Umazen: Decentralized AI Infrastructure on Solana

Production-Grade Framework for On-Chain AI Model Training & Inference Markets

Build a decentralized AI infrastructure on Solana, enabling secure on-chain model training and creating a global marketplace for AI inference services

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Table of Contents

• Architecture Overview
• Key Features
• Getting Started
  • Prerequisites
  • Installation
  • Configuration
• Development Workflow
  • Local Development
  • Testing
  • Production Deployment
• Security
• Performance Optimization
• Contributing
• License

Architecture Overview

%% Architecture Diagram for Umazen
graph TD
    subgraph Blockchain Layer
        SP[Solana Programs] -->|Program Instructions| MR(Model Registry)
        SP -->|State Management| VM[Verification Market]
        SP -->|Settlement| PM[Payment Module]
        MR -->|Hash Storage| IPFS[(IPFS Cluster)]
        VM -->|Proof Verification| ZK[ZK Proof Verifier]
    end

    subgraph AI Infrastructure Layer
        FL[Federated Learning Engine] -->|Aggregated Updates| MR
        FL -->|Differential Privacy| DP[Privacy Engine]
        ZK[ZK Provers] -->|Proof Generation| VM
        IM[Inference Market] -->|Resource Allocation| KC[Kubernetes Cluster]
        KC -->|GPU Orchestration| GPU([NVIDIA GPUs])
        DP -->|Secure Aggregation| SA[Secure Aggregator]
    end

    subgraph Client Layer
        UI[Web Interface] -->|User Operations| SP
        UI -->|Model Visualization| TS[TypeScript SDK]
        TS -->|API Calls| BE[Backend Services]
        BE -->|Training Jobs| FL
        BE -->|Inference Requests| IM
        PY[Python SDK] -->|Local Training| FL
        PY -->|Model Submission| MR
    end

    classDef solana fill:#14f195,color:#000;
    classDef ai fill:#7e57c2,color:#fff;
    classDef client fill:#2196f3,color:#fff;
    
    class SP,MR,VM,PM solana;
    class FL,ZK,IM,DP,SA,KC ai;
    class UI,TS,PY,BE client;

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The system comprises three core layers:

1. Blockchain Layer

   • Solana Program Modules
   • On-Chain Model Registry
   • Proof Verification System

2. AI Infrastructure Layer

   • Federated Learning Engine
   • ZKML Proof Generation
   • Distributed Inference Nodes

3. Client Layer

   • TypeScript SDK
   • Python Training Toolkit
   • React Web Interface

Key Features

Core Capabilities

• 🧠 On-Chain Model Training

  • Federated Learning with Cryptographically Verified Updates
  • Privacy-Preserving Training via Secure Aggregation
  • Model Version Control with Merkleized Snapshots

• ⚡ Inference Marketplace

  • Real-Time Bidding System for Compute Resources
  • ZK-Proof Verified Inference Results
  • Multi-Currency Payment Support

• 🔒 Security Framework

  • Role-Based Access Control (RBAC)
  • Model Integrity Proofs using Poseidon Hashes
  • Encrypted Model Weights Storage

Advanced Functionality

• Parallelized Proof Generation using CUDA
• Automated Model Quantization (FP32 → INT8)
• Cross-Chain Verification Bridges
• Dynamic Pricing Oracle for Compute Resources
• GPU-Optimized Training Pipelines

Getting Started

Prerequisites

System Requirements

• x86_64 Architecture (AVX2 Support Required)
• NVIDIA GPU (Compute Capability ≥ 7.0)
• 64GB RAM (Minimum)
• 1TB NVMe SSD

Software Dependencies

• Rust 1.71+ (with Solana Tool Suite)
• Python 3.10+ (CUDA 12.1 Toolkit)
• Node.js 20.0+ (PNPM 8.15+)
• PostgreSQL 15+ with TimescaleDB
• IPFS Kubo 0.22.0+

Installation

1. Clone Repository

git clone --recurse-submodules https://github.com/umazen/umazen.git
cd umazen

2. Install Core Dependencies

# Install Solana Toolchain
./scripts/install_solana.sh

# Setup Python Environment
make venv && source .venv/bin/activate
pip install -r requirements.txt

# Install Node Modules
pnpm install

3. Initialize Infrastructure

# Start Local Validator
solana-test-validator --reset --quiet &

# Deploy Programs
anchor build && anchor deploy

# Start IPFS Cluster
ipfs init --profile=test
ipfs daemon --enable-gc --routing=dhtclient &

Configuration

Environment Variables

# .env.example
SOLANA_RPC_URL="https://api.mainnet-beta.solana.com"
MODEL_REGISTRY_PROGRAM_ID="Umaz...1111"
ZK_PROVER_KEY_PATH="./keys/prover.key"
INFERENCE_NODE_GPU=0,1  # Comma-separated GPU indices

Development Workflow

Local Development

# Start Development Servers
make dev

# Program Hot-Reloading
anchor watch

Testing

# Run All Test Suites
make test

# Specific Test Targets
cargo test -p umazen_program --test federated_learning
pnpm test:e2e -- --network localnet

Production Deployment

Kubernetes Setup

# Apply Cluster Configuration
kubectl apply -f deploy/k8s/production

# Monitor Deployment
kubectl get pods -n umazen -w

Mainnet Deployment Checklist

• Verify Program IDs in Anchor.toml
• Generate Final Keys: make generate-keys
• Deploy Programs: anchor deploy --provider.cluster mainnet
• Initialize Market: pnpm cli market:init

Security

Audit Process

• Critical Fixes Response Time: <4 hours
• Quarterly Third-Party Audits

Key Management

• Hardware Security Module (HSM) Integration
• Threshold Signatures for Multi-Sig Wallets
• Ephemeral Proving Keys for ZK Operations

Performance Optimization

Benchmark Targets

Operation	Target Latency	Throughput
Training Round	2.5s	150 models/min
Inference	85ms	10k req/s
Proof Generation	1.2s	45 proofs/min

Optimization Techniques

• Batch Proof Aggregation
• CUDA-Accelerated Linear Algebra
• Memory-Mapped Model Weights
• Async Runtime for Concurrent Verifications