Why can't you run ChatGPT on your laptop?

ChatGPT feels like software. You type, it responds. So why can't you just download it and run it offline?

You technically can run AI models locally. But not the good ones. Not easily. And definitely not ChatGPT.

Here's why.

The model is enormous

GPT-4 (the model behind ChatGPT) reportedly has over 1 trillion parameters.

Each parameter is a number, stored as 2-4 bytes. Do the math:

• 1 trillion parameters × 2 bytes = 2 terabytes minimum
• With overhead: probably 4-8 TB

Your laptop has maybe 8-16 GB of RAM. The model doesn't fit.

It's like trying to load a feature film into your phone's text message app. The container is the wrong size.

Memory bandwidth is the real killer

Let's say you had enough storage. You still couldn't run it fast.

When the model generates text, it needs to read through those parameters constantly. Every single token requires touching a huge portion of the model.

Your laptop's memory can push maybe 50-100 GB/second. Good, but not enough. The model needs to stream through terabytes for each response.

GPUsGPUGraphics Processing Unit — specialized chips that excel at parallel computations needed for AI.Click to learn more → have much faster memory (up to 3 TB/s on high-end chips), which is why AI runs on them. But even then, you need many GPUs working together.

ChatGPT runs on clusters of GPUs worth millions of dollars.

It's not just hardware, it's also secrecy

OpenAI hasn't released GPT-4's weights. They're a closely guarded secret.

Why? Because training GPT-4 cost hundreds of millions of dollars. The model weights are the product. Releasing them would be like Coca-Cola publishing their recipe.

So even if your laptop was a supercomputer, you couldn't run ChatGPT. You don't have access to the actual model.

What you CAN run locally

There's good news: smaller, open models exist.

Llama (Meta): 7B to 70B parameters. The 7B version runs on a decent laptop.

Mistral: Competitive quality at smaller sizes. 7B runs great locally.

Phi (Microsoft): Tiny but surprisingly capable. Runs on phones.

These models are way smaller than GPT-4 but can still:

• Answer questions
• Write code
• Summarize documents
• Have conversations

The trade-off: they're not as smart. They hallucinate more. They can't handle complex reasoning as well.

How to actually run local AI

If you want to try:

1. Ollama: Dead simple. One command to download and run models. Works on Mac, Linux, Windows.

2. LM Studio: Nice GUI. Browse and download models like apps.

3. llama.cpp: For nerds. Maximum performance, runs on CPU or GPU.

4. Hugging Face: The app store for AI models. Thousands of options.

A good laptop with 16GB RAM can run 7B parameter models smoothly. 32GB lets you run 13B. You need a GPU for anything larger.

The cost comparison

Running ChatGPT (GPT-4) through OpenAI: ~$10-30/month for typical use.

Running it yourself (if you could):

• 8x NVIDIA H100 GPUs: ~$250,000
• Electricity: hundreds per month
• Cooling, maintenance, expertise...

For most people, paying OpenAI $20/month is the obvious choice.

Why this matters

The fact that you can't run the best AI locally has big implications:

Privacy: Your conversations go to company servers. For sensitive work, that matters.

Dependence: If OpenAI goes down, raises prices, or changes policies, you're stuck.

Censorship: Companies decide what their models will and won't say.

Access: Internet required. Subscription required. Not everyone has both.

Local AI solves these problems, but it's slower and dumber. For now.

The future

Every year, models get more efficient. What required a datacenter in 2023 runs on a laptop in 2025.

Maybe in a few years, GPT-4-level quality will run locally. Apple is working on it. So is everyone else.

Until then: cloud AI for power, local AI for privacy.

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Want to understand why AI hardware costs so much? Read Why are GPUs so expensive?, the supply chain behind the AI boom.