5 ms·

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 mo

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.

jurgenburgen 7mo ago
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.
- DeathArrow 7mo ago
  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.
- ssyhape 7mo ago
  [flagged]
  - lucasfin000 7mo ago
    MoE feels a lot more like engineering to me. You're routing around the problem rather than actually solving it. The real math gains are things like quantization schemes that change how information is actually represented. Whether that distinction matters long term probably will depend on whether we hit a capability wall first or an efficiency ceiling first.
- throwatdem12311 7mo ago
  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.
  - Imustaskforhelp 7mo ago
    > 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 https://news.ycombinator.com/item?id=47297428
    - joquarky 7mo ago
      What is the difference between "hard earned" and not?
      - Imustaskforhelp 7mo ago
        Well cartel money for example, depends on the definition of hard earned but I don't quite imagine for example the japanese Yakuza to deposit into banks/stock markets for example, I am not sure but I imagine something like gold/cash being used. Maybe you can argue that yakuza is making hard earned money but imo, they are doing illegal activities within the law and are doing something more closer to extortion. Ironically, in a sense, what AI did in a sense is also an extortion. One is just legal (barely, I am not even sure how or why), the other isn't. That was my intention to highlight when I said hard earned money.
  - topspin 7mo ago
    > 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.
    - throwatdem12311 7mo ago
- Ray20 7mo ago
  > If models become more efficient Then we can make them even bigger.
  - Imustaskforhelp 7mo ago
    > 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.
    - Almondsetat 7mo ago
      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
      - 9rx 7mo ago
        > The first company which can achieve human level intelligence will just be able to... They say prostitution is the oldest industry of all. We know how to achieve human-level intelligence quite well. The outstanding challenge is figuring out how to produce an energy efficient human-level intelligence.
      - Dylan16807 7mo ago
        There's no particular reason to assume a human level AI would be able to improve itself any better than the thousands of human level humans that designed it.
- mustyoshi 7mo ago
  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.
  - singpolyma3 7mo ago
    Until you put up your solar and then power is almost free...
    - vidarh 7mo ago
      The amortised cost including the panels and labour is nowhere near "almost free".
      - boredatoms 7mo ago
        It is over a couple of years
        vidarh 7mo ago
        Even if you live somewhere where it does, that is not remotely "almost free", and lots of places the payback period is more in the range of 10-15 years even with subsidies.
  - leptons 7mo ago
    AI is not cheap to run no matter where it is running. The price we get charged today for AI is a loss-leader. The actual cost is much higher, so much higher that the average paying user today would balk at what it actually costs to run. These AI companies are trying to get people hooked on their product, to get it integrated into every business and workflow that they can, then start raising prices.
- delecti 7mo ago
  As I understand this advancement, this doesn't let you run bigger models, it lets you maintain more chat context. So Anthropic and OpenAI won't need as much hardware running inference to serve their users, but it doesn't do much to make bigger models work on smaller hardware. Though I'm not an expert, maybe my understanding of the memory allocation is wrong.
  - dd8601fn 7mo ago
    Seems to me if the model and the kv cache are competing for the same pool of memory, then massively compressing the cache necessarily means more ram available for (if it fits) a larger model, no?
    - delecti 7mo ago
      Yes, but the context is a comparatively smaller part of how much memory is used when running it locally for a single user, vs when running it on a server for public... serving.
