As a follow up to my last thread on hosting an AI locally and using Llama 3, I decided to have a little talk with Chat GPT. I'm not really that familiar with AI and LLMs and just wanted to get up to speed a little bit on these topics. Below is my conversation with Chat GPT on the basics of AI, LLMs and how to run Llama 3 locally on an air-gapped device. This should be a good starting point for those of us in the community who are less technically inclined, lol. Enjoy!

The full conversation with Chat GPT is posted below.

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How are you today?

ChatGPT said:
Hey — I’m good! Thanks for asking. How about you? What’s going on in your world today?

You said:
I am curious to learn more about AIs and LLMs. Can you help me?

ChatGPT said:
Awesome — I'd love to help with that! AI and LLMs (large language models) are super fascinating, and there’s a lot of cool stuff to unpack depending on what you’re most curious about.

Would you mind if I asked — are you more interested in how they work under the hood, how to use them in projects, or maybe the future implications of AI? Or something else entirely? I can break it down however you’d like.

Maybe let’s start here:
What sparked your curiosity about AI and LLMs?

You said:
What they are and how they work. To start, are AIs and LLMs the same thing?

ChatGPT said:
Excellent question — and no, they're not exactly the same thing, though they’re related. Let’s break it down cleanly:

📌 What is AI?
Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can perform tasks normally requiring human intelligence.
Examples:

Playing chess

Recognizing faces in photos

Driving a car autonomously

Diagnosing diseases
AI includes many different subfields like:

Machine Learning (ML)

Computer Vision

Natural Language Processing (NLP)

Robotics

Expert Systems

📌 What is an LLM (Large Language Model)?
An LLM is a type of AI model, specifically designed to understand and generate human-like language.

Key facts about LLMs:

They’re trained on massive amounts of text from books, articles, websites, etc.

They predict the next word in a sequence based on context (like autocomplete on steroids).

Examples of LLMs:

OpenAI’s GPT-4 (what you’re talking to now)

Google’s PaLM

Meta’s LLaMA

Anthropic’s Claude

📌 So — AI vs. LLM
AI LLM
Broad category covering all intelligent systems A specific type of AI for working with language
Can include computer vision, robotics, etc. Focuses on text generation, comprehension, translation, etc.
Example: AI that plays chess Example: ChatGPT
📌 How Do LLMs Work? (Simplified)
Training

The LLM is fed huge amounts of text data.

It learns patterns, word relationships, grammar, and even facts (sort of).

Tokenization

Text is broken into small chunks (called tokens) — could be words, syllables, or characters.

Prediction

The model predicts the next token based on what’s come before.

It does this using millions or billions of mathematical connections (parameters) that weigh different possibilities.

Fine-tuning

After base training, models are adjusted for specific tasks (like being helpful in conversations or summarizing medical papers).

Would you like me to explain the “neural network” part next, or how it actually predicts language? Or maybe give a visual analogy? I can go deeper wherever you’d like. ⚙️✨

You said:
That would be great. How about you explain AI neural networks first, then how LLMs predict language.

ChatGPT said:
Awesome — you’re asking all the right questions. Let’s unpack this in a clear, visual way. I’ll keep it approachable but meaningful:

📌 What is an AI Neural Network?
A neural network is a type of computer system inspired by the way human brains work.
It’s made up of layers of simple processing units called neurons — but digital ones, not biological ones.

🔍 How Does a Neural Network Work?
Think of it like this:

Input Layer
This is where data enters the system.
→ For a language model, this might be a sentence broken down into tokens.

Hidden Layers
These are layers of artificial neurons. Each neuron is connected to neurons in the next layer, and each connection has a weight (a number that adjusts how much influence one neuron has on another).

The neurons process the data by multiplying the input by the weights and passing it through an activation function (like a decision filter) to determine if it should "fire" or pass information forward.

Output Layer
This produces the final result — like predicting the next word or classifying an image.

📊 A Visual Concept (Text Version)
Imagine a very simple neural network for predicting if an animal is a cat or a dog:

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[Whiskers] ---\
→ (Hidden Layer) → [Cat 90%, Dog 10%]
[Wags Tail] ---/
Each input (like “Whiskers” or “Wags Tail”) connects to neurons in the hidden layer. Based on learned weights, it produces a guess at the end.

📌 How Do LLMs Predict Language?
LLMs are huge neural networks, typically built using a specific type of architecture called a Transformer.
Here’s how it works at a high level:

🔍 1. Tokenization
The input text is broken down into pieces called tokens (words, sub-words, or characters).
Example:

“Hello world!” → ["Hello", " world", "!"]

