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Or you can build a DistilBERT model and get your egregiously inefficient 2 seconds down to tens of milliseconds.

If you have to classify user input as they’re inputting it to provide a response — so it can’t be batched - 2 seconds could potentially be really slow.

Though LLMs sure have made creating training data to train old school models for those cases a lot easier.

Yeah, that’s what I do: use LLM to help make training data for small models. It’s ao much more efficient, fast, and ergo, scalable.

Yes and no. Having used these tools extensively I think it will be some time before LLMs are truly performant. Even smaller models can't be compared to running optimized code with efficient data structures. And smaller models (in general) do reduce the quality of your results in most cases. Maybe LLMs will kill off NLP and other pursuits pretty soon, but at the moment, each have their tradeoffs.

Correct me if I'm wrong, but, if you run multiple inferences at the same time on the same GPU you will need load multiple models in the vram and the models will fight for resources right? So running 10 parallel inferences will slow everything down 5 times right? Or am I missing something?

No, the key is to use the full context window so you structure the prompt as something like: For each line below, repeat the line, add a comma then output whether it most closely represents a product or service:

20 bottles of ferric chloride

salesforce

...

Appreciate the concrete advice in this response. Thank you.

Inference for single example is memory bound. By doing batch inference, you can interleave computation with memory loads, without losing much speed (up until you cross the compute bound threshold).

You will most likely be using the same model so just 1 to load into vram.

even more naive way - just club several requests into batch of classification requests into one prompt. in practice, this is not production-ready as the llm output does not always contain results for the same number of input (sometimes more than inputted even!)

two seconds is a VERY VERY VERY long time. That is mind-bogglingly, insanely slow.

At 2s per query for 10m entries, that's 251 days to run through the database.

FFS... "Lots of writers, few readers". Read again and do the math: 2 seconds, multiply that by 10 million records which contain this, as well as "alarm installation in two locations" and a whole bunch of other crap with little to no repetition (<2%) and where does that get you? 2 * 10,000,000 = 20,000,000 SECONDS!!!! A day has 86,400 seconds (24 * 3600 = 86,400). The data pipeline needs to finish in <24 hours. Everyone needs to get this into their heads somehow: LLM's are not a silver bullet. They will not cure cancer anytime soon, nor will they be effective or cheap enough to run at massive scale. And I don't mean cheap as in "oh, just get openai subscription hurr durr". Throwing money mindlessly into something is never an effective way to solve a problem.

Why are you using 2 seconds? The commenter you are responding to hypothesized being able to do 250/s based on "100 parallel inference at 5 at a time". Not speaking to the validity of that, but find it strange that you ran with the 2 seconds number after seemingly having stopped reading after that line, while yourself lamenting people don't read and telling them to "read again".

OP said 2 seconds as if that wasn't an eternity...

But then they said 250/second when running multiple inference? Again I don't know if their assertions about running multiple inference are correct but why focus on the wrong number instead of addressing the actual claim?

250/s is still nothing when compared to an actual NLP pipeline that takes a few ms per it, because you can parallelize that too.

I know it's hard to understand, but you can achieve a throughput that is a few orders of magnitude higher.

250/s is few (4) ms per it

Ok, let me dumb it down for you: you have a cockroach in your bathroom and you want to kill it. You have an RPG and you have a slipper. Are you gonna use the RPG or are you going to use the slipper? Even if your bathroom is fine after getting shot with an RPG somehow, isn't this an overkill? If you can code and binary classifier train a classifier in 2 hours that uses nearly 0 resources and gives you good enough results(in my case way above what my targets were) without having to use a ton of resources, libraries, rags, hardware and hell, even electricity? I mean how hard is this to comprehend really?

https://deviq.com/antipatterns/shiny-toy

Sure, but this doesn't answer my question nor tie into your last comment at all. It's Saturday evening in much of the world, are you sober?

This thread is chock full of people who have no clue about what traditional AI even is. I'm sorry you have to deal with literal children

Assuming the 10M records is ~2000M input tokens + 200M output tokens, this would cost $300 to classify using llama-3.3-70b[1]. If using llama lets you do this in say one day instead of two days for a traditional NLP pipeline, it's worthwhile.

[1]: https://openrouter.ai/meta-llama/llama-3.3-70b-instruct

> ...two days for a traditional NLP pipeline

Why 2 days? Machine Learning took over the NLP space 10-15 years ago, so the comparison is between small, performant task-specific models versus LLMs. There is no reason to believe the "traditional" NLP pipelines are inherently slower than Large Language Models, and they aren't.

my claim is not that it would take two days for such a pipeline to run but that it would take two days to make an NLP pipeline whereas an LLM pipeline would be faster to make.