No, you are limited by:

Compute Performance, you will need 10,000%+ more compute than was available per chip, and those PCIe accelerators don’t have the ability to compute the way they do now. You are going to have to rely on CPUs which is worse

Lack of scalabality of interconnecting chips to behave as one, increasing IO requirements dramatically.

Lack of memory pooling (yes you qualified it), memory bandwidth and memory sizes (we are talking in megabytes), imagine waiting for 1 billion parameter model calculations to load and store in each layer of a neural network using floppy disks.

The fastest computer in 1985 was the CRAY-2 supercomputer at 1.9 gigaflops. ChatGPT 3 can be trained on 1024 A100 GPUs in 34 days*. An A100 outputs 312 teraflops. So no, I don’t think it can be done in 1985 if given the entire year. There’s also storage for incoming digital texts for training - the input data didn’t exist back then, not to the capacity. I don’t think it could be done in a reasonable time.

• https://www.assemblyai.com/blog/how-to-train-large-deep-learning-models-as-a-startup/

It’d be easier to build a suit in a cave, with a box of scraps.

It wouldn’t. Training the neural nets for LLMs are all about brute force, and it’s only been possible in the last few years to train these models without spending billions. Even going back to 2010 I think it’d be largely infeasible.

The good news is if we fast forward even just a few years, training will become relatively cheap compared to today.

cant even adress so much memory on 16 bit systems :)

The growth rate in computing has been exponential. Using the fastest available computer from 1985 continuously for 38 years and still going, you would be passed by a quad GPU-based server in hours.

Do you mean one computer from 1985? No. There is no computer from that year that had enough RAM. If you mean all the computers from 1985, working together, then yes. You only need sufficient RAM, a Turing-complete machine, and probably some centuries to do it.