> applying this compression algorithm at scale may significantly relax the memory bottleneck issue.
I don’t think they’re going to downsize though, I think the big players are just going to use the freed up memory for more workflows or larger models because the big players want to scale up. It’s a cat and mouse race for the best models.
It's also less frustrating to organize world wide ram production and logistics than to deal with a single mathematician.
Constantly sitting around trying to solve problems that nobody has made headway on for hundreds of years. Or inventing theorems around 15th century mysticism that won't be applicable for hundreds of years.
Now if you'll excuse me I need to multiply some numbers by 3 and divide them by 2 ... I'm so close guys.
I don't know, I think if you weighed up the costs of AI related datacentre spend vs. the average mathematics academic's salary you could come to a different conclusion.
Compute, bytes of ram used, bytes in model, bytes accessed per iteration, bytes of data used for training.
You can trade the balance if you can find another way to do things, extreme quantisation is but one direction to try. KANs were aiming for more compute and fewer parameters. The recent optimisation project have been pushing at these various properties. Sometimes gains in one comes at the cost of another, but that needn't always be the case.
Ive thought for a while that the real gains now will not come from throwing more hardware at the problem, but advances in mathematical techniques to make things for more efficient.
> If I were Google, I wouldn’t release research that exposes a competitive advantage.
Isn't that a classic tit for tat decision and head for a loss?
Excellence and prestige are valuable too. You get those expensive ML for a small discount, public/professional perception, etc. Considering the public communication from Google, that isn't complete sociopathic, they know this war isn't won in one night, they are the only sustainably funded company in the competition. Surely they are at risk with their business, but can either go rampant or focus. They decided to focus.
Doesn't seem relevant here. TurboQuant isn't a domain-specific technique like the BL is talking about, it's a general optimisation for transformers that helps leverage computation more effectively.
We will not see memory demand decrease because this will simply allow AI companies to run more instances. They still want an infinite amount of memory at the moment, no matter how AI improves.
The hyperscalers do not want us running models at the edge and they will spend infinite amounts of circular fake money to ensure hardware remains prohibitively expensive forever.
> and they will spend infinite amounts of circular fake money to ensure hardware remains prohibitively expensive forever.
That's ridiculous, "infinite money" isn't a thing. They will spend as much as they can not because they want to keep local solutions out, but because it enables them to provide cheaper services and capture more of the market. We all eventually benefit from that.
Oh it gets worse than that, the money which caused all of this by OpenAI was taken from Japanese banks at cheap interest rates (by softbank for the stargate project), and the Japanese Banks are able to do it because of Japanese people/Japanese companies and also the collateral are stocks which are inflated by the value of people who invest their hard earned money into the markets
So in a way they are using real hard earned money to fund all of this, they are using your money to basically attack you behind your backs.
But what if it becomes "good enough", that for most intents and purposes, small models can be "good enough"
There are some people here/on r/localllama who I have seen run some small models and sometimes even run multiple of them to solve/iterate quickly and have a larger model plug into it and fix anything remaining.
This would still mean that larger/SOTA models might have some demand but I don't think that the demand would be nearly enough that people think, I mean, we all still kind of feel like there are different models which are good for different tasks and a good recommendation is to benchmark different models for your own use cases as sometimes there are some small models who can be good within your particular domain worth having within your toolset.
Because the true goal is AGI, not just nice little tools to solve subsets of problems. The first company which can achieve human level intelligence will just be able to self-improve at such a rate as to create a gigantic moat
I don't think we are there yet. Models running in data centers will still be noticeably better as efficiency will allow them to build and run better models.
Not many people would like today models comparable to what was SOTA 2 years ago.
To run models locally and have results as good as the models running in data centers we need both efficiency and to hit a wall in AI improvement.
None of those two conditions seem to become true for the near future.
I like the mainframe comparison but isn't there a key difference? Mainframes died because hardware got cheap -- that's predictable. LLM efficiency improving enough to run locally needs algorithmic breakthroughs, which... aren't. My gut says we'll end up with a split. Stuff where latency matters (copilot, local agents) moves to edge once models actually fit on a laptop. But training and big context windows stay in the cloud because that's where the data lives. One thing I keep going back and forth on: is MoE "better math" or just "better engineering"? Feels like that distinction matters a lot for where this all goes.
Citation needed. I've heard this quite often, but so far, I haven't seen proof of the stated causality.
PS: This doesn't mean that better public transportation could deliver more bang for the buck than the n-th additional car lane. But never ever have I heard from anybody that they chose to buy a car or use an existing car more often because an additional lane has been built.
> applying this compression algorithm at scale may significantly relax the memory bottleneck issue.
I don’t think they’re going to downsize though, I think the big players are just going to use the freed up memory for more workflows or larger models because the big players want to scale up. It’s a cat and mouse race for the best models.
Known in the business as 'pulling a jevons'
Despite the shortage, RAM is still cheaper than mathematicians.
It's also less frustrating to organize world wide ram production and logistics than to deal with a single mathematician.
Constantly sitting around trying to solve problems that nobody has made headway on for hundreds of years. Or inventing theorems around 15th century mysticism that won't be applicable for hundreds of years.
Now if you'll excuse me I need to multiply some numbers by 3 and divide them by 2 ... I'm so close guys.
I don't know, I think if you weighed up the costs of AI related datacentre spend vs. the average mathematics academic's salary you could come to a different conclusion.
Doubt it. You have to pay these mathematicians once and then you can deploy to millions of sites.
But not everyone has to pay mathematicians, like RAM :-)
At the same time, processing is much cheaper than memory
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This is one of the basic avenues for advancement.
Compute, bytes of ram used, bytes in model, bytes accessed per iteration, bytes of data used for training.
