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Comment by gtirloni | Hacker News Reader

I'm on paid (rich, I know) and the performance is all over the place. Sometimes it'll spit out a whole paragraph almost instantly and other times it's like I'm back to my 2400bps modem.

I haven't noticed prompt size having an impact jut I'll test that.

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This reflects my experience. Sometimes I'll provide a single sentence (to GPT-4 with the largest context window) and it will slowly type out 3 or so words every 5 seconds, and in other cases I'll give it a massive prompts and it returns data extremely fast. This is also true of smaller context window models. There seems to be no way to predict the performance.

Oh hey... leep an eye on your CPU load. The problem might be on the near end. In my case on a slower machine it slows down if you're dealing with a very long chat.

(DO report this as a bug if so)

I think that's not the issue here but I do notice the browser going crazy after a while of chatting with ChatGPT. The tab seems to consume a baseline CPU while doing nothing. I just brush it off and close it... bad JavaScript maybe. I should look into this and report as a bug, thanks for the advice.

This is basically how I respond to requests myself. Sometimes a single short sentence will cause me to slowly spit out a few words. Other times I can respond instantly to paragraphs of technical information with high accuracy and detailed explanations. There seems to be no way to predict my performance.

Early on, I noticed that if I ask ChatGPT an unique question that might not have been asked before, it'll split out a response slowly, but repeating the same question would result in a much quicker response.

Is it possible that you have a caching system too so that you are able to respond instantly with paragraphs of technical information to some types of requests that you have seen before?

Yes, search for LLM caching and semantic searches. They must be using something like that.

I cannot tell if this comment was made in just or in earnest.

As far as I understand, the earlier GPT generations required a fixed amount of compute per token inferred.

But given the tremendous load on their systems, I wouldn’t be surprised if OpenAI is playing games with running a smaller model when they predict they can get away with it. (Is there evidence for this?)

I'm guessing there are so many other impacts of own on the model that size of print probably gets lost. I can see a future where people are forecasting updates to ChatGPT like we do with the weather.

Yeah. It has so many moving parts that I doubt anyone can make a science out of it, but people will try for sure. Just like with most psycology/social experiments and SEO. I'm flooded with prompt engineering course spam these days.

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