- rainsford 7mo ago
  We moved from the mainframe era to desktops and smaller servers because computers got fast enough to do what we needed them to do locally. Centralized computing resources are still vastly more powerful than what's under your desk or in a laptop, but it doesn't matter because people generally don't need that much power for their daily tasks. The problem with AI is that it's not obvious what the upper limit of capability demand might be. And until or if we get there, there will always be demand for the more capable models that run on centralized computing resources. Even if at some point I'm able to run a model on my local desktop that's equivalent to current Claude Opus, if what Anthropic is offering as a service is significantly better in a way that matters to my use case, I will still want to use the SaaS one.
  - lelanthran 7mo ago
    > Even if at some point I'm able to run a model on my local desktop that's equivalent to current Claude Opus, if what Anthropic is offering as a service is significantly better in a way that matters to my use case, I will still want to use the SaaS one. Only if it's competitively priced. You wouldn't want to use the SaaS if the breakeven in investment on local instances is a matter of months. Right now people are shelling out for Claude Code and similar because for $200/m they can consume $10k/m of tokens. If you were actually paying $10k/m, than it makes sense to splurge $20k-$30k for a local instance.
  - zozbot234 7mo ago
    The underlying advantage of local inference is that you're repurposing your existing hardware for free. You don't need your token spend to pay a share of the capex cost for datacenters that are large enough to draw gigawatts in power, you can just pay for your own energy use. Even though the raw energy cost per operation will probably be higher for local inference, the overall savings in hardware costs can still be quite real.
- acuozzo 7mo ago
  But what about The Jevons Paradox?
redrove 7mo ago
I disagree. I think a sharp drop in memory requirements of at least an order of magnitude will cause demand to adjust accordingly.
- 3yr-i-frew-up 7mo ago
  [dead]
- cyanydeez 7mo ago
  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.
  - nkmnz 7mo ago
    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.
    - j16sdiz 7mo ago
      Have you tried the "Reference" section on the Wikipedia article? https://en.wikipedia.org/wiki/Induced_demand#cite_note-vanderbilt-11 https://en.wikipedia.org/wiki/Induced_demand#cite_note-vande...
    - cyanydeez 7mo ago
      You've never heard anyone choose to take side streets instead of the highway because of traffic jams? No one ever goes out of their way to avoid heavily trafficed areas?
      - nkmnz 7mo ago
        I don't understand what the point is you're trying to make. When people at t0 take detours because of traffic jams on the direct route, and then at t1, there are less traffic jam on the direct route due to additional lanes, so they decide to take the direct route, then total traffic is down, because they no longer take a detour. Even if they are still part of a newly induced traffic jam.
jLaForest 7mo ago
Jevons paradox https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
rainsford 7mo ago
I'm not sure that's infinitely true as long as AI costs to the user are proportional to the cost it takes to run the model. Even if user costs are heavily subsidized by investment, as long as they are non-zero and go up when models cost more, there will be at least some pressure for cheaper models and not just more capable ones and that pressure will go up with costs. AI is a crazy industry, but it's not totally immune to the law of supply and demand. The real question though is how close are we to the point where the pressure is more for efficiency rather than capability. Anecdotally I think it's a ways off. Right now the general vibe I get is that people feel AI is very impressive for how cheap it is to use, which suggests to me that a lot of users would be very willing to pay more for more capable models. So the tipping point where AI hardware demand might slow down seems a ways off.
- LoganDark 7mo ago
  It has yet to be seen when hyperscalers will change their tune.

