Run AI Locally: The Developer's Guide to Private, On-Device Language Models (AI & LLM Engineering Book 4) by Jacob Ren
English | August 11, 2026 | ASIN: B0HDSFMFV8 | 162 pages | EPUB | 0.18 Mb
Stop paying per token for AI you could be running on the laptop you already own.
Running Local LLMs for Developers is a hands-on guide to private, offline AI for working programmers. You'll install Ollama, pick models that actually fit consumer hardware, and understand exactly what quantization costs you in quality - with real numbers, not vibes. Then you'll wire a local model into real code through Ollama's REST API and its OpenAI-compatible endpoint, so your existing apps switch to localhost with a two-line change.
This book is honest about the tradeoffs. You'll learn the four legitimate reasons to go local, the four bad ones, and the hybrid routing pattern that gives you local's cost and privacy wins without inheriting its quality ceiling. The capstone is docchat: a fully offline document Q&A tool with citations that you can verify with the network cable unplugged.
What you'll learn:
Install Ollama and run your first model in under ten minutes
Size any model to your VRAM before downloading it
Decode quantization tags like Q4_K_M and know what they cost
Build a personal eval harness so you stop trusting leaderboards
Bake system prompts and parameters into reusable Modelfiles
Manage context windows and the KV cache without OOM surprises
Point the OpenAI SDK at localhost with a two-line change
Force schema-valid JSON out of small models reliably
Build local semantic search with embeddings and SQLite
Route hard cases to hosted models without leaking sensitive data
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