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Continuous batching to increase LLM inference throughput and reduce p50 latency

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Re: Continuous batching to increase LLM inference throughput and reduce p50 latency

#11
It's very hard to filter blatant self-promotion and marketing jargon from actual innovation (which goes at a much slower pace) these days.

The speed people are just creating wrappers or minor changes and using words like "disruptive", "game-changing", "democratising " just feel so inflated and boring at this point.

I hope this gets very soon into the next phase of the hype cycle[1], so the we can talk about something else. [1] https://en.wikipedia.org/wiki/Gartner_hype_cycle

Re: Continuous batching to increase LLM inference throughput and reduce p50 latency

#14

It's very hard to filter blatant self-promotion and marketing jargon from actual innovation (which goes at a much slower pace) these days. The speed people are just creating wrappers or minor changes and using words like "disruptive", "game-changing", "democratising " just feel so inflated and boring at this point. I hope this gets very soon into the next phase of the hype cycle[1], so the we can talk about something…

[deleted]

Re: Continuous batching to increase LLM inference throughput and reduce p50 latency

#19
post #3

I was a bit surprised by this: > For example, suppose you prompt with a sentence "What is the capital of California: ", it would take ten forward pass iterations to get back the full response of ["S", "a", "c", "r", “a”, "m", "e", "n", "t", "o"] From the content I have been reading trying to understand LLMs, I thought that the output was a token and not a string of chars. What am I missing here?

A number of submissions lately have "simplified" and presented every character as a token. This has only confused many readers.

LLMs use a vocabulary of statistically chosen tokens. GPT 3 vocab, for instance, splits Sacramento into three tokens-

Sac - 38318

rament - 15141

o - 78

There is a rule of thumb that about every four letters in English text becomes a token but that's just the average.

California is a single token (25284). As is Canada (17940). And so on.

Re: Continuous batching to increase LLM inference throughput and reduce p50 latency

#20

It's very hard to filter blatant self-promotion and marketing jargon from actual innovation (which goes at a much slower pace) these days. The speed people are just creating wrappers or minor changes and using words like "disruptive", "game-changing", "democratising " just feel so inflated and boring at this point. I hope this gets very soon into the next phase of the hype cycle[1], so the we can talk about something…

"The speed people are just creating wrappers or minor changes"

To be fair, the overhead of running LLMs at scale -- the cost per interaction -- is the limiting factor for commercial deployments. Efficiency is an enormous advantage.

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