Patterns

Edge & Mobile Inference

Edge & Mobile Deployment

Deploy AI models on mobile devices, IoT systems, and edge computing platforms. These solutions are optimized for resource-constrained environments while maintaining good performance.

MobileVLM

Fast, strong vision language assistant optimized for mobile devices

Key Features
1.4B-2.7B parameters
21.5 tokens/sec on mobile
Snapdragon optimized
CLIP-based vision
Platform: iOS/Android
OpenInfer

Hybrid, local-first AI runtime for edge devices and constrained environments

Key Features
Local-first
Progressive enhancement
Cross-platform
Enterprise-grade
Platform: Edge devices
TensorFlow Lite

Lightweight solution for mobile and embedded device inference

Key Features
Model quantization
Hardware acceleration
Cross-platform
Optimized kernels
Platform: Mobile/Embedded
ONNX Runtime

Cross-platform inference for ONNX models on various hardware

Key Features
Hardware acceleration
Quantization
Multiple backends
Production-ready
Platform: Cross-platform

Performance Considerations

Memory Usage

Quantized models can reduce memory usage by 50-75% with minimal accuracy loss

Battery Life

Edge inference reduces network usage, extending battery life significantly

Hardware Acceleration

Utilize NPUs, GPUs, and specialized chips for optimal performance

AI Inference Guide

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Core Concepts

4
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Deployment Options

3
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Tools & Services

2

Advanced Topics

2
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