Welcome to Local LLM Guide ๐
Hi, I'm Ling. I'm a medical student who fell into AI by accident. No CS degree, no big tech job โ just a laptop, a lot of curiosity, and a belief that AI should be for everyone.
Not sure where to start? Pick your path:
๐จโ๐ป For Developers
You want to run LLMs locally โ on your own hardware, with your own data, without paying API fees.
| # | Article | Level | Read Time |
|---|---|---|---|
| 01 | Getting Started: Run Your First Local LLM in 5 Minutes | ๐ข Beginner | 5 min |
| 02 | Hardware Guide: What You Actually Need | ๐ข Beginner | 8 min |
| 03 | DeepSeek-R1: The $0 o1 Alternative | ๐ก Intermediate | 10 min |
| 04 | Qwen 3.6 & 2.5: The Most Versatile Local Models | ๐ก Intermediate | 10 min |
| 05 | Open WebUI: Your Local ChatGPT | ๐ก Intermediate | 8 min |
| 06 | GGUF & Modelfile: The Power User's Guide | ๐ก Intermediate | 12 min |
| 07 | Local RAG: Chat With Your Documents | ๐ก Intermediate | 10 min |
| 08 | Production-Ready Local LLMs: From Terminal to Team Deployment | ๐ด Advanced | 15 min |
| 09 | Function Calling for Local LLMs: DeepSeek, Qwen, GLM-4 & LangChain | ๐ด Advanced | 15 min |
๐ Full source code & scripts: GitHub: Lingdas1/local-llm-guide
๐งโ๐ซ New to AI? Start Here
You've heard about AI but feel overwhelmed. You have a regular laptop and want to understand what's possible โ in plain English, no jargon.
| # | Guide | Read Time |
|---|---|---|
| 01 | AI Is Too Expensive? I Run It for Free on My Laptop | 5 min |
| 02 | What Is an LLM? (No, It's Not Magic) | 6 min |
| 03 | Step-by-Step: Run Your First AI Model in 10 Minutes | Coming soon! |
| 04 | 5 Free Things You Can Do with Local AI | Coming soon! |
๐ก Don't know where to start? Begin with Article 01 โ it explains why you don't need money or technical skills to use AI.
All guides are written by a medical student who learned this stuff from zero. No assumed knowledge, no skipped steps.
๐ The Complete Guide (All-in-One)
If you prefer one long read, start here:
๐ The Complete Guide to Running LLMs Locally in 2026: From Ollama to Production
Covers everything from installing Ollama to production deployment โ all in one article.
๐ What This Series Covers
๐ข Beginner โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโ 01. Getting Started (5 min setup) โ
โโโ 02. Hardware Guide (what you need) โ
โ
๐ก Intermediate โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โโโ 03. DeepSeek-R1 Guide โ
โโโ 04. Qwen 3.6 & 2.5 Guide โ
โโโ 05. Open WebUI Setup โ
โโโ 06. GGUF & Modelfile Customization โ
โโโ 07. Local RAG with AnythingLLM โ
โ
๐ด Advanced โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โโโ 08. Production Deployment โ
โโโ 09. Function Calling & Tool Use โ
โ
๐ฆ Bonus: Scripts + Docker Compose + Benchmarks โ
All on GitHub โฌ๏ธ โ
โ
GitHub.com/Lingdas1/local-llm-guide โโโโโโโโโโโ
๐ฏ Why This Series Exists
I started this journey because I was frustrated. Every AI tutorial assumed I had:
- Unlimited API budget ($200/month for ChatGPT Pro)
- A rack of A100 GPUs
- A CS degree from Stanford
Real life is different. I have a laptop, a curious mind, and no budget for API fees. I'm a medical student โ not a software engineer.
If I can figure this out, so can you. That's the whole point of this series.
๐ Quick Links
| What | Where |
|---|---|
| All source code | github.com/Lingdas1/local-llm-guide |
| Complete guide (one article) | Here |
| Beginner path | Start at Article #1 above |
| Developer path | Start at Article #3 above |
| Found this useful? | โญ Star the repo |
If this guide helped you, consider:
- โญ Starring the repo โ it helps others find it and you'll get notified when new chapters drop
- ๐ฌ Leaving a comment โ I read every one
- ๐ Sharing with a friend who's curious about running AI locally
Ling โ May 2026
I'm a medical student sharing what I learn about local AI. No CS degree, no big tech โ just honest guides for real people.
Top comments (1)
Article 02 is the one I needed. Most hardware guides only ask "can you load the model?" โ not whether you still have RAM left for Docker and a .NET API on the same box.
I compared a few mini PCs for that combo. Bigger quant on unified memory is nice for one-shot chat; for agent-style loops I kept picking the box with faster sustained throughput, even if the max model was smaller.
Did your hardware piece go into Apple Silicon vs a dGPU mini PC for dev + Ollama, or is that a follow-up?