neuroshard-ai 0.2.86

Decentralized AI Network

NeuroShard

Decentralized LLM Training Network

Website • Documentation • Whitepaper • Discord • Twitter

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What is NeuroShard?

NeuroShard is a decentralized network for training large language models. Anyone can contribute GPU/CPU power and earn NEURO tokens through Proof of Neural Work.

Unlike centralized AI companies, NeuroShard distributes both the compute AND the rewards across all participants.

Key Features

Feature	Description
DiLoCo Training	Distributed Low-Communication training - sync every 500 steps, not every step
Byzantine Tolerance	Robust gradient aggregation (Krum, Trimmed Mean) handles malicious nodes
NEURO Rewards	Earn tokens for contributing compute via Proof of Neural Work
Cryptographic Proofs	ECDSA-signed proofs ensure trustless verification
Web Dashboard	Real-time monitoring at http://localhost:8000
P2P Network	Decentralized peer discovery and gossip protocol

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Quick Start

Installation

pip install neuroshard

Run a Node

# Get your token from neuroshard.com
neuroshard --token YOUR_TOKEN

That's it! Your node will:

1. Connect to the network
2. Start training model layers
3. Earn NEURO for your contribution

Web Dashboard

Open http://localhost:8000 to see:

• Node status and role
• Training progress (DiLoCo inner/outer steps)
• NEURO balance
• Network statistics

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System Requirements

Component	Minimum	Recommended
RAM	4 GB	8+ GB
Python	3.9+	3.10+
GPU	Optional	NVIDIA 8GB+ VRAM

GPU Support (Optional)

For NVIDIA GPUs with CUDA:

pip install torch --index-url https://download.pytorch.org/whl/cu118

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How It Works

DiLoCo Distributed Training

NeuroShard uses DiLoCo (Distributed Low-Communication) for efficient distributed training:

┌─────────────────────────────────────────────────┐
│  INNER LOOP (500 steps - no communication)      │
│  • Each node trains independently               │
│  • Local AdamW optimization                     │
└─────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────┐
│  OUTER LOOP (sync with peers)                   │
│  • Compute pseudo-gradient: Δθ = θ₀ - θ₅₀₀     │
│  • Gossip to peers                              │
│  • Byzantine-tolerant aggregation               │
│  • Nesterov momentum update                     │
└─────────────────────────────────────────────────┘
                      ↓
              (Repeat)

This reduces network communication by 500x compared to synchronous training!

Proof of Neural Work

Nodes earn NEURO by submitting cryptographically signed proofs of their work:

• Training batches processed
• Inference requests served
• Uptime contribution
• Data samples provided

All proofs are verified using ECDSA signatures (secp256k1).

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Configuration

CLI Options

neuroshard --token YOUR_TOKEN \
           --port 8000 \
           --tracker https://tracker.neuroshard.com \
           --training \
           --diloco-steps 500

Option	Default	Description
--token	Required	Your node authentication token
--port	8000	HTTP server port
--tracker	Auto	Tracker server URL
--training	False	Enable training mode
--diloco-steps	500	Inner steps before sync

See full CLI reference for all options.

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                      NeuroShard Node                        │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │  NeuroLLM   │  │   DiLoCo    │  │  Proof of Neural    │  │
│  │  (Model)    │  │  Trainer    │  │  Work Ledger        │  │
│  └─────────────┘  └─────────────┘  └─────────────────────┘  │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │  P2P/DHT    │  │  Gradient   │  │  ECDSA Crypto       │  │
│  │  Network    │  │  Aggregator │  │  (secp256k1)        │  │
│  └─────────────┘  └─────────────┘  └─────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

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Documentation

• Whitepaper - Technical whitepaper (PDF)
• Getting Started - First steps
• Running a Node - Detailed setup
• Architecture - System design
• Economics - NEURO tokenomics
• API Reference - SDK & endpoints

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Links

Resource	Link
Website	neuroshard.com
Documentation	docs.neuroshard.com
Whitepaper	PDF
Discord	discord.gg/4R49xpj7vn
Twitter	@shardneuro
PyPI	pypi.org/project/neuroshard

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Contributing

We welcome contributions! Please see our Contributing Guide for details.

# Clone the repo
git clone https://github.com/neuroshard-ai/neuroshard.git
cd neuroshard

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

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License

Apache License 2.0 - see LICENSE for details.

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Train AI. Earn NEURO. Own the Network.