You can trade the balance if you can find another way to do things, extreme quantisation is but one direction to try. KANs were aiming for more compute and fewer parameters. The recent optimisation project have been pushing at these various properties. Sometimes gains in one comes at the cost of another, but that needn't always be the case.
The drop in memory stocks seems counterintuitive to me.
The demand for memory isn't going to go down, we'll just be able to do more with the same amount of memory.
The same could be said about other IT domain... When you see single webpages that weight by tens of MB you wonder how we came to this.
Detachment from reality. Code elegance is more important then anything else. As simple as that.
Ive thought for a while that the real gains now will not come from throwing more hardware at the problem, but advances in mathematical techniques to make things for more efficient.
> If I were Google, I wouldn’t release research that exposes a competitive advantage.
Isn't that a classic tit for tat decision and head for a loss?
Excellence and prestige are valuable too. You get those expensive ML for a small discount, public/professional perception, etc. Considering the public communication from Google, that isn't complete sociopathic, they know this war isn't won in one night, they are the only sustainably funded company in the competition. Surely they are at risk with their business, but can either go rampant or focus. They decided to focus.
Sigh. Don't make me tap the sign [1]
[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Doesn't seem relevant here. TurboQuant isn't a domain-specific technique like the BL is talking about, it's a general optimisation for transformers that helps leverage computation more effectively.
We will not see memory demand decrease because this will simply allow AI companies to run more instances. They still want an infinite amount of memory at the moment, no matter how AI improves.
If models become more efficient we will move more of the work to local devices instead of using SaaS models. We’re still in the mainframe era of LLM.
I don't see how we'll ever get to widespread local LLM.
The power efficiency alone is a strong enough pressure to use centralized model providers.
My 3090 running 24b or 32b models is fun, but I know I'm paying way more per token in electricity, on top of lower quality tokens.
It's fun to run them locally, but for anything actually useful it's cheaper to just pay API prices currently.
The hyperscalers do not want us running models at the edge and they will spend infinite amounts of circular fake money to ensure hardware remains prohibitively expensive forever.
> they will spend infinite amounts of circular fake money > forever
If that's the plan (there is no plan) then it expires at some point, because it's a spiral and such spirals always bottom out.
And when that happens people STILL won’t be able to afford the hardware.
> and they will spend infinite amounts of circular fake money to ensure hardware remains prohibitively expensive forever.
That's ridiculous, "infinite money" isn't a thing. They will spend as much as they can not because they want to keep local solutions out, but because it enables them to provide cheaper services and capture more of the market. We all eventually benefit from that.
> of circular fake money
Oh it gets worse than that, the money which caused all of this by OpenAI was taken from Japanese banks at cheap interest rates (by softbank for the stargate project), and the Japanese Banks are able to do it because of Japanese people/Japanese companies and also the collateral are stocks which are inflated by the value of people who invest their hard earned money into the markets
So in a way they are using real hard earned money to fund all of this, they are using your money to basically attack you behind your backs.
I once wrote an really long comment about the shaky finances of stargate, I feel like suggesting it here: https://news.ycombinator.com/item?id=47297428
> If models become more efficient
Then we can make them even bigger.
> Then we can make them even bigger.
But what if it becomes "good enough", that for most intents and purposes, small models can be "good enough"
There are some people here/on r/localllama who I have seen run some small models and sometimes even run multiple of them to solve/iterate quickly and have a larger model plug into it and fix anything remaining.
This would still mean that larger/SOTA models might have some demand but I don't think that the demand would be nearly enough that people think, I mean, we all still kind of feel like there are different models which are good for different tasks and a good recommendation is to benchmark different models for your own use cases as sometimes there are some small models who can be good within your particular domain worth having within your toolset.
> But what if it becomes "good enough", that for most intents and purposes, small models can be "good enough"
It's simple: then we'll make our intents and purposes bigger.
Because the true goal is AGI, not just nice little tools to solve subsets of problems. The first company which can achieve human level intelligence will just be able to self-improve at such a rate as to create a gigantic moat
I don't think we are there yet. Models running in data centers will still be noticeably better as efficiency will allow them to build and run better models.
Not many people would like today models comparable to what was SOTA 2 years ago.
To run models locally and have results as good as the models running in data centers we need both efficiency and to hit a wall in AI improvement.
None of those two conditions seem to become true for the near future.
I like the mainframe comparison but isn't there a key difference? Mainframes died because hardware got cheap -- that's predictable. LLM efficiency improving enough to run locally needs algorithmic breakthroughs, which... aren't. My gut says we'll end up with a split. Stuff where latency matters (copilot, local agents) moves to edge once models actually fit on a laptop. But training and big context windows stay in the cloud because that's where the data lives. One thing I keep going back and forth on: is MoE "better math" or just "better engineering"? Feels like that distinction matters a lot for where this all goes.
I disagree. I think a sharp drop in memory requirements of at least an order of magnitude will cause demand to adjust accordingly.
Department of Transportation always thinks adding more lanes will reduce traffic.
It doesn't, it induces demand. Why? Because there's always too many people with cars who will fill those lanes.
Citation needed. I've heard this quite often, but so far, I haven't seen proof of the stated causality.
PS: This doesn't mean that better public transportation could deliver more bang for the buck than the n-th additional car lane. But never ever have I heard from anybody that they chose to buy a car or use an existing car more often because an additional lane has been built.
Have you tried the "Reference" section on the Wikipedia article?
https://en.wikipedia.org/wiki/Induced_demand#cite_note-vande...
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Jevons paradox https://en.wikipedia.org/wiki/Jevons_paradox
Can we say something about the compression factor for pure knowledge of these models?