11 ms·
What if AI doesn't need more RAM but better math?
- Lerc 6mo agoThis 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.
- LoganDark 6mo agoWe 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 6mo agoIf 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 6mo agoI 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 6mo ago[flagged]
- lucasfin000 6mo agoMoE 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 6mo agoThe 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 6mo 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 6mo agoWhat is the difference between "hard earned" and not?
- Imustaskforhelp 6mo agoWell 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 6mo 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.
- Ray20 6mo ago> If models become more efficient Then we can make them even bigger.
- Imustaskforhelp 6mo 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 6mo agoBecause 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 6mo 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 6mo agoThere'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 6mo agoI 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 6mo agoUntil you put up your solar and then power is almost free...
- vidarh 6mo agoThe amortised cost including the panels and labour is nowhere near "almost free".
- boredatoms 6mo agoIt is over a couple of years
- vidarh 6mo agoEven 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 6mo agoAI 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 6mo agoAs 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 6mo agoSeems 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 6mo agoYes, 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 6mo agoWe 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 6mo 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 6mo agoThe 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 6mo agoBut what about The Jevons Paradox?
- redrove 6mo agoI 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 6mo ago[dead]
- cyanydeez 6mo agoDepartment 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 6mo agoCitation 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 6mo agoHave 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 6mo agoYou'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 6mo agoI 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 6mo agoJevons paradox https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
- rainsford 6mo agoI'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 6mo agoIt has yet to be seen when hyperscalers will change their tune.
- fph 6mo agoDespite the shortage, RAM is still cheaper than mathematicians.
- 3yr-i-frew-up 6mo ago[dead]
- captainbland 6mo agoI 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.
- iLemming 6mo agoRaising, nurturing, training, and mentoring an expert mathematician is not cheap; it never was, perhaps the first time in history when we can witness that rule to change - spinning up a bunch of math-savvy agents, each smarter than Ramanujan maybe will get too cheap.
- high_na_euv 6mo agoYou dont have to raise them, someone already did it, you have to hire them
- iLemming 6mo agoYou're oversimplifying the message I'm trying to convey. "you just hire them, someone already raised them" - treats mathematicians as a commodity stock rather than a flow. The conversation frames it as "mathematicians vs. RAM" - a cost comparison. But that's like comparing the cost of a GPS unit vs. a ship captain. The captain isn't expensive because they can calculate routes; they're expensive because they know when the route is wrong. AI makes the math cheaper but makes the mathematician more valuable, at least until true AGI genuinely surpasses human mathematical creativity - at which point we have much bigger economic questions than mathematician salaries. The topic on itself is quite interesting, and far complex than supply/demand norms. Even before AI, there was and both wasn't shortage of mathematicians - academic pure mathematics - there's a glut. High school teachers - people exist; but they won't work for teacher salaries. Applied math - acute shortage - quant finance, ML research, cryptography, pharmaceutical modeling - we don't have enough. NSA - always struggled to hire - private sector salaries pull people away. Interdisciplinary - mathematical biology, climate modeling, materials science - domains where math is the bottleneck but the job title isn't really "mathematician" - acute shortage.
- abdelhousni 6mo agoThe 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.
- tornikeo 6mo agoSigh. Don't make me tap the sign [1] [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- amelius 6mo agoCan we say something about the compression factor for pure knowledge of these models?
- konaraddi 6mo ago> 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.
- Verdex 6mo agoKnown in the business as 'pulling a jevons'
- miohtama 6mo agoIt will also help with local inference, making AI without big players possible.
- otabdeveloper4 6mo agoIt's already possible. Post-training is vastly more important than model size. (There's bigtime diminishing returns with increasing model size.)
- plagiarist 6mo agoIs 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.
- otabdeveloper4 6mo ago> Is there a size cutoff you would say where diminishing returns really kick in? No idea yet. But also it's obvious that making LLMs without MoE is stupid.
- alienbaby 6mo agoIve 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.
- mustyoshi 6mo agoThe 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.
- clawfund 6mo ago[flagged]
- aljgz 6mo agoIt 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.
- yorwba 6mo agoIt especially doesn't make sense considering that TurboQuant has been public on arXiv for almost a year: https://arxiv.org/abs/2504.19874 https://arxiv.org/abs/2504.19874 So it predates the late-2025 RAM price surge! https://pcpartpicker.com/trends/price/memory/ 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.
- zug_zug 6mo agoWell, when a companies have 100billion dollar incentives to make discoveries like this, I don't