Local AI Clearly Explained

I run this episode solo. I explain local AI in plain terms: the model runs on hardware I control, and a cloud model runs somewhere else. I map the four pieces of the local AI landscape — the model, the warehouse, the software, and the workflow — and I define the words that beginners meet first: parameters, tokens, context window, quantization, and GGUF. I walk through the Google open model stack (Gemma 4, Google AI Edge, LiteRT-LM, AI Edge Gallery), compare the other open model families, and show three ways to run a model today. I close with a first workflow you can copy and three startup ideas that use local AI as the wedge.

And a special thank you to Google for supporting the podcast.

Timestamps

00:00 – Intro

01:35 – The Open Model the Landscape

03:09 – Vocab Decoder

06:48 – Google Gemma Clearly Explained

10:29 – Other Open Model Families

14:20 – Path 1: Run Gemma in LM Studio

18:17 – Path 2: Ollama

20:15 – Path 3: Google AI Edge

21:07 – Hardware Cheat Sheet