[rahen]

No need for an RPi 5. Back in 1982, a dual or quad-CPU X-MP could have run a small LLM, say, with 200–300K weights, without trouble. The Crays were, ironically, very well suited for neural networks, we just didn’t know it yet. Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization. By the late 80s, a Y-MP might even have been enough to support a small conversational agent.

A modest PDP-11/34 cluster with AP-120 vector coprocessors might even have served as a cheaper pathfinder in the late 70s for labs and companies who couldn't afford a Cray 1 and its infrastructure.

But we lacked both the data and the concepts. Massive, curated datasets (and backpropagation!) weren’t even a thing until the late 80s or 90s. And even then, they ran on far less powerful hardware than the Crays. Ideas and concepts were the limiting factor, not the hardware.

[adwn]

> a small LLM, say, with 200–300K weights

A "small Large Language Model", you say? So a "Language Model"? ;-)

> Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization.

No, not even close. You're off by 3 orders of magnitude if you want even the most basic text understanding, 4 OOM if you want anything slightly more complex (like code autocompletion), and 5–6 OOM for good speech recognition and generation. Hardware was very much a limiting factor.

[rahen]

I would have thought the same, but EXO Labs showed otherwise by getting a 300K-parameter LLM to run on a Pentium II with only 128 MB of RAM at about 50 tokens per second. The X-MP was in the same ballpark, with the added benefit of native vector processing (not just some extension bolted onto a scalar CPU) which performs very well on matmul.

https://www.tomshardware.com/tech-industry/artificial-intell...

John Carmack was also hinting at this: we might have had AI decades earlier, obviously not large GPT-4 models but useful language reasoning at a small scale was possible. The hardware wasn't that far off. The software and incentives were.

https://x.com/ID_AA_Carmack/status/1911872001507016826

[adwn]

> EXO Labs showed otherwise by getting a 300K-parameter LLM to run on a Pentium II with only 128 MB of RAM at about 50 tokens per second

50 token/s is completely useless if the tokens themselves are useless. Just look at the "story" generated by the model presented in your link: Each individual sentence is somewhat grammatically correct, but they have next to nothing to do with each other, they make absolutely no sense. Take this, for example:

"I lost my broken broke in my cold rock. It is okay, you can't."

Good luck tuning this for turn-based conversations, let alone for solving any practical task. This model is so restricted that you couldn't even benchmark its performance, because it wouldn't be able to follow the simplest of instructions.

[rahen]

You're missing the point. No one is claiming that a 300K-param model on a Pentium II matches GPT-4. The point is that it works: it parses input, generates plausible syntax, and does so using algorithms and compute budgets that were entirely feasible decades ago. The claim is that we could have explored and deployed narrow AI use cases decades earlier, had the conceptual focus been there.

Even at that small scale, you can already do useful things like basic code or text autocompletion, and with a few million parameters on a machine like a Cray Y-MP, you could reasonably attempt tasks like summarizing structured or technical documentation. It's constrained in scope, granted, but it's a solid proof of concept.

The fact that a functioning language model runs at all on a Pentium II, with resources not far off from a 1982 Cray X-MP, is the whole point: we weren’t held back by hardware, we were held back by ideas.

[alganet]

> we weren’t held back by hardware

Llama 3 8B took 1.3M hours to train in a H100-80GB.

Of course, it didn't took 1.3M hours (~150 years). So, many machines with 80GB were used.

Let's do some napkin math. 150 machines with a total of 12TB VRAM for a year.

So, what would be needed to train a 300K parameter model that runs on 128MB RAM? Definitely more, much more than 128MB RAM.

Llama 3 runs on 16GB VRAM. Let's imagine that's our Pentium II of today. You need at least 750 times what is needed to run it in order to train it. So, you would have needed ~100GB RAM back then, running for a full year, to get that 300K model.

How many computers with 100GB+ RAM do you think existed in 1997?

Also, I only did RAM. You also need raw processing power and massive amounts of training data.

[rahen]

You’re basically arguing that because A380s need millions of liters of fuel and a 4km runway, the Wright Flyer was impossible in 1903. That logic just doesn’t hold. Different goals, different scales, different assumptions. The 300K model shows that even in the 80s, it was both possible and sufficient for narrow but genuinely useful tasks.

We simply weren’t looking, blinded by symbolic programming and expert systems. This could have been a wake-up call, steering AI research in a completely different direction and accelerating progress by decades. That’s the whole point.

[fentonc]

I think a quad-CPU X-MP is probably the first computer that could have run (not train!) a reasonably impressive LLM if you could magically transport one back in time. It supported a 4GB (512 MWord) SRAM-based "Solid State Drive" with a supported transfer bandwidth of 2 GB/s, and about 800 MFLOPS CPU performance on something like a big matmul. You could probably run a 7B parameter model with 4-bit quantization on it with careful programming, and get a token every couple seconds.

[rahen]

This sounds plausible and fascinating. Let’s see what it would have taken to train a model as well.

Given an estimate of 6 FLOPs per token per parameter, training a 7B parameter model would require about 1.26×10^22 FLOPs. That translates to roughly 500 000 years on an 800 MFLOPS X-MP, far too long to be feasible. Training a 100M parameter model would still take nearly 70 years.

However, a 7M-parameter model would only have required about six months of training, and a 14M one about a year, so let’s settle on 10 million. That’s already far more reasonable than the 300K model I mentioned earlier.

Moreover, a 10M parameter model would have been far from useless. It could have performed decent summarization, categorization, basic code autocompletion, and even powered a simple chatbot with a short context, all that in 1984, which would have been pure sci-fi back in those days. And pretty snappy too, maybe around 10 tokens per second if not a little more.

Too bad we lacked the datasets and the concepts...