Beginner
Local AI for Small Business: Email, Invoicing, and Customer Support Without Monthly Subscriptions
A 5-person team spends $1,500-3,000/year on AI subscriptions. A $600 mini PC running Ollama replaces all of them. Here's the setup, the workflows, and the math.
What Can You Actually Run on 4GB VRAM?
1B-3B models run at 18-55 tok/s. Qwen 2.5 3B at Q4 is the sweet spot for chat and simple coding. 7B models don't fit. What works on GTX 1050 Ti and 1650, and when to upgrade.
Stable Diffusion Locally: Getting Started
SD 1.5 runs on 4GB VRAM, SDXL needs 8GB, Flux needs 12GB+. Generate unlimited images for free in under 5 minutes with Fooocus or ComfyUI. Setup, models, and first image tips.
Best Models Under 3B: Small LLMs That Work
The best models under 3B parameters for laptops, old GPUs, Raspberry Pi, and phones. What works, what doesn't, and which tiny LLM to pick for your use case.
What Can You Actually Run on 8GB VRAM?
Qwen 3.5 9B is the new king of 8GB VRAM — 7GB at Q4_K_M with native vision. Plus every model that works on RTX 4060 and 3060 Ti, Stable Diffusion benchmarks, and the best upgrade path. Updated March 2026.
Used Optiplex + RTX 3060 = Local AI for Under $450 (Full Build)
$100 used Optiplex, $180 RTX 3060 12GB, done. Runs 14B LLMs at 25 tokens/sec and Stable Diffusion out of the box. Complete parts list, where to buy cheap, assembly photos, and first benchmarks.
Run Your First Local LLM in 15 Minutes
Install Ollama, pull a model, and chat with AI offline—all in 15 minutes. Works on any Mac, Windows, or Linux machine with 8GB RAM. No accounts, no API keys, no fees.
Quantization Explained: What It Means for Local AI
Q4_K_M shrinks a 7B model from 14GB to ~4GB while keeping 90-95% quality. What every quantization format means, how much VRAM each saves, and which to pick for your GPU.
Ollama vs LM Studio: Speed, Setup, and Verdict
Ollama gives you a CLI with 100+ models and an OpenAI-compatible API. LM Studio gives you a visual GUI with one-click downloads. Most power users run both—here's when to use each.