"The TurboQuant paper (ICLR 2026) contains serious issues in how it describes RaBitQ, including incorrect technical claims and misleading theory/experiment comparisons.
We flagged these issues to the authors before submission. They acknowledged them, but chose not to fix them. The paper was later accepted and widely promoted by Google, reaching tens of millions of views.
We’re speaking up now because once a misleading narrative spreads, it becomes much harder to correct. We’ve written a public comment on openreview (https://openreview.net/forum?id=tO3AS
KZlok
).
We would greatly appreciate your attention and help in sharing it."
> The obvious one outside of KV caches as mentioned above is vector databases. Any RAG pipeline that stores embedding vectors for retrieval benefits from the same compression. TurboQuant reduces indexing time to “virtually zero” on vector search tasks and outperforms product quantisation and RabbiQ on recall benchmarks using GloVe vectors.
This part sounds especially cool. I did not think about this application when reading the other articles about TurboQuant. It would be cool to have access to this performance optimization for local RAG.
> 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.
Is there a size cutoff you would say where diminishing returns really kick in?
My experience doesn't disagree, at least. I've been using Qwen for coding locally a bit. It is much better than I thought it would be. But also still falls short in some obvious ways compared to the frontiers.
I think that either investors were extremely skittish that the stocks might crash and jumped at the first sign of trouble (creating a self-fulfilling prophecy) or they were trading on non-public information and analysts who don't have access to said information are reading too much into the temporal coincidence of the Google Research blog highlighting this paper.
The stock drop isn't about demand volume, it's about pricing power. HBM vendors have been charging huge premiums because AI buyers had no alternative to buying more memory. A 6x compression result means per-GB willingness to pay drops even if total shipments hold. Flat volume at lower margins is a worse business than growing volume at premium margins.
There are techniques which already achieve great compression of the cache at 4 bit, eg using hadamard transforms. Going from
4 bit to 3 bit isn’t the great leap people expect this to be. It’s actually slower to run and is generally worse in practice.
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.
Unfortunately, nobody at big companies know, what exactly math will win, so competition not end.
So, researchers will try one solution, then other solution, etc, until find something perfect, or until semiconductors production (Moore's Law) made enough semiconductors to run current models fast enough.
I believe, somebody already have silver bullet of ideal AI algorithm, which will lead all us to AGI, when scaled in some big company, but this knowledge is not obvious at the moment.
The TurboQuant paper is from April 2025. I’m sure the major labs knew about it on, or even before, the day it published. Any impact it had would have been a year ago. Yet I keep seeing these posts and discuss completely ignoring this.
Can we please start talking about this in that context? We already know what TurboQuant will do to DRAM demand. We already know what it will do to context windows. There is no need to speculate. There is no need to panic sell stocks.
I mean, since GPT-4, I believe the RAM is no longer creating the miracle that the LLM performance scales directly with the model size. At least ChatGPT itself convinced me that any decent-sized company can create a GPT4 equivalent in terms of model size, but limited by service options, like memory cache and hallucination handling. Companies buy RAM simply to ride the stock hype.
I am no expert, so this is a shallow take, but I think the global LLM already reaches its limit, and general AGI could only be possible if it's living in the moment, i.e., retraining every minute or so, and associating it with a much smaller device that can observe the surroundings, like a robot or such.
Instead of KV cache, I have an idea of using LoRA's instead: having a central LLM unchanged by learning, surrounded by a dozen or thousands of LoRAs, made orthogonal to each other, each competed by weights to be trained every 1 min say. The LLM, since it's a RNN anyway, provides "summarize what your state and goal is at this moment" and trains the LoRAs with the summary along with all the observations and say inputs from the users. The output of the LoRAs feeds back to the LLM for it to decide the weights for further LoRAs training.
Anyways, I am just thinking there needs to be a structure change of some kind.
