It’s the generation speed. Internally LLMs use tokens which represent either words or parts of words and map them to integer values. The model then does it’s prediction on which integer is most likely to come after the input. How the words are split up is an implementation detail that can vary from model to model
Comment on DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI
Linktank@lemmy.today 1 week ago
Okay, can somebody who knows about this stuff please explain what the hell a “token per second” means?
fluffykittycat@slrpnk.net 1 week ago
IrritableOcelot@beehaw.org 1 week ago
Not somebody who knows a lot about this stuff, as I’m a bit of an AI Luddite, but I know just enough to answer this!
“Tokens” are essentially just a unit of work – instead of interacting directly with the user’s input, the model first “tokenizes” the user’s input, simplifying it down into a unit which the actual ML model can process more efficiently. The model then spits out a token or series of tokens as a response, which are then expanded back into text or whatever the output of the model is.
I think tokens are used because most models use them, and use them in a similar way, so they’re the lowest-level common unit of work where you can compare across devices and models.
Mniot@programming.dev 1 week ago
Not an answer to your question, but I thought this was a nice article for getting some basic grounding on the new AI stuff: arstechnica.com/…/a-jargon-free-explanation-of-ho…
IndeterminateName@beehaw.org 1 week ago
A bit like a syllable when you are talking about text based responses. 20 tokens a second is faster than most people could read the output so that’s sufficient for a real time feeling “chat”.
SteevyT@beehaw.org 1 week ago
Huh, yeah that actually is above my reading speed assuming 1 token = 1 word. Although, I found that anything above 100 words per minute, while slow to read, feels real time to me since that’s about the absolute top end of what most people type.