>appears slowly as models scale up?"

Both, I think, based on limited tinkering with smaller models.

I've been using GPT4ALL and oobabooga to make testing models easier on my single (entry-level discrete GPU) machine. Using GGML versions of llama models, I get drastically different results.

With a 7B parameter model I mostly-- not always-- get an on topic and somewhat coherent response. By which I mean, if I start off with "Are you ready to answer questions?" it will say "Yes and blah blah blah..." for a paragraph about something random. On a specific task it will perform a bit better: my benchmark request has been to ask for a haiku. It was confused, classified haikus as a form of gift, but when pushed it would output something resembling a poem but not a haiku.

Then I try a 13B model. It's a lot better at answering a simple question like "are you ready?" but will still sometimes say yes and then give a random dialogue as if it's creating a story where someone asks it to do something. It will readily create a poem on first attempts, though still not a haiku in any way. If I go through about a dozen rounds of asking it what a haiku is and then, in subsequent responses, "reminding it" to stay on course for those definitions, it will kind of get it and give me 4 or 5 short lines.

A 30B model answers simple questions and follow simply instructions fairly easily. It will produce something resembling a haiku, though often with an extra line and a few extra syllables, with minimal prodding an prompt engineering.

None of the above, at least the versions I've tried (variations & advances are coming daily) have a very good memory. The clearly have some knowledge of past context but mostly ignore it when it comes to keeping responses logically consistent across multiple prompts. I can ask it "what was my first prompt?" and get a correct response, But when I tell it to respond as if it's name is "Bob" then a few prompts later it's calling me Bob and back to labelling itself an AI assistant.

Then there's the 65B parameter model. I think this is a big leap. I'm not sure though, my PC can barely run the 30B model and gets maybe 1 token every 3 seconds on 30B. The 65B model I have to let use disk swap space or it won't work at all, and it produces roughly 1 token per 2-3 minutes. It's also much more verbose, reiterating my request and agreeing to it before it proceeds, so that adds a lot of time. However, a simple insistence on a "Yes/No" answer will succeed. A request for a Haiku succeeds on the first try, with nearly the correct syllable count too, using an extra few syllables in trying to produce something on the topic I specify. This is commensurate with what I get with normal ChatGPT, which has > 150B parameters that aren't even quantized.

However I have yet to explore the 65B parameter model in any detail. 1 token every 2-3 minutes, sucking up all system resources, makes things very slow going, so I haven't done much more than what I described.

Apart from these, I was just playing around with the 13B model a few hours ago and it did do a very decent job at producing basic SQL code I asked it to produce against a single table. Max value for the table, max value per a specified dimension, etc. It did this across multiple prompts without much need to "remind" it about anything a few prompts earlier. At that point though I was all LLM burned out for the day (I'd been fiddling for hours) so I didn't get around to asking it for simple joins.

So in short, where I began, I think its both. Abilities are somewhat task specific, as are the quality improvements for a given task across larger parameter models. Sometimes a specific task has moderate or little improvement at higher levels, sometimes another task does much better, or only does much better when it reaches a certain point: e.g., haikus from 13B to 30B weren't a great improvement, but 30B to 65B was an enormous improvement.*