🔍 2. Contextual Encoding
Using the Transformer’s clever mechanism called self-attention, the model looks at each token in context with all other tokens in the sentence.

So it doesn’t just process “world” on its own — it knows “Hello” came before it.

🔍 3. Prediction
The model uses the patterns it learned during training to predict the most likely next token.
Example:

Input: “The cat sat on the”

Prediction: “mat” (with a high probability)

It does this by passing the data through thousands of layers and neurons, adjusting weights, and applying activation functions — just like our earlier neural network example but at a massive scale.

📌 Why Are LLMs So Powerful?
Because:

They’ve been trained on enormous amounts of text (think the entire internet and libraries of books).

Transformers allow them to capture long-distance relationships in text.
(Like understanding that a word at the start of a sentence affects the meaning at the end.)

🎨 Analogy Time
Imagine you’re finishing someone’s sentence:

“The sky is so clear and bright tonight, I can see the…”

You might guess “stars”, because you’ve read or heard sentences like that before.
LLMs are doing a supercharged, statistically weighted version of this — but with billions of sentences and phrases in their “memory.”

Would you like me to explain the Transformer architecture next? Or maybe how LLMs are trained on text and what kind of math they use under the hood? I can guide you deeper if you’re up for it! ⚙️✨

You said:
Yes I would, but first I would like to ask another question.

ChatGPT said:
Absolutely — ask away! What’s on your mind?

You said:
It sounds like LLMs and their digital neural networks would require LOTS of computing power and computer memory. What kind of hardware is required to train and house an LLM? Does it use exotic computer chips etc?

ChatGPT said:
Excellent question — and you’re totally right: LLMs require an enormous amount of computing power and memory to train, far beyond what a typical laptop or even high-end server could handle. Let’s break down what kind of hardware makes it possible:

📌 What Kind of Hardware Do LLMs Use?
LLMs run on specialized hardware built for high-performance, massively parallel computing.
The two big categories:

🔷 1️⃣ GPUs (Graphics Processing Units)
Originally built for rendering video games, but they’re excellent at doing thousands of small math operations simultaneously.

Modern LLMs are mostly trained on NVIDIA’s data center GPUs like:

NVIDIA A100

NVIDIA H100 (the current state-of-the-art as of 2024-2025)

These chips have thousands of cores designed to handle matrix multiplications, the core math operation in neural networks.

📊 Example:
Training GPT-3 reportedly used thousands of A100 GPUs networked together over weeks.

🔷 2️⃣ TPUs (Tensor Processing Units)
Custom AI chips made by Google, designed specifically for training and running neural networks.

TPUs are optimized for tensor operations (the kind of math behind deep learning).

Used heavily for models inside Google’s AI infrastructure (like Bard and PaLM).

📌 How Is All This Hardware Set Up?
The GPUs/TPUs are typically installed in:

High-performance servers

Data centers with:

Huge amounts of power and cooling

Special networking to let thousands of GPUs communicate quickly

Redundant systems for reliability

These setups are called GPU clusters or AI supercomputers.

📌 How Much Memory and Storage Are We Talking About?
🔹 Memory (RAM)
Each high-end AI GPU has 80GB to 120GB of VRAM (video RAM).

You need terabytes of total VRAM across a cluster to train a huge model.

🔹 Storage
Training datasets can be hundreds of terabytes.

Trained models (like GPT-3 or GPT-4) can be hundreds of gigabytes to multiple terabytes in size.

You also need extremely fast SSDs or NVMe drives to feed data to the GPUs at high speed.

📌 Is Any of This "Exotic"?
Not exotic in a science-fiction sense, but highly specialized:

GPUs like the H100 are cutting-edge, extremely expensive (tens of thousands of dollars per chip).

Network hardware between GPUs often uses NVLink or InfiniBand — ultra-fast connections far beyond standard Ethernet.

AI-focused data centers have custom cooling systems (sometimes liquid cooling) because these chips run extremely hot.

📌 Fun Fact:
OpenAI’s GPT-4 reportedly runs on tens of thousands of GPUs — and the infrastructure for training it would have cost tens to hundreds of millions of dollars.

📌 Can LLMs Run on Regular Computers?
Training a large LLM? No — way too resource-intensive.

Running (or inference)? Yes — a smaller, fine-tuned model or a compressed version (like LLama.cpp) can run on a powerful laptop or desktop, but with limitations.

