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Getting Started with Local AI
1 hour 17 minutes Posted Apr 21, 2026 at 12:35 pm.
Cold Open: Why Local AI?
Welcome & Introduction
Following Up on the Frontier Models Episode
Du'An's Background & AI Inference at Akamai
What If You Wanted to Own Your Data?
Local AI vs Cloud AI: A Different Layer of the Stack
Why GPUs Matter: The Nvidia Story
CPU vs GPU: Serial vs Parallel Processing
Model Weights & Quantization Explained
Choosing the Right Model for Your Hardware
Getting Started with Ollama
Live Demo: Running Your First Local Model
Hardware Recommendations & Requirements
Hugging Face & Finding Models
Performance Tips & Benchmarking
Use Cases: When to Go Local vs Cloud
Live Demo: Claude Put-in-Work Repo
Bonus: Building a Deck with Co-work Live
Preview: Episodes 2 & 3
Wrap-up
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1:17:47
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Show notes
Join us for Part 1 of a 3-part series as Du'An Lightfoot (Senior AI Engineer at Akamai) breaks down everything you need to know to get started running AI models locally on your own hardware.
Du'An walks through the fundamentals of local AI - from understanding why you'd want to run models privately (data ownership, air-gapped environments, IP protection) to the hardware concepts that make it possible. You'll learn how inference actually works under the hood, why GPUs matter for AI workloads, how to choose and quantize models for your hardware, and how to get up and running with tools like Ollama. This is Part 1 of a 3-part series - future episodes cover serving models via API and distributing inference at the edge with Kubernetes.
Timestamps
How to find Du'An:
https://www.duanlightfoot.com/
https://github.com/labeveryday/
Links from the show:
https://ollama.com/
https://apxml.com/
https://localllm.in/
https://huggingface.co/
https://github.com/labeveryday/claude-put-in-work
https://claude.ai/