Introduction
NeuroShard is a decentralized architecture for collective intelligence — a protocol that turns a global network of heterogeneous devices into one continuously evolving optimizer that collectively trains a shared model, NeuroLLM, from random initialization.
The Problem with Centralized AI
The current landscape of artificial intelligence is defined by a small number of vertically integrated AI platforms that own the models, the data, and the distribution channels:
- Centralized Control: Alignment, safety, and access policies are set unilaterally with limited transparency
- Infrastructure Barriers: Training frontier models requires billion-dollar clusters, shutting out independent researchers
- Compute Waste: While hyperscale data centers consume enormous energy, the world's edge compute remains unused
- Data Privacy: Centralized training creates systemic privacy risks
The NeuroShard Solution
NeuroShard proposes a decentralized alternative: a protocol for collaborative model creation where the network, not any single operator, is the place where the model lives.
Core Principles
1. Organic Scalability
The model architecture is not fixed but elastic. Using a Dynamic Layer Pool, the model expands as new nodes join and contribute memory — no fixed "7B/70B" jumps.
2. Verifiable Computation
Through Proof of Neural Work (PoNW), the network cryptographically validates that participants are performing useful training operations. Gradients are proofs of work.
3. Byzantine Tolerance
The training process is secured against malicious actors through robust statistics (Krum, Trimmed Mean, Coordinate-wise Median) and a fraud-proof slashing mechanism.
4. Economic Alignment
The NEURO token aligns incentives, rewarding participants for verifiable contributions of compute, data, and uptime. The token is backed by the network's collective computational intelligence.
NeuroLLM: The Model
NeuroLLM is not a fork or fine-tune of an existing model. It is a completely new transformer architecture designed with specific goals:
| Feature | Description |
|---|---|
| Efficient Gradients | Optimized for gradient compression and gossip transmission |
| Stable Training | Robust to asynchronous, noisy gradient updates |
| Scalable Architecture | Grows from 125M to 70B+ parameters as the network matures |
| Privacy-Compatible | Supports differential privacy in training data |
Architecture Components
- RMSNorm: More stable for distributed training than LayerNorm
- Rotary Position Embeddings (RoPE): No fixed maximum sequence length
- Grouped Query Attention (GQA): 3x reduction in KV cache size
- SwiGLU Activation: Better gradient flow than ReLU or GELU
How It Works
Key Differentiators
| Feature | NeuroShard | Other Systems |
|---|---|---|
| Model Ownership | Community-owned, trained from scratch | Pre-trained by corporations |
| Architecture | Dynamic, grows with network | Fixed model sizes |
| Training | Decentralized DiLoCo protocol | Centralized data centers |
| Rewards | Proof of Neural Work | Proof of Stake/Output validation |
| Data | Genesis dataset (verifiable) | Unknown training data |
Next Steps
- Quick Start Guide — Get your node running in 5 minutes
- How It Works — Deep dive into the technology
- NEURO Economics — Understand rewards and tokenomics
- Architecture Overview — Technical deep dive