How large are large language models?

All digitized books ever written/encoded compress to a few TB. The public web is ~50TB. I think a usable zip of all english electronic text publicly available would be on O(100TB). So we're at about 1% of that in model size, and we're in a diminishing-returns area of training -- ie., going to >1% has not yielded improvements (cf. gpt4.5 vs 4o).

This is why compute spend is moving to inference time with "reasoning" models. It's likely we're close to diminshing returns on inference-time compute now too, hence agents whereby (mostly,) deterministic tools are supplementing information /capability into the system.

I think to get any more value out of this model class, we'll be looking at domain-specific specialisation beyond instruction fine-tuning.

I'd guess targeting 1TB inference-time VRAM would be a reasonable medium-term target for high quality open source models -- that's within the reach of most SMEs today. That's about 250bn params.

Where you getting these numbers from? Interested to see how that's calculated.

I read somewhere, but cannot find the source anymore, that all written text prior to this century was approx 50MB. (Might be misquoted as don't have source anymore).

50 MB feels too low, unless the quote meant text up until the 20th century, in which case it feels much more believable. In terms of text production and publishing, we're still riding an exponent, so a couple orders of magnitude increase between 1899 and 2025 is not surprising.

(Talking about S-curves is all the hotness these days, but I feel it's usually a way to avoid understanding what exponential growth means - if one assumes we're past the inflection point, one can wave their hands and pretend the change is linear, and continue to not understand it.)