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Comment by aeve890 | Hacker News Reader

>Random one in terms of applications: getting GPT-4-level[0] LLMs to operate at hundreds of tokens per second on edge hardware

That's low hanging for you?

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If we throw in hardware dedicated to a specific LLM, it seems to be a rather low hanging fruit. Especially considering that this is already happening for vision models [1].

[1]: "FPGA-based CNN Acceleration using Pattern-Aware Pruning" https://inria.hal.science/hal-04689673/document

> hardware dedicated to a specific LLM

That wording screams "Taalas". Which, importantly, is not the only player trying to abate the distance between data and arithmetics...

Maybe they meant in the sense of “untapped potential”, because so far a lot of the focus has been on increasing model capabilities, not necessarily performance/power budget.

Yes. Point is, it's untapped only because everyone is running in the race (even if out of curiosity), and there's just not enough people with means to tap into these side threads. For the past few years, there's been many interesting papers that circulated the industry, got recognized as worthwhile pursuits, and then dropped because running behind the Big Vendors had massively better ROI.

Yes. It's well within realm of possibility, but so far wasn't pursued because the Big Vendors went all-in into capability growth (rightfully testing "the bitter lesson" to its limits) and got themselves stuck in an arms race, while everyone else is barely keeping up and/or starstruck with fascination, exploring what these models can do.

This got everyone racing forward and right now there is not enough human attention left in the world to productionize this, or any of the other "side threads". When the race slows down, people will catch up, branch out, and loop back.

Just like renewable energy and so many other things. Hyperconcentration of capital is really tragic. I hope things turn around.

They will. That's the fallacy of the "S-curve" everyone likes to commit these days actually giving a positive outlook.

Assuming it won't get to full RSI, the current approach will burn out - most likely economically. The race slows down, people branch out, look back, start picking up the "untapped potential"/low-hanging fruits, and you have new S-curves launching in place of the one that just tapered off (hence a fallacy - a stack of S-curves adds up to continuing exponential growth).

In other words: it comes and goes. Hyperconcentrated capital will eventually deconcentrate.

We already have examples of LLMs running 16k+ tokens a second using custom ASICs.

It's down at the moment (Not sure if it'll return?) but Chat Jimmy[0] produced by Taalas[1] was powered by an ASIC running Llama 3.1 8B, and hitting 17,000 tokens/sec. It was amazing to use, you'd no sooner have hit enter than you had a full response back. I actually found its speed to be a problem for interactive stuff, as every answer got several paragraphs I'd then wade through, vs a model populating text closer to my reading speed.

I appreciate, there are differences between an 8 billion parameter model and something GPT-4-ish, but we're currently in the middle of a race between a half dozen or so companies to produce the next best frontier model, which requires their infrastructure to be dynamic.

We really don't always need newer better faster stronger models, there's quite a lot of room for "good enough" where getting 17kt/s at significantly lower power would be amazing.

[0] https://chatjimmy.ai/ [1] https://taalas.com/

>I actually found its speed to be a problem for interactive stuff, as every answer got several paragraphs I'd then wade through, vs a model populating text closer to my reading speed.

It would be interesting to pair the super fast model with a normal speed model. Have the super fast one do all the background research, code writing, etc. The normal model would just relay the needed info to you at a more reasonable pace.

Why do you even need a second model for that? There are many simpler ways to slow down the rate that the near-immediate response is pseudo-typed onto the screen.

It's likely quite close. There are so many papers proving concepts that would bring this, they just haven't been combined in production.

> That's low hanging for you

An important part of the industry is studying that: it is built-up effort. Sooner or later, the fruits will be harvested. The targeted preparation has been there for years now.

That's a high hanging fruit, not low.

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