GitHub - franzkruhm/Peermind: Decentralized Renewable-Powered P2P LLM Network Specification Proposal

Decentralized Renewable-Powered P2P LLM Network Specification

1. Overview

A globally distributed, peer-to-peer (P2P) network for large language models (LLMs) that ensures:

• Privacy: All queries remain on local nodes unless federated updates are performed.
• Censorship resistance: No central authority controls outputs.
• Renewable energy utilization: Inferers only operate when powered by solar, wind, or other green energy sources.
• Continuous operation: Geographic distribution ensures night/day operation.
• Community-driven model selection: Nodes vote on the model to use, ensuring it fits inferer constraints.

2. Node Roles

1. Inferers

   • Machines with sufficient VRAM to run full LLMs (e.g., Gemma 27B Q4 requires ~24 GB).

   • Responsibilities:

     • Run local inference for themselves and optionally for light users.
     • Report renewable energy headroom to referee nodes.
     • Participate in federated updates when surplus renewable energy is available.
     • Participate in model voting and selection.

   • Energy management:

     • Integrate renewable adapters to report real-time energy availability.
     • Compute rolling averages (30–60 sec) and apply hysteresis thresholds.
     • Idle GPU and unload model if energy headroom drops below thresholds.

2. Light Users

   • Machines without sufficient VRAM to run the full model.

   • Responsibilities:

     • Submit inference queries to available inferers.
     • Participate in federated updates (lightweight gradient contributions).

   • No heavy GPU usage; minimal energy footprint.

3. Referee Nodes

   • Coordinate query routing and load balancing.
   • Monitor global inferer energy availability.
   • Assign queries to inferers with sufficient renewable headroom and capacity.
   • Enforce inferer-to-user ratio to prevent overload.

3. Renewable Energy Integration

• Each inferer must run on renewable energy (solar/wind/battery).

• Power reporting:

  • Use adapters to normalize different inverter APIs into a standard format.
  • Report: available power, battery state, renewable headroom, timestamp.

• Thresholds and hysteresis:

  • GPU activates if renewable headroom > upper threshold.
  • GPU idles/unloads if headroom < lower threshold.
  • Rolling average smoothing prevents load/unload chatter.

• Optional battery integration:

  • Inferers may continue running at reduced capacity if stored renewable energy allows.

4. Inference Workflow

1. Light user submits query.

2. Referee selects inferer with sufficient renewable headroom and available compute.

3. Inferer processes query locally.

4. Output returned to user.

5. Federated updates (optional):

   • Performed only when inferers have surplus renewable energy.
   • Only weight updates/gradients are shared, never raw queries.

5. Model Selection & Voting

• Voting principles:

  • Only inferers participate in votes.
  • Models must meet VRAM, energy, and latency constraints.

• Voting workflow:

  1. Proposal stage: Community submits candidate models with metrics and licensing.
  2. Evaluation stage: Nodes test benchmarks (inference speed, throughput, energy usage, accuracy).
  3. Voting stage: Nodes cast votes; weighted voting optionally considers node capacity.
  4. Selection stage: Model with majority or supermajority is adopted.

• Model updates:

  • Voting can be repeated for upgrades or model changes.
  • Federated updates evolve the model without central authority interference.

6. Global Distribution

• Geographic diversity ensures at least some inferers have sufficient renewable power at all times.

• Referee dynamically routes queries based on:

  • Location and time zone
  • Renewable headroom
  • Current load

• Network achieves near 24/7 operation without fossil-fuel energy.

7. Network Management

• Inferer-to-user ratio enforced (e.g., 1 inferer per 5–10 light users).

• Load balancing prevents inferers from being overwhelmed.

• Optional caching reduces repeated queries to distant inferers.

• Security:

  • Energy reporting and task assignment must be tamper-resistant.
  • Federated updates should use cryptographic verification to prevent malicious contributions.

8. Software Components

1. Renewable Adapter Layer

   • Abstracts inverter APIs to a standard reporting interface.

2. Local Inference Engine

   • Runs LLM on GPU (or CPU if feasible).

3. Federated Update Engine

   • Handles secure weight/gradient sharing between nodes.

4. Referee Scheduler

   • Assigns queries based on energy availability, load, and inferer-to-user ratio.

5. Voting Module

   • Manages model proposals, evaluation, voting, and adoption.

6. Monitoring & Logging

   • Tracks node status, energy headroom, query throughput, model votes, and updates.

9. Implementation Roadmap

1. Phase 1 (Today)

   • Small-scale hobbyist network (2–3 nodes).
   • Full local inference + periodic federated updates.
   • Manual renewable adapters.
   • Initial model voting among participating inferers.

2. Phase 2 (1–3 years)

   • Semi-automated P2P updates.
   • Community-maintained adapters for common inverters.
   • Cryptographic verification of updates.
   • Small-to-medium sized networks.
   • Formalized voting process for model selection.

3. Phase 3 (5+ years)

   • Plug-and-play P2P AI networks.
   • Lightweight models for broader adoption.
   • Global geographic distribution.
   • Optional collaborative sharding for high-bandwidth nodes.
   • Continuous model voting for upgrades and evolution.

10. Summary

This specification outlines a privacy-focused, renewable-powered, globally distributed P2P LLM network with:

• Full local inference on inferers.
• Light user support.
• Federated model updates.
• Energy-aware scheduling with rolling averaging and hysteresis.
• Global distribution for continuous operation.
• Community-driven model selection via inferer voting.

The design emphasizes sustainability, privacy, accessibility, and democratic governance, while remaining feasible for hobbyist and professional implementation.