This 1997 PC Just Ran an AI Model from 2025—Here's How

How a 25-Year-Old Pentium II Ran a Modern AI Model (No, Seriously)

Let’s get this straight: a 25-year-old Pentium II machine with 128MB of RAM—yes, less memory than your average smart toaster—just ran a modern AI model from 2025.

Not a simulation. Not an emulator. A real, physical PC from the 90s… running Meta’s LLaMA 2 transformer model.

This isn’t about retro computing nostalgia. It’s a full-on techquake—and a vision of a radically different AI future.

The Ancient Machine That Could

The machine in question? A dusty old Pentium II from 1997, bought for £118 on eBay.
No USB ports.
Just old-school PS/2 ports for mouse and keyboard.
128MB of RAM. No SSD. Just that classic beige box energy.

And yet, a team of researchers from EXO Labs, led by none other than Andrej Karpathy, got this dinosaur to run LLaMA 2 using nothing more than ~700 lines of pure C code (llama2.c) that Karpathy hand-wrote.

The result wasn’t just impressive. It was downright historic.

How They Pulled It Off

You might be wondering: how do you even get an AI model onto a machine that predates USB flash drives?

Here’s how they did it:

• Booted into Windows 98
• Used Borland C++ 5.02—yes, the same ancient IDE people used in the ‘90s
• Transferred files from a modern MacBook Pro using Ethernet and FTP, with static IPs (because USB was a no-go)

Then came the real wizardry:
The team had to:

• Rewrite data loaders
• Simplify memory usage
• Change variable declarations and types
• Optimize every bit of memory handling just to avoid crashes

Why? Because this machine was never designed to touch anything close to what modern AI requires.

What Models Did It Run?

The researchers tested several versions:

• Tiny LLaMA (~260k parameters): Ran at 39.3 tokens/sec — surprisingly usable speed
• 15M parameter model: Slower, but still functional — 1.03 tokens/sec
• Partial 1B parameter model: Crawled along at 0.0093 tokens/sec, but it worked

Let’s be real—these aren’t blazing-fast results. But that’s not the point.

The point is that modern AI, once thought exclusive to GPUs and data centers, can now run on hardware old enough to drink.

Why It Matters: Lean AI and the Future of Accessibility

This isn’t a gimmick. It’s a glimpse at what researchers are calling "Lean AI"—the idea that AI doesn’t have to be big, bloated, or dependent on high-end GPUs.

Just like Microsoft’s recent BitNet architecture, which uses ternary weights (-1, 0, 1), the model used here was ultra-compact, memory efficient, and optimized for inference on limited hardware.

Get this:

• BitNet-7B only needs 1.38GB of storage
• Can run on a basic CPU, no GPU required

That means powerful AI could run on:

• Budget phones
• Tablets
• IoT devices
• Legacy systems in developing nations
• Military or industrial hardware that can’t be upgraded

It’s not just cool—it’s a game-changer for global access.

Inference for the Masses

Sure, training these models still needs GPU clusters and major compute power. But inference—actually using the model—can be stupidly lightweight, as this project proves.

And that flips the entire AI economy on its head.

We’re used to thinking of AI as something that lives in the cloud, tied to massive infrastructure. But what if AI could be brought to the edge—to your router, your microwave, your old school PC?

This project says: it can. And it already has.

The Future Is Retro

This is more than a fun "AI on an old PC" story. It’s a warning shot across the data center skyline.

It tells us the future of AI might not be in expensive server farms or behind paywalled APIs—but in ultra-compact, lean models that anyone can run, on any device.

The revolution won't be streamed from the cloud. It’ll boot from a floppy.

Lean AI. Not nostalgia—just intelligence.

Stay sharp and keep decoding the future with Land of Geek Magazine—where even your toaster might be the next AI supercomputer.

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