Local LLM Guide · 2026

The AI you're
renting could
be yours.

Run Llama, Qwen, Mistral, and DeepSeek on your own hardware. Private. Unlimited. No monthly fee after today. Works on any GPU with 4 GB+ VRAM.

Buy the guide — $37 Instant download. 7-day refund guarantee.

The problem

You're paying rent
for something you can own.

Cost

$20/month is $240/year. For a model you don't control, can't use offline, and that changes without warning.
Privacy

Every prompt you send is potentially logged, used for training, or stored indefinitely. Your code. Your ideas. Their servers.
Limits

Rate limits. Context caps. Temporarily unavailable. You pay more or you wait.
Dependency

Prices change. Models get restricted. APIs go down. You have no control over any of it.

With local AI

Run it 24/7 with no usage limits
Nothing leaves your machine
Works fully offline
Switch models in seconds
Connect to any app via API
Works on hardware you already own

Hardware compatibility

Probably works on
what you already have.

Written and tested on a GTX 1060 6 GB — 8-year-old hardware. If it works there, it works anywhere.

Hardware	Model	Speed	Context
GTX 1060 · 6 GB	Qwen3.5-9B Q4_K_M	12–20 t/s	4096
RTX 3060 · 12 GB	Qwen3.5-14B Q4_K_M	20–35 t/s	8192
RTX 3070 · 8 GB	Llama 3.3-8B Q5_K_M	18–28 t/s	6144
Apple M2 · 16 GB	Qwen3.5-14B Q4_K_M	20–30 t/s	8192
No GPU · 16 GB RAM	Any 7B Q4_K_M	2–5 t/s	2048

Contents — 80 pages

Everything, in order.
No 10-page introductions.

What hardware you actually need

Full GPU table from 4–24 GB VRAM. Used hardware by budget. Why you probably don't need to upgrade.

Models and quantization — GGUF, Q4_K_M explained

What the formats mean. Why Q4_K_M is the right default. Llama vs Qwen vs DeepSeek — when to use each.

llama.cpp — full setup and configuration

Install on Windows, Linux, macOS. Every parameter with real values. Six ready-to-copy hardware configs.

Ollama — manage models without complexity

Two-command install. Pull any model by name. REST API setup. Custom Modelfiles to shape behavior.

LiteLLM — connect local AI to any application

Full config.yaml walkthrough. Connect VS Code, Cursor, Continue, and any OpenAI-compatible client.

Performance — getting the most from your hardware

GPU layer offloading. Context vs speed tradeoffs with real numbers. Batch size, threads, monitoring.

Troubleshooting — the 25 most common errors

CUDA out of memory. Model won't load. Slow generation. API not responding. Each error with a tested fix.

10 things you can do with local AI right now

Coding assistant. PDF summarizer. Private chat. RAG on your documents. Each with setup instructions.

Included in the ZIP

Copy the configs.
Skip the setup time.

config.yaml LiteLLM config with 5 models pre-configured. Replace the paths and start the server.

start-ai.sh Single script that starts llama-server, Ollama, and LiteLLM in the correct order.

gpu-table.csv 50+ GPUs with optimal -ngl, context, and batch settings. Find your GPU, copy the values.

prompts.md 20 tested prompt templates for coding, writing, summarization, and analysis.

.env.example All environment variables for every tool, with comments explaining each one.

llama-server.service Systemd unit file for Linux. Drop it in and your model starts with the system.

Get started

Your own AI.
Running tonight.

80 pages. Tested configs. Real hardware. One purchase and your monthly AI bill stops.

Buy for $37 — instant download

7-day money-back guarantee. Follow the guide and can't get it running — full refund, no questions.

The AI you'rerenting couldbe yours.

You're paying rentfor something you can own.

With local AI

Probably works onwhat you already have.

Everything, in order.No 10-page introductions.