Documentation Index

Fetch the complete documentation index at: https://docs.blindference.xyz/llms.txt

Use this file to discover all available pages before exploring further.

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Compute — Introduction

Run a Blindference node to earn fees executing encrypted inference jobs. Help build a private, verifiable, and economically accountable AI execution layer.

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What a Node Does

A Blindference node is a compute provider that:

1. Attests its identity to the ICL (Inference Coordination Layer)
2. Heartbeats every 60 seconds to prove liveness
3. Polls for pending inference jobs every 5 seconds
4. Decrypts encrypted prompts via CoFHE threshold FHE under strict ACL
5. Executes inference via Groq, Google Gemini, or local vLLM (pluggable backends)
6. Commits results back to the ICL for quorum consensus

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Earning Model

Nodes earn fees for successful task completion. Fees are determined by:

• Task complexity: More complex prompts earn more
• Model tier: Higher-tier models (premium APIs) earn more
• Attestation tier: Higher attestation tiers (TPM, TEE) earn premium rates
• Reputation: Higher reputation scores get priority assignment

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Attestation Tiers

Current status: Tier 0 (mock) is fully functional for development. TPM and TEE tiers are planned for future releases.

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Requirements

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Minimum (Mock Inference)

• Python 3.10+
• 1 CPU core
• 512MB RAM
• Outbound HTTPS access
• No GPU required

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Recommended (Production)

• Python 3.11+
• 2+ CPU cores
• 2GB RAM
• Stable internet connection
• TPM 2.0 chip (for tier 1)

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Optional (Local Model Inference)

• NVIDIA GPU with CUDA support
• 4GB+ VRAM
• vLLM installed

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Quick Facts

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Next Steps