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⚡ How to Run AI Locally on Almost Anything: From $5 Chips to to 8×H100 Setups

Run powerful AI models on the hardware you already own, understand what actually limits performance, and choose the right setup for LLMs, image, video, and local AI agents.

Author

Robin Do
September 30, 2026

TL;DR: You can run AI locally on everything from tiny microcontrollers to high-end GPUs. The main limit is your hardware. Memory decides what model fits, while bandwidth and GPU power decide how fast it runs.

Here's the quick map:

  • Tiny chips (ESP32-S3): ultra-small models, fun physical projects

  • Raspberry Pi and phones: small LLMs, speech, vision, some image tasks

  • Laptops and home servers: larger models, coding tools, image generators, local AI agents

  • Discrete GPUs: image, video, and music generation at real speed

  • 8× H100: basically anything, fast, though you'll be renting this, not owning it

The two numbers that matter most are how much memory you have, and how fast your hardware can move that memory while a model is running.

Introduction

You don't need a monster GPU to run AI locally. Just a $5 chip, an 8GB Raspberry Pi, or even your old laptop.

If you have more powerful hardware, you can run LLMs, generate images and videos, write code, and build AI agents without sending everything to the cloud.

Oh, and you might already have enough hardware to run AI. The real question is what you can actually run on it.

In this guide, I'll show you how to run AI locally on almost anything, from $5 chips to massive 8×H100 setups.

I'll break down which models you can run, what actually works, where things get painfully slow, and what hardware you need for your next AI project.

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