Introduction to Accelerate AI

Welcome to Accelerate AI - the first platform to bring true AI processing to the Solana blockchain. Execute machine learning models, deploy intelligent smart contracts, and build autonomous agents with sub-second finality.

What You Can Build

Quick Start

Get up and running with Accelerate AI in under 5 minutes.

npm install @accelerate-ai/sdk @solana/web3.js

import { AccelerateAI } from '@accelerate-ai/sdk';
import { Connection, Keypair } from '@solana/web3.js';

// Connect to Solana
const connection = new Connection('https://api.mainnet-beta.solana.com');
const wallet = Keypair.generate();

// Initialize Accelerate AI
const ai = new AccelerateAI({
  connection,
  wallet,
  network: 'mainnet-beta'
});

// Use a pre-trained model
const result = await ai.inference({
  model: 'sentiment-analysis-v1',
  input: 'Solana is the fastest blockchain!',
});

console.log(result.prediction); // "positive"
console.log(result.confidence); // 0.96
console.log(result.txSignature); // On-chain proof

Installation

Prerequisites

• Node.js 16+ or Python 3.8+
• Solana CLI tools (optional, for deployment)
• A Solana wallet with SOL for transactions

JavaScript/TypeScript

npm install @accelerate-ai/sdk

Python

pip install accelerate-ai-python

Rust (for on-chain programs)

cargo add accelerate-ai

Deploy Your First Model

Learn how to deploy a custom machine learning model on Solana.

Model Requirements

• Model must be serialized in ONNX, TensorFlow Lite, or PyTorch format
• Maximum model size: 10MB (optimized for on-chain storage)
• Supported architectures: Neural Networks, Decision Trees, Linear Models

Deployment Steps

import { AccelerateAI } from '@accelerate-ai/sdk';

// Load your model
const model = await ai.loadModel('./my-model.onnx');

// Deploy to Solana
const deployment = await ai.deploy({
  model: model,
  name: 'price-predictor-v1',
  description: 'Predicts SOL price movements',
  inputSchema: {
    type: 'array',
    shape: [5], // 5 input features
  },
  pricing: {
    inferenceFeeLamports: 1000, // 0.000001 SOL per inference
  }
});

console.log('Model deployed at:', deployment.programId);
console.log('Model URL:', deployment.explorerUrl);

Architecture Overview

Understanding how Accelerate AI integrates with Solana.

Key Components

• Model Registry: On-chain registry of deployed AI models
• Inference Engine: Executes models within Solana's compute budget
• Verification Layer: Cryptographic proof of inference results
• Training Coordinator: Manages decentralized training jobs

AI Smart Contracts

Build intelligent smart contracts that use AI for decision-making.

Example: AI-Powered Trading Bot

use accelerate_ai::prelude::*;
use anchor_lang::prelude::*;

#[program]
mod ai_trader {
    pub fn execute_trade(ctx: Context<ExecuteTrade>) -> Result<()> {
        // Get market data
        let market_data = fetch_market_data(&ctx.accounts.market)?;
        
        // Run AI prediction
        let prediction = accelerate_ai::inference(
            "price-predictor-v1",
            market_data
        )?;
        
        // Execute trade based on AI decision
        if prediction.confidence > 0.8 {
            if prediction.label == "bullish" {
                execute_buy_order(&ctx, prediction.amount)?;
            } else {
                execute_sell_order(&ctx, prediction.amount)?;
            }
        }
        
        Ok(())
    }
}

SDK Reference

Core Methods

{
  prediction: any,        // Model output
  confidence: number,     // Confidence score (0-1)
  txSignature: string,    // Transaction signature
  computeUnits: number    // Compute units used
}

Optimization Guide

Optimize your models for on-chain execution.

Model Size Optimization

• Quantization: Reduce model precision from FP32 to INT8
• Pruning: Remove unnecessary weights and connections
• Knowledge Distillation: Train smaller models that mimic larger ones

Compute Budget Tips

// Check compute usage before deployment
const analysis = await ai.analyzeModel('./model.onnx');
console.log('Estimated compute units:', analysis.computeUnits);

if (analysis.computeUnits > 1_400_000) {
  console.log('⚠️ Model too large, consider optimization');
}

Security Best Practices

Input Validation

// Always validate inputs
function validateInput(input: number[]): boolean {
  // Check array length
  if (input.length !== 5) return false;
  
  // Check for valid ranges
  return input.every(x => x >= 0 && x <= 1 && !isNaN(x));
}

const userInput = [0.1, 0.5, 0.3, 0.8, 0.2];
if (!validateInput(userInput)) {
  throw new Error('Invalid input data');
}

const result = await ai.inference({
  model: 'my-model',
  input: userInput
});