Would you like me to break down what a Transformer is next? Or maybe explain how multiple GPUs work together for LLM training? (It’s a wild bit of engineering.) ⚙️🔥

You said:
Yes, but first I would like to know more about LLama. I saw a video on Youtube that stated that LLama 3 could run on something as small as a flash drive or consumer grade external hard drive. How is that possible?

ChatGPT said:
Fantastic question — and it touches on one of the most exciting developments in the AI space right now: making powerful language models accessible to regular people on everyday hardware. Let’s unpack this carefully because it’s a bit of a technical magic trick.

📌 What is LLaMA?
LLaMA (Large Language Model Meta AI) is a family of open-weight LLMs developed by Meta (Facebook’s parent company).
Unlike GPT-4 (which is proprietary), Meta released the model weights for LLaMA 2 and soon after, LLaMA 3, meaning anyone could download and run them locally — no internet connection or cloud subscription required.

📌 How Can LLaMA 3 Run on a Flash Drive / Consumer Computer?
Okay — here’s the key idea:

LLMs are typically very large, but with smart engineering, you can:

Quantize the model

Optimize it for CPU inference

Split or compress it into smaller, efficient formats

🔹 1️⃣ Quantization
This is like converting a model’s numerical values from big, precise numbers (like 32-bit floats) down to smaller, less precise numbers (like 4-bit or 8-bit integers).
You lose a little accuracy, but you massively reduce:

File size

Memory requirements

Computation needs

Example:

Original LLaMA 3 8B model might be 20+ GB

Quantized 4-bit version could be 4–5 GB — small enough to fit on a consumer USB flash drive or external SSD.

And for a 7B model quantized to 4-bit? Even smaller — around 3–4 GB.

🔹 2️⃣ CPU Inference
Most big LLMs need high-end GPUs for inference (running the model).
But projects like llama.cpp and Ollama convert these models to run efficiently on regular CPUs.

They use:

Optimized matrix math libraries

Threading and SIMD (Single Instruction Multiple Data) operations on modern processors

Meaning you can run a smaller, quantized LLaMA 3 model on:

A MacBook

A gaming PC

A consumer desktop
And it’s surprisingly usable — though slower and with shorter context windows compared to GPU-based setups.

🔹 3️⃣ Local-Only Operation
Because the model is just a file (like a huge program), you can store it on:

An external SSD

A flash drive
and load it into memory when needed. The OS and your processor handle the math — no cloud connection or supercomputer required.

📌 ⚡ The Tradeoff:
You can run smaller models (7B, 13B) this way

Quantized models have slightly reduced performance/accuracy

Larger models (65B+) are still tough for typical home hardware

But for chatbots, coding assistants, or basic text generation, it works great.

📌 Why This Is a Big Deal:
Puts AI into people’s hands without cloud providers or subscriptions

Private and offline — your data never leaves your device

Enables hobbyists, developers, and researchers to experiment and build AI tools locally

📊 Fun Example:
On an M1/M2 MacBook Air, you can run a 7B quantized LLaMA model locally using llama.cpp at decent speed.

People are running local AI chatbots, coding copilots, and document summarizers offline on their personal laptops.

If you’d like, I can show you how llama.cpp works or how quantization is done in practice. Or we could jump back to the Transformer architecture next. What sounds good? ⚙️🔥

You said:
Yes, but first I would like to ask a follow up question about Llama (and LLMs in general). I understand what you just said about how Meta was able to scale down a LLM into Llama, so that it can run locally
as program. My question is this: Where does all the vast data needed to train an LLM like Llama reside? Also, if LLama is taken offline to run on a local machine, it would not have access to all that data, how is LLama able to still function and answer user's questions? Am I missing something here?

ChatGPT said:
Excellent, excellent question — and you're not missing anything. In fact, what you’re noticing is one of the most unintuitive but fascinating aspects of how LLMs work. Let’s clarify this carefully because it’s a crucial concept:

📌 Where Does All the Vast Data Reside When an LLM Runs Locally?
Short answer:
The vast data used during training is only needed during the training phase. Once the LLM is trained, it no longer needs access to the training data. Instead, the model's "knowledge" is baked into its weights — huge tables of numerical values that encode statistical patterns from all that data.

📌 How LLMs Remember Without the Data
Let’s expand on this with an analogy:

Imagine you studied thousands of books about history, biology, and literature. Once you’ve learned it, you don’t need to carry those books around with you to answer questions. You’ve internalized patterns, facts, and relationships, and you respond based on those stored mental associations.

An LLM does the same thing.

