before it leaves your app. The coordinator routes traffic it cannot read. The provider serves the request inside a hardened process the operator cannot inspect.
OpenAI-compatible API
Keep your SDK, request shape, and streaming code. Point the client at Darkbloom and start routing private inference.
from openai import OpenAI client = OpenAI( base_url="https://api.darkbloom.dev/v1", api_key="your-api-key" ) response = client.chat.completions.create( model="gemma-4-26b", messages=[{"role": "user", "content": "Hello!"}], stream=True ) for chunk in response: print(chunk.choices[0].delta.content, end="")
from openai import OpenAI client = OpenAI( base_url="https://api.darkbloom.dev/v1", api_key="your-api-key" ) response = client.chat.completions.create( model="gemma-4-26b", messages=[{"role": "user", "content": "Hello!"}], stream=True ) for chunk in response: print(chunk.choices[0].delta.content, end="")