NeuroShard AI
Decentralized AI Training Network
Quick Start
# Install
pip install nextaitech
# Run a node (you'll need a token from neuroshard.com)
neuroshard --token YOUR_TOKEN
What is NeuroShard?
NeuroShard is a decentralized network for training large language models. Contributors share their GPU/CPU power and earn NEURO tokens based on their contribution (Proof of Neural Work).
Key Features
- Swarm Architecture - Fault-tolerant, multipath routing for resilient training
- Async Training - DiLoCo-style gradient accumulation for 90%+ bandwidth reduction
- Earn NEURO - Get rewarded for contributing compute power
- Cryptographic Proofs - ECDSA-signed Proof of Neural Work
- Web Dashboard - Monitor your node at
http://localhost:8000
Requirements
- Python 3.9+
- 4GB+ RAM (8GB+ recommended)
- GPU optional but recommended for training
GPU Support
For NVIDIA GPU support, install PyTorch with CUDA:
pip install torch --index-url https://download.pytorch.org/whl/cu118
Usage
Basic Node
neuroshard --token YOUR_TOKEN
With Custom Port
neuroshard --token YOUR_TOKEN --port 9000
Connect to Specific Tracker
neuroshard --token YOUR_TOKEN --tracker tracker.neuroshard.com:8080
Web Dashboard
Once running, open your browser to http://localhost:8000 to view:
- Node status and role
- Network statistics
- Training progress
- NEURO balance
- Resource usage
Architecture
NeuroShard uses a swarm-based architecture for maximum resilience:
- Dynamic Routing - If one node fails, work automatically routes to others
- Activation Buffering - GPUs never starve waiting for network
- DiLoCo Training - Local gradient accumulation reduces sync frequency by 90%+
- Speculative Checkpoints - Fast recovery from node failures
Links
- Website: https://neuroshard.com
- Documentation: https://docs.neuroshard.com
- Get a Token: https://neuroshard.com/register
License
MIT License - see LICENSE for details.
Metadata
Release files for nextaitech 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nextaitech-0.0.4.tar.gz | 285.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nextaitech-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 572.9 kB
Release files / nextaitech-0.0.4.tar.gz
| Download URL | nextaitech-0.0.4.tar.gz |
|---|---|
| Size | 285.9 kB |
| Tags | Source |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.10.12
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Release files / nextaitech-0.0.4-py3-none-any.whl
| Download URL | nextaitech-0.0.4-py3-none-any.whl |
|---|---|
| Size | 287.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.12
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