You don’t need a monster GPU to run AI locally. A tiny microcontroller can already handle basic models, while a Raspberry Pi, laptop, home server, or GPU can take you all the way to speech, vision, coding, image generation, and video.

The key is understanding just two things: how much memory you have, and how fast your hardware can move that memory.

We’ll talk about:

From ESP32 to Raspberry Pi: What tiny devices can actually run, and where microcontrollers hit their limits.
Why 8GB Isn’t Always 8GB: How the Raspberry Pi 5 and iPhone 16 can have the same RAM but very different AI performance.
The RAM Formula: A simple way to estimate how large an LLM your laptop or desktop can fit.
32GB, 64GB, 128GB: What MacBooks, Mac Studios, and AMD Ryzen AI Halo systems unlock for local models.
RTX 4090 vs. Huge-Memory Systems: Why VRAM and bandwidth matter more for image and video generation.
The Extreme End: What 8× H100 systems can run, and when renting cloud GPUs makes more sense than buying hardware.

Keywords: Local AI, Run LLM Locally, Raspberry Pi AI, ESP32 AI, RTX 4090, H100, AMD Ryzen AI Halo, Mac Studio, Qwen3, Whisper, Ollama, Local LLM, GPU VRAM, AI Hardware, Image Generation, Video Generation.

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#116 Robin: How Much AI Can Your Hardware Actually Run? From $5 Chips to 8× H100s

AI Fire Daily

#116 Robin: How Much AI Can Your Hardware Actually Run? From $5 Chips to 8× H100s