Does the KV cache really grow to use more memory than the model weights? The reduction in overall RAM relies on the KV cache being a substantial proportion of the memory usage but with very large models I can't see how that holds true.
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.
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.
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.
> 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.
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.
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 when that happens people STILL won’t be able to afford the hardware.
Of course they will - if that happens all these AI token providers won't have a use for all that hardware they bought. You'll be buying used H100s and H200s off eBay for pennies on the dollar.
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.
> 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.
> That's ridiculous, "infinite money" isn't a thing.
My reading of GP is that he was being sarcastic - "infinite amounts of circular fake money" is probably a reference to these circular deals going on.
If A hands B investment of $100, then B hands A $100 for purchase of hardware, A's equity in B, on paper, is $100, plus A has revenue of $100 (from B), which gives A total assets of $200.
Obviously it has to be shuffled more thoroughly, but that's the basic idea that I thought GP was referring to.
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.
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?
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.
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.
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
> 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.
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.
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.
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.
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?
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.
> 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.
"The TurboQuant paper (ICLR 2026) contains serious issues in how it describes RaBitQ, including incorrect technical claims and misleading theory/experiment comparisons.
We flagged these issues to the authors before submission. They acknowledged them, but chose not to fix them. The paper was later accepted and widely promoted by Google, reaching tens of millions of views.
We’re speaking up now because once a misleading narrative spreads, it becomes much harder to correct. We’ve written a public comment on openreview (https://openreview.net/forum?id=tO3AS KZlok ).
We would greatly appreciate your attention and help in sharing it."
https://x.com/gaoj0017/status/2037532673812443214
Openreview link is not working, was split apparently.
https://openreview.net/forum?id=tO3ASKZlok
> The obvious one outside of KV caches as mentioned above is vector databases. Any RAG pipeline that stores embedding vectors for retrieval benefits from the same compression. TurboQuant reduces indexing time to “virtually zero” on vector search tasks and outperforms product quantisation and RabbiQ on recall benchmarks using GloVe vectors.
This part sounds especially cool. I did not think about this application when reading the other articles about TurboQuant. It would be cool to have access to this performance optimization for local RAG.
> 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 will also help with local inference, making AI without big players possible.
It's already possible. Post-training is vastly more important than model size. (There's bigtime diminishing returns with increasing model size.)
Is there a size cutoff you would say where diminishing returns really kick in?
My experience doesn't disagree, at least. I've been using Qwen for coding locally a bit. It is much better than I thought it would be. But also still falls short in some obvious ways compared to the frontiers.
Known in the business as 'pulling a jevons'
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.
It especially doesn't make sense considering that TurboQuant has been public on arXiv for almost a year: https://arxiv.org/abs/2504.19874 So it predates the late-2025 RAM price surge! https://pcpartpicker.com/trends/price/memory/
I think that either investors were extremely skittish that the stocks might crash and jumped at the first sign of trouble (creating a self-fulfilling prophecy) or they were trading on non-public information and analysts who don't have access to said information are reading too much into the temporal coincidence of the Google Research blog highlighting this paper.
The stock drop isn't about demand volume, it's about pricing power. HBM vendors have been charging huge premiums because AI buyers had no alternative to buying more memory. A 6x compression result means per-GB willingness to pay drops even if total shipments hold. Flat volume at lower margins is a worse business than growing volume at premium margins.
It could also reduce the total cost of AI to the point it becomes feasible for more tasks, increasing the demand, in case Jevon's kicks in.
There are techniques which already achieve great compression of the cache at 4 bit, eg using hadamard transforms. Going from 4 bit to 3 bit isn’t the great leap people expect this to be. It’s actually slower to run and is generally worse in practice.
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.
The comment feels a bit like Verdex may have dated a mathematician at some point and it went sour.
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
Without memory you have no data to compute on. Memory and compute scaling only makes sense in tandem.
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.
I think the biggest issue isn’t the tool itself, but access and stability. I had more trouble finding reliable AI accounts than using them tbh
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.