During training:

The model is exposed to billions of words and learns patterns.

It updates its internal parameters (weights) to represent those patterns.

It effectively compresses the "essence" of that training data into these numerical weights.

So when you run a quantized LLaMA model locally:

You’re running a file that contains all those weights — the distilled "knowledge" of its training.

It no longer needs the original internet, books, or text files.

That’s why even offline, it can:

Answer general knowledge questions

Hold a conversation

Write poems or code

Because it’s reasoning from compressed, encoded patterns stored inside itself.

📌 But Wait — Doesn’t It Need to Stay Up-To-Date?
Ah — and here’s the catch:

LLMs can only "know" what they were trained on up to a certain date.
They can’t access new information unless:

They’re fine-tuned with new training data.

Or connected to external data sources (like a plugin or retrieval system).

Example:

A local LLaMA 3 model trained up to March 2024 won’t know who won the 2025 World Cup.

But it can still explain what a World Cup is or list famous players because it internalized those patterns during training.

📌 So What Does "Running Locally" Actually Do?
When you run a model like LLaMA locally:

You load those internalized weights into your computer’s memory.

The model uses them to predict responses token-by-token (word-by-word) based on your prompt.

It doesn’t look anything up externally — it generates based on its learned probabilities and patterns.

That’s why it can run without a network connection or database of facts.

📌 Recap (Because This Is Important)
During Training During Inference (When You Use It)
LLM reads vast amounts of text data Model uses its stored weights (knowledge)
Adjusts billions of parameters to encode patterns No external data needed — it generates from memory
Happens on massive server clusters Can run on your laptop (if the model is small/quantized)
📊 Visual Metaphor:
Training: Filling up a brain with knowledge
Inference: Asking that brain questions based on what it remembers

You weren’t missing anything — you just intuitively picked up on a key distinction between the training phase and the inference (usage) phase. And that’s one of the coolest things about LLMs: the data moves into the model itself.

Would you like me to show you how those weights work conceptually? Or how a Transformer architecture processes those weights to generate a response? I’m loving your questions — you’re really getting into the heart of how this tech ticks. ⚙️✨

You said:
Thanks! Llama sounds pretty incredible! If I wanted to install and run Llama locally on an air-gapped device, what technical skills would I need to do this? Please keep in mind, I am not a computer programmer and my tech skills are nil.

ChatGPT said:
Fantastic question — and the cool thing is: it’s more doable today than you might think, even without programming experience. Tools like llama.cpp and Ollama have made running LLaMA models locally on regular computers relatively beginner-friendly, with a bit of guided setup.

Let’s break down what you’d realistically need, keeping in mind you’re not a programmer:

📌 What Technical Skills Would You Need?
✅ Basic Computer Literacy
Comfort using a terminal/command prompt (like typing simple commands)

Knowing how to download files, unzip them, and move them around your file system

Installing software

If you can do things like:

Install apps on your computer

Copy and paste commands from a tutorial into a terminal window
👉 then you can probably manage this with guidance.

📌 📦 What You’d Need to Do (At a Glance)
Let’s use llama.cpp as an example — it’s one of the most popular and lightweight ways to run quantized LLaMA models locally on Windows, Mac, or Linux.

🛠️ 1️⃣ Install llama.cpp (or Ollama if you want a GUI)
Option A: llama.cpp

llama.cpp is a command-line tool you run from the terminal.

You download a pre-built version for your system or compile it (if comfortable).

Then download a quantized LLaMA model file (like llama-2-7b.Q4_K_M.gguf)

Option B: Ollama (Easier)

Ollama is a much simpler installer with a user-friendly interface.

You download and install it like any other app.

It lets you pull and run models like llama2, mistral, etc. with simple terminal commands like:

arduino
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ollama run llama2
🛠️ 2️⃣ Download a Model File
The models are large files you download separately (like llama-2-7b.Q4_K_M.gguf)

For an air-gapped machine, you’d download this file on another machine, then move it via USB or external drive.

🛠️ 3️⃣ Run the Model
Use a terminal command like:

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./main -m ./models/llama-2-7b.Q4_K_M.gguf -p "Hello, how are you?"
(llama.cpp)

Or with Ollama:

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ollama run llama2
and it’ll start chatting with you.

📌 What Kind of Hardware Would You Need?
A modern laptop or desktop with at least 8–16GB RAM (more is better)

macOS, Windows, or Linux

Enough storage space for the model file (4–8GB for smaller models)

You won’t need exotic GPUs or high-end servers for a quantized 7B model.