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.

jurgenburgen 7mo ago

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.

DeathArrow 7mo ago
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.
ssyhape 7mo ago
[flagged]
- lucasfin000 7mo ago
  MoE feels a lot more like engineering to me. You're routing around the problem rather than actually solving it. The real math gains are things like quantization schemes that change how information is actually represented. Whether that distinction matters long term probably will depend on whether we hit a capability wall first or an efficiency ceiling first.
throwatdem12311 7mo ago
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.
- Imustaskforhelp 7mo ago
  > 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 https://news.ycombinator.com/item?id=47297428
  - joquarky 7mo ago
    What is the difference between "hard earned" and not?
    - Imustaskforhelp 7mo ago
      Well cartel money for example, depends on the definition of hard earned but I don't quite imagine for example the japanese Yakuza to deposit into banks/stock markets for example, I am not sure but I imagine something like gold/cash being used. Maybe you can argue that yakuza is making hard earned money but imo, they are doing illegal activities within the law and are doing something more closer to extortion. Ironically, in a sense, what AI did in a sense is also an extortion. One is just legal (barely, I am not even sure how or why), the other isn't. That was my intention to highlight when I said hard earned money.
- topspin 7mo ago
  > 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.
  - throwatdem12311 7mo ago
Ray20 7mo ago
> If models become more efficient Then we can make them even bigger.
- Imustaskforhelp 7mo ago
  > 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.
  - Almondsetat 7mo ago
    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
    - 9rx 7mo ago
      > The first company which can achieve human level intelligence will just be able to... They say prostitution is the oldest industry of all. We know how to achieve human-level intelligence quite well. The outstanding challenge is figuring out how to produce an energy efficient human-level intelligence.
    - Dylan16807 7mo ago
      There's no particular reason to assume a human level AI would be able to improve itself any better than the thousands of human level humans that designed it.
mustyoshi 7mo ago
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.
- singpolyma3 7mo ago
  Until you put up your solar and then power is almost free...
  - vidarh 7mo ago
    The amortised cost including the panels and labour is nowhere near "almost free".
    - boredatoms 7mo ago
      It is over a couple of years
      - vidarh 7mo ago
        Even if you live somewhere where it does, that is not remotely "almost free", and lots of places the payback period is more in the range of 10-15 years even with subsidies.
- leptons 7mo ago
  AI is not cheap to run no matter where it is running. The price we get charged today for AI is a loss-leader. The actual cost is much higher, so much higher that the average paying user today would balk at what it actually costs to run. These AI companies are trying to get people hooked on their product, to get it integrated into every business and workflow that they can, then start raising prices.
delecti 7mo ago
As I understand this advancement, this doesn't let you run bigger models, it lets you maintain more chat context. So Anthropic and OpenAI won't need as much hardware running inference to serve their users, but it doesn't do much to make bigger models work on smaller hardware. Though I'm not an expert, maybe my understanding of the memory allocation is wrong.
- dd8601fn 7mo ago
  Seems to me if the model and the kv cache are competing for the same pool of memory, then massively compressing the cache necessarily means more ram available for (if it fits) a larger model, no?
  - delecti 7mo ago
    Yes, but the context is a comparatively smaller part of how much memory is used when running it locally for a single user, vs when running it on a server for public... serving.
rainsford 7mo ago
We moved from the mainframe era to desktops and smaller servers because computers got fast enough to do what we needed them to do locally. Centralized computing resources are still vastly more powerful than what's under your desk or in a laptop, but it doesn't matter because people generally don't need that much power for their daily tasks. The problem with AI is that it's not obvious what the upper limit of capability demand might be. And until or if we get there, there will always be demand for the more capable models that run on centralized computing resources. Even if at some point I'm able to run a model on my local desktop that's equivalent to current Claude Opus, if what Anthropic is offering as a service is significantly better in a way that matters to my use case, I will still want to use the SaaS one.
- lelanthran 7mo ago
  > Even if at some point I'm able to run a model on my local desktop that's equivalent to current Claude Opus, if what Anthropic is offering as a service is significantly better in a way that matters to my use case, I will still want to use the SaaS one. Only if it's competitively priced. You wouldn't want to use the SaaS if the breakeven in investment on local instances is a matter of months. Right now people are shelling out for Claude Code and similar because for $200/m they can consume $10k/m of tokens. If you were actually paying $10k/m, than it makes sense to splurge $20k-$30k for a local instance.
- zozbot234 7mo ago
  The underlying advantage of local inference is that you're repurposing your existing hardware for free. You don't need your token spend to pay a share of the capex cost for datacenters that are large enough to draw gigawatts in power, you can just pay for your own energy use. Even though the raw energy cost per operation will probably be higher for local inference, the overall savings in hardware costs can still be quite real.
acuozzo 7mo ago
But what about The Jevons Paradox?

redrove 7mo ago

I disagree. I think a sharp drop in memory requirements of at least an order of magnitude will cause demand to adjust accordingly.

3yr-i-frew-up 7mo ago
[dead]
cyanydeez 7mo ago
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.
- nkmnz 7mo ago
  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.
  - j16sdiz 7mo ago
    Have you tried the "Reference" section on the Wikipedia article? https://en.wikipedia.org/wiki/Induced_demand#cite_note-vanderbilt-11 https://en.wikipedia.org/wiki/Induced_demand#cite_note-vande...
  - cyanydeez 7mo ago
    You've never heard anyone choose to take side streets instead of the highway because of traffic jams? No one ever goes out of their way to avoid heavily trafficed areas?
    - nkmnz 7mo ago
      I don't understand what the point is you're trying to make. When people at t0 take detours because of traffic jams on the direct route, and then at t1, there are less traffic jam on the direct route due to additional lanes, so they decide to take the direct route, then total traffic is down, because they no longer take a detour. Even if they are still part of a newly induced traffic jam.

jLaForest 7mo ago

Jevons paradox https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox

rainsford 7mo ago

I'm not sure that's infinitely true as long as AI costs to the user are proportional to the cost it takes to run the model. Even if user costs are heavily subsidized by investment, as long as they are non-zero and go up when models cost more, there will be at least some pressure for cheaper models and not just more capable ones and that pressure will go up with costs. AI is a crazy industry, but it's not totally immune to the law of supply and demand. The real question though is how close are we to the point where the pressure is more for efficiency rather than capability. Anecdotally I think it's a ways off. Right now the general vibe I get is that people feel AI is very impressive for how cheap it is to use, which suggests to me that a lot of users would be very willing to pay more for more capable models. So the tipping point where AI hardware demand might slow down seems a ways off.

LoganDark 7mo ago
It has yet to be seen when hyperscalers will change their tune.