Cactus Framework: The Ultimate Solution for On-Device AI Development on Mobile

Why Do We Need Mobile-Optimized AI Frameworks?

With smartphone capabilities reaching new heights, running AI models locally has become an industry imperative. The Cactus framework addresses three critical technical challenges through innovative solutions:

1. Memory Optimization – 1.2GB memory footprint for 1.5B parameter models
2. Cross-Platform Consistency – Unified APIs for Flutter/React-Native
3. Power Efficiency – 15% battery drain for 3hr continuous inference

[Architecture Diagram]
Application Layer → Binding Layer → C++ Core → GGML/GGUF Backend
Supports React/Flutter/Native implementations
Optimized via Llama.cpp computation

Core Feature Matrix

Implemented Features

• ✅ Text Generation & Chat Completion
• ✅ Vision-Language Models (VLM)
• ✅ Streaming Token Generation
• ✅ Early-Stage TTS Support
• ✅ JSON Schema Validation
• ✅ Jinja2 Template Engine

Roadmap Features

• 🔜 Cross-App Agent Workflows
• 🔜 Local File System Access
• 🔜 Planning & Evaluation Modules
• 🔜 High-Level Sentiment Analysis APIs

3-Minute Setup Guide

Flutter Integration

# Add to pubspec.yaml
dependencies:
  cactus: ^0.0.3

flutter pub get

React Native Implementation

# NPM Installation
npm install cactus-react-native

# iOS Dependencies
cd ios && npx pod-install

C++ Native Development

# Language Model Example
cd example/cpp-llm && ./build.sh

Performance Benchmarks

Note: t/s = tokens per second

Real-World Implementation Examples

Smart Note-Taking App

// Flutter Implementation
final meetingSummary = await Cactus.generate(
  prompt: "Summarize key points: ${voiceInput}",
  maxTokens: 500
);

Cross-Modal Search

// React Native Implementation
const searchResults = await cactus.vlmSearch(
  image: cameraCapture,
  query: "Identify all electronic devices"
);

Advanced Developer Guide

Model Conversion

# Using GGUF Toolchain
python convert.py --input model.bin --output model.gguf

Memory Optimization

// C++ Configuration
ctx = llama_new_context_with_model(model, {
  .n_gpu_layers = 30,
  .main_gpu = 0,
  .low_vram = true
});

Technical FAQ

How to Choose Models?

• Entry-Level: SmolLM2 360M (<500MB RAM)
• Performance: Qwen-2.5 1.5B (1.8GB RAM)
• Vision Tasks: SmolVLM (Image Understanding)

Device Requirements?

• iOS: A12 Chip or newer
• Android: Snapdragon 865/Dimensity 1000+
• Minimum RAM: 2GB Free Space

Ecosystem Resources

Official Documentation

• Core API Docs
• Flutter Guide
• Performance Tuning

Sample Projects

1. Cross-Platform Chat
2. Smart Diary System
3. AR Vision Assistant

Contribution Guidelines

Development Process

1. Create detailed feature proposals
2. Follow existing test patterns
3. Run validation pre-PR submission

./scripts/test-cactus.sh

Support Channels

• Developer Email: founders@cactuscompute.com
• Discord Community: Join Discussion

Live Application Demos

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All performance data sourced from official Cactus benchmarks. Actual results may vary based on device configuration and environmental conditions. Developers are encouraged to test with sample projects.