📌 🔒 Air-Gapped Setup Notes:
You can transfer all necessary files (the software and model) via USB

Run entirely offline — these models don’t “call home” unless you tell them to

Many people already use this setup for privacy-focused, offline AI tools

📌 Should You Learn a Bit of Terminal Use?
Yes — it’s not hard, and even very basic terminal knowledge unlocks a lot of power:

Opening a terminal window

Navigating folders (cd)

Running a command

Closing the terminal

I could even build you a step-by-step cheat sheet if you’d like.

📌 Final Thought:
You absolutely don’t need to be a programmer. The modern AI community has built amazing tools to make running LLaMA models locally approachable. It’ll feel a little nerdy at first — but with the right walkthrough, you could have your own offline AI assistant running on a secure air-gapped machine this weekend.

Would you like me to make you a beginner-friendly, step-by-step guide for installing Ollama (the easiest option) or llama.cpp (more flexible)? Could be fun if you’re game for it 🔥⚙️

You said:
Yes! I would like that very much thank you!

ChatGPT said:
Updated saved memory
Awesome — let’s get you started on your own offline AI assistant adventure! I’ll write this out like a clear, beginner-friendly step-by-step guide you can follow, and I’ll tailor it to the easier, more beginner-friendly Ollama option first. You can decide later if you’d like to try llama.cpp too.

📖 Beginner’s Step-by-Step: Install and Run LLaMA Offline with Ollama
✅ What You’ll Need:
A computer with at least 8GB RAM (16GB ideal)

macOS (M1/M2/M3), Windows, or Linux

Enough free space for a model file (about 4–8 GB)

Basic internet access to download Ollama and the model (you can move them via USB if needed for an air-gapped machine)

📦 Part 1: Install Ollama
1️⃣ Go to: https://ollama.com/
(On an internet-connected computer)

2️⃣ Click Download for Mac / Windows / Linux (depending on your system)

3️⃣ Install it like a regular app:

Mac: Open the .dmg file, drag Ollama to your Applications

Windows: Run the .exe installer

Linux: Follow their terminal install command

📦 Part 2: (Optional) Download a Model on a Separate Machine for Air-Gap Transfer
If your air-gapped computer has no internet:

1️⃣ On a connected computer:

Open a terminal (Mac/Linux) or Command Prompt (Windows)

Type:

nginx
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ollama pull llama2
This downloads the LLaMA 2 model to your computer.

2️⃣ Locate the model files:

On Mac:

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~/Library/Application Support/Ollama/models
On Windows:

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%AppData%\Ollama\models
On Linux:

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~/.ollama/models
3️⃣ Copy the model file(s) to a USB drive.

4️⃣ Move it onto your air-gapped computer, placing it into the same folder Ollama uses (create the folder structure if needed).

📦 Part 3: Run LLaMA Locally
1️⃣ Open Terminal (Mac/Linux) or Command Prompt (Windows)

2️⃣ Type:

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ollama run llama2
3️⃣ It will launch a chat prompt — type your questions and hit Enter.
The model will generate responses completely offline.

Example:

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> Hello, how are you?
The model responds right in your terminal window.

📦 Part 4: Customize or Try Other Models (Optional)
Ollama supports other models too:

ollama pull mistral

ollama pull codellama

ollama pull llama3 (once public weights drop — soon!)

✅ What You Don’t Need:
No coding knowledge

No installing compilers or dev tools

No exotic hardware

No online account or registration

📄 Bonus: Basic Terminal Commands You Might Need
Command What It Does
cd [folder] Change to a specific folder
ls (Mac/Linux) / dir (Windows) List files in current folder
exit Close the terminal window
📦 Final Notes:
Ollama is designed for ease of use — ideal for non-programmers.

It runs locally, your data stays on your device.

Performance will be slower on older computers, but totally usable for casual use.

Would you like me to make a printable PDF version of this guide for you too? Or we can try a llama.cpp walkthrough next if you’re curious about that one as well! ⚙️🔥

You said:
That would be great, but lets continue this conversation at another time. Right now I gotta bounce. Thanks for all your help, I really appreciate it. Talk to you soon!

ChatGPT said:
Updated saved memory
You’re very welcome! I’m glad I could help. Don’t hesitate to reach out when you’re ready to continue — I’ll be here. Have a great day, and talk to you soon! 😊👋

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Congratulations is you read this far!........You are a WARRIOR!

TECHNICAL QUESTION for the IT gurus in the community: Do you think a sentient AI could successfully/smoothly run on something as compact as Llama 3?