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.
Sure, we need better math, it is obvious.
Unfortunately, nobody at big companies know, what exactly math will win, so competition not end.
So, researchers will try one solution, then other solution, etc, until find something perfect, or until semiconductors production (Moore's Law) made enough semiconductors to run current models fast enough.
I believe, somebody already have silver bullet of ideal AI algorithm, which will lead all us to AGI, when scaled in some big company, but this knowledge is not obvious at the moment.
The TurboQuant paper is from April 2025. I’m sure the major labs knew about it on, or even before, the day it published. Any impact it had would have been a year ago. Yet I keep seeing these posts and discuss completely ignoring this.
Can we please start talking about this in that context? We already know what TurboQuant will do to DRAM demand. We already know what it will do to context windows. There is no need to speculate. There is no need to panic sell stocks.
Is this something that will show up in Ollama any time soon to increase context size of local models?
KV quantization has long been available in llama.cpp
I mean, since GPT-4, I believe the RAM is no longer creating the miracle that the LLM performance scales directly with the model size. At least ChatGPT itself convinced me that any decent-sized company can create a GPT4 equivalent in terms of model size, but limited by service options, like memory cache and hallucination handling. Companies buy RAM simply to ride the stock hype.
I am no expert, so this is a shallow take, but I think the global LLM already reaches its limit, and general AGI could only be possible if it's living in the moment, i.e., retraining every minute or so, and associating it with a much smaller device that can observe the surroundings, like a robot or such.
Instead of KV cache, I have an idea of using LoRA's instead: having a central LLM unchanged by learning, surrounded by a dozen or thousands of LoRAs, made orthogonal to each other, each competed by weights to be trained every 1 min say. The LLM, since it's a RNN anyway, provides "summarize what your state and goal is at this moment" and trains the LoRAs with the summary along with all the observations and say inputs from the users. The output of the LoRAs feeds back to the LLM for it to decide the weights for further LoRAs training.
Anyways, I am just thinking there needs to be a structure change of some kind.
share it on gh and make a show hn post about it, maybe you're right
the models are still very stupid atm something needs to change
I was thinking it needs speciality hardware. Sort of like how GPUs were born…
Does the KV cache really grow to use more memory than the model weights? The reduction in overall RAM relies on the KV cache being a substantial proportion of the memory usage but with very large models I can't see how that holds true.
For long context, yes this is at least plausible. And the latest models are reaching context lengths of 1M tokens or perhaps more.
this is exactly correct.
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.
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.
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.
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.
> 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.
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.
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 when that happens people STILL won’t be able to afford the hardware.
Of course they will - if that happens all these AI token providers won't have a use for all that hardware they bought. You'll be buying used H100s and H200s off eBay for pennies on the dollar.
No they won’t they’re just going to get absorbed into Azure and AWS and used for generic GPU compute that you rent until they’re burned out trash.
> 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
> 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.
> That's ridiculous, "infinite money" isn't a thing.
My reading of GP is that he was being sarcastic - "infinite amounts of circular fake money" is probably a reference to these circular deals going on.
If A hands B investment of $100, then B hands A $100 for purchase of hardware, A's equity in B, on paper, is $100, plus A has revenue of $100 (from B), which gives A total assets of $200.
Obviously it has to be shuffled more thoroughly, but that's the basic idea that I thought GP was referring to.
Cheaper for who? For them maybe but certainly not for you or me.
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.
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?
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.
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.
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.
Until you put up your solar and then power is almost free...
The amortised cost including the panels and labour is nowhere near "almost free".
It is over a couple of years
> 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.
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
> 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.
> 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.
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.
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.
Jevons paradox https://en.wikipedia.org/wiki/Jevons_paradox
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...
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?
And maverick 2
Can we say something about the compression factor for pure knowledge of these models?
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.
> 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.
why not, you know, just use LLMs to do this job ?