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The Magic Behind Running Powerful AI Models on Your Regular MacBook


Ever come across the term "llama.cpp" and wonder why it's causing such a stir? Let's break it down in simple words.


Imagine wanting to run a big and complex program, say a language learning model (LLM), on your personal computer. You'd probably think, "Don't I need a supercomputer for this?" But surprise! With llama.cpp, you can run these large models on a device as commonplace as a MacBook.



The Basics

When you run these models at their simplest setting (think of it as asking your computer to do one task at a time), what's primarily used is the computer's memory, not its speed or processing power. It's like having many workers (computer chips) waiting for tasks, but the tasks arrive slowly, one-by-one, through a tiny door (this is the memory bandwidth).


A Comparison

Let's compare two devices:

1. A supercomputer component called A100.

2. Your regular MacBook M2 chip.

Even though the A100 is theoretically 200 times more powerful in processing tasks, when it comes to moving tasks around (memory bandwidth), it's only 20 times better than the MacBook. So, when running big models like LLMs, the MacBook is only around 20 times slower, not 200 times as you might initially think.


But, There's a Twist

Things change when you ask the computer to multitask, like handling many requests at once or during training sessions. Here, once the tasks enter the room, they can be distributed and processed by the waiting workers efficiently. This makes the supercomputer component, A100, show its true power.


In a Nutshell

Wondering why these big AI models work surprisingly well on your MacBook? It's because, for simple tasks, the difference in memory bandwidth (task moving capacity) between supercomputers and regular chips like in a MacBook isn't that vast. While supercomputers excel in processing, our everyday devices are catching up in moving tasks around.


So next time someone talks tech jargon about llama.cpp, you can casually mention, "Oh, it's all about memory bandwidth, isn't it?" 😉

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