Apple's AI Breakthrough Will Transform Your iPhone in 2024

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TLDR:

AI Research of the Week: Apple’s Discovery That Will Change the iPhone

Leaders: What does it take to Make an AI Superhuman?

🤯 AI Research of the week 🤯

Out of all the big-tech titans, one of them slept through the AI revolution of 2023.

Or so we thought.

Apple, the most valuable company and brand in the history of capitalism, has finally given real details of its intentions with Generative AI with the presentation of Flash-memory Large Language Models (LLMs), a discovery that can change how we deploy AI and make it completely ubiquitous in our lives.

And the main outcome?

Transforming the likes of the iPhone and all other Apple hardware into entirely new digital products.

The Year of Efficiency

Last year, Mark Zuckerberg predicted that “2023 would be the year of efficiency.”

Of course, they meant cutting costs. But in our case, Apple is thinking about AI disruption.

The memory problem

If 2023 is the year humanity discovered it could train huge models, 2024 will be the year we will learn to run these huge models efficiently.

But why is this so important and why is Apple putting its focus here?

Basically, Apple’s entire AI value proposition is achieving what is thought as the holy grail of Generative AI model serving: running huge LLMs on smartphones.

To understand how seemingly impossible this is, let’s see an example.

If we take LLaMa 2 7B, a minuscule model by today’s standards, the weights file alone occupies 14 GB of DRAM memory, knowing it has a half-precision floating point format.

Half-precision means that each parameter occupies 16 bits, or 2 bytes of memory. As we have 7 billion of them, that means you need 14 billion bytes, or 14 GB, just to host the model.

That is almost two times the RAM capacity of the iPhone 15 Pro (8 GB), meaning that even a very small LLM model can’t be used on smartphones.

And for larger models, you need a state-of-the-art GPU cluster, requiring hundreds of thousands of US dollars in investment.

What’s more, that is pocket change if you want to run models like GPT-4, as companies like OpenAI literally burn that money, and more, on the daily.

By the looks of it, even though LLMs could have the potential to disrupt the smartphone market entirely, it’s an impossible task unless we figure out how to increase smartphones’ RAM capacity by orders of magnitude, which is not happening any time soon.

That, or you can leverage Apple’s recent breakthrough.

Flash LLMs

To understand how important Apple’s leap forward is, we need to understand how LLMs are run in the first place.

The Access Issue

Generally speaking, computers leverage two types of memory: Flash and RAM.

The former is used mainly for storage, while the latter is used as the ‘workspace’ of the computer at any given…

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