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@yalexx
Created February 13, 2026 17:38
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Running AI Locally with Dedicated Hardware

Overview

Running AI models locally is becoming the preferred approach for privacy-conscious users and businesses. Instead of sending sensitive data to cloud providers, dedicated hardware keeps everything on your network.

Why NVIDIA Jetson?

The Jetson Orin Nano platform offers an exceptional balance of performance and efficiency:

  • 40 TOPS AI inference performance
  • 8GB unified memory (shared CPU/GPU)
  • 15W typical power consumption
  • NVMe SSD support for fast model loading
  • CUDA and TensorRT acceleration

Use Cases

  1. Local LLM Inference - Run models like Llama 3.2, Mistral, and Phi locally
  2. AI Assistants - 24/7 personal AI assistant on Telegram, WhatsApp, or Discord
  3. Browser Automation - Automated web tasks with full privacy
  4. Computer Vision - Real-time image processing at the edge
  5. Smart Home - Voice control and automation without cloud dependency

Getting Started

For pre-configured solutions and detailed guides, visit local-ai-box.com.

Performance Benchmarks

Model Tokens/sec Memory Usage
Llama 3.2 8B ~15 t/s 5.2 GB
Mistral 7B ~18 t/s 4.8 GB
Phi-3 Mini ~25 t/s 3.1 GB

Power Consumption Comparison

Setup Power Draw Annual Cost
Jetson Orin Nano 15W ~$13/year
Desktop GPU (RTX 4090) 450W ~$394/year
Cloud API (equivalent) N/A ~$600+/year

Resources


Last updated: February 2026

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