Every AI you’ve ever used is a model: one big file of numbers, plus a program that runs it. That’s the whole secret. This page is the working vocabulary you need to build at home — earned from running dozens of them on one PC.
What a model actually is
Training bakes patterns from mountains of examples into billions of numbers (“weights”). Running a model (“inference”) is just math between your input and those weights. A model doesn’t know things the way a database does — it has instincts. That’s why it can write a poem and also confidently invent a fact: same mechanism.
The numbers that matter to you
- Parameters (7B, 35B, 70B…) — the model’s size. Bigger = generally smarter, slower, hungrier. The home sweet spot in 2026: 7B–35B.
- VRAM — the real constraint. Rough rule: a model needs its size-on-disk in GPU memory, and quantization (compressing weights to 4–8 bits) cuts that by half or more for a small quality cost. A 32GB card runs a quantized 70B; an 8GB card runs a great 7B.
- Context window — the model’s working memory per conversation, in tokens. More context costs VRAM too; we run a 262K-capable model at 16K for chat because headroom = speed.
- Thinking modes — some models reason step-by-step before answering: brilliant for hard problems, painful for chat (minutes of silence). Most expose an off switch. (We benchmarked this the hard way →)
Open vs closed weights (the split that defines everything)
- Closed: you rent access to a file on someone else’s computer (GPT, Claude, Gemini). Best raw capability, zero ownership.
- Open weights: you download the file (Apache/MIT licenses are the good ones). It’s yours forever — offline, private, fine-tunable. The open world runs months behind the frontier and closes the gap constantly; in 2026 an MIT-licensed model tops real usage leaderboards.
Our position is the whole site: rent when it wins, but own your engine.
The model types you’ll actually touch
| Type | What it does | Ours in action |
|---|---|---|
| LLM / chat | text in, text out; the “brain” | the agent’s brain |
| Multimodal / vision | also sees images and screens | giving your AI eyes |
| Diffusion (image) | noise → picture, guided by your words | ComfyUI from zero |
| Video | animate a still image | animate an image |
| TTS / voice | text → speech, cloneable | clone a voice |
| LoRA (add-on) | tiny file that teaches a big model one thing | character LoRA |
How to pick a model (the 4-question version)
- What job? Chat, code, images, voice — pick the type first.
- What fits? Your VRAM decides the size class; take the best quantized model that fits with headroom.
- What license? Apache 2.0 / MIT = build freely. “Open-ish” licenses with usage carve-outs deserve a read.
- Does it behave? Benchmark on your real tasks, not leaderboard vibes — clean structured output and fast first tokens beat two extra IQ points for agent work.
The fastest education is Step 1 of the beginner path: pull one model tonight and talk to it. The file on your disk will teach you the rest.