| title | What Are AI Models? Complete Beginner's Guide to Local AI |
|---|---|
| description | Understand how AI language models work, how they're trained, and why you can run powerful AI like ChatGPT on your computer for free. |
| keywords | AI models explained, how AI works, language models, local AI, ChatGPT alternatives, AI for beginners |
Look, I get it. Everyone's talking about AI but nobody explains what the hell these "models" actually are. Let me break it down without the jargon.
This guide explains how AI language models work, how they're trained, and why you can run powerful AI like ChatGPT on your own computer for free.
TL;DR: It's like a really smart autocomplete that read the entire internet and got good at predicting helpful responses.
Imagine you had a friend who spent their entire life reading - every book, every Wikipedia article, every blog post, every forum discussion. They read so much that they got really good at predicting what comes next in any conversation.
That's basically what an AI model is. It's a computer program that "read" huge chunks of the internet and learned patterns in how people write and communicate. When you ask it something, it's making educated guesses about what a helpful response would look like based on all that reading.
Here's the thing though: It's not actually thinking. It's just really, really good at pattern matching. But the results? They're pretty impressive.
Think "downloading the entire internet"
Companies scrape text from everywhere - books, websites, Reddit posts, news articles, you name it. We're talking about datasets with trillions of words. For perspective, that's like reading everything ever written, several times over.
Like the world's most intense word prediction game
They show the computer incomplete sentences:
- "The cat sat on the..."
- Computer guesses: "mat"
- Right answer? Gets a gold star. Wrong? Try again.
Do this billions of times with different sentences and eventually the computer gets scary good at predicting what comes next.
Because nobody wants an AI that's technically correct but acts like a jerk
After the prediction training, humans (and increasingly, other AI models) rate the responses:
- "Was this helpful?"
- "Was this polite?"
- "Did it answer the actual question?"
The AI learns to optimize for responses that humans actually want to receive. This is why chatbots say "please" and "thank you" instead of just spitting out raw information.
This is what changed between 2023 and now
Modern models are also trained to write out their reasoning before giving a final answer - and they're rewarded based on whether the final answer is actually correct. That's why a "thinking" or "reasoning" model pauses and produces a wall of internal notes before responding. It's slower, but it's dramatically better at math, logic, and multi-step problems.
Most current models let you turn this on or off depending on whether you want speed or accuracy.
- Bigger models = More capacity to store patterns = Usually smarter responses
- Smaller models = Faster, use less power = Good enough for lots of tasks
- Think of it like comparing a middle schooler to a PhD professor - both can help, but one has more knowledge to draw from
There's a twist though: mixture-of-experts (MoE) models split themselves into specialists and only wake up a fraction of the model for each word. A model labeled "35B-A3B" has 35 billion parameters total but only uses about 3 billion at a time. You need memory for all of it, but it runs at small-model speed.
- Better source material = Smarter AI (garbage in, garbage out)
- More training time = Better performance (but costs more)
- Smarter training techniques = More efficient learning
This is why a 4B model released this year often beats a 13B model from a couple of years ago. Technique improved faster than size did.
- Generalist models = Decent at everything, not amazing at anything specific
- Specialist models = Incredible at one thing (like coding), mediocre at everything else
It's like hiring people - do you want a jack-of-all-trades or a specialist?
The short version: Months of work and many millions of dollars.
The longer story: Training a frontier model takes months using tens of thousands of high-end accelerators running around the clock. We're talking eight or nine figures in compute alone, not counting the salaries or the data work.
This is why most of us don't train our own models from scratch - it's like asking why you don't build your own car from raw materials.
Good question! A few reasons:
-
Releasing weights is good strategy Meta, Google, Alibaba, IBM, Mistral, and even OpenAI now publish free models to build ecosystems and goodwill
-
Research benefits They want smart people to build cool things with their models
-
Competitive pressure If a free model is 90% as good as a paid one, the paid one has to keep getting better
-
The hard part's done Training costs millions, but copying the finished model costs pennies
It's like how pharmaceutical companies spend billions developing a drug, but generic versions are cheap once the patent expires.
One caveat: most of these are open weights, not open source. You get the finished model file, but usually not the training data or the code that produced it - and some come with license restrictions. Check the license if you're using one commercially.
- Tiny (under 1B parameters) - Runs on a phone. Good for simple, narrow tasks
- Small (2B-9B parameters) - Like a sharp generalist. This is where most people should live
- Medium (12B-32B parameters) - Like an experienced professional
- Large (70B+ parameters) - Like a domain expert. Needs serious hardware
Parameters are the model's internal dials - the numbers it tuned during training. More dials means more capacity to encode patterns, not a bigger list of stored facts.
- Chat / instruct models - Designed for conversations and following directions
- Reasoning models - Work through problems step by step before answering
- Code models - Specialized for programming and editing files
- Vision and multimodal models - Can look at images, and sometimes listen to audio
- Embedding models - Turn text into numbers so you can search it. The engine behind "chat with your documents"
- You type something - "What's the weather like?"
- Model breaks it down - Figures out you're asking about weather and current conditions
- Model generates a response - Predicts what a helpful response would look like, word by word
- You see the result - "I don't have access to current weather data, but you could check..."
Here's the weird part: The model isn't actually "thinking" about your question. It's running a very sophisticated prediction algorithm based on patterns it learned during training. But the results feel like thinking, which is pretty wild when you think about it.
Nope - They're incredibly sophisticated autocomplete, not digital brains
Wrong - They only know what was in their training data, which usually has a cutoff date
Definitely not - They make mistakes, especially about recent events, math, or specific facts. When one confidently invents something, that's called a hallucination, and it happens more than you'd like
Not really - Bigger models are often smarter, but they're also slower and need beefier hardware. A well-trained 4B model from this year beats a 13B model from a few years ago
Usually not quite - Most are "open weights." You get the model file, but not the training data or recipe, and licenses vary
Now that you know what these things actually are, here's why running them on your own computer is pretty great:
- Your business stays your business - No company logging your conversations
- No monthly fees - Pay once for hardware, use forever
- Works offline - Internet down? Don't care.
- You can tinker - Want to modify how it behaves? Go nuts.
- Educational - It's honestly fascinating to see how this stuff works under the hood
Now that you understand what's under the hood, you're ready to run one yourself! Check out our main guide to get started.
Bottom line: You're about to run the same technology that powers ChatGPT, except it's running on your machine, for free, and completely private. That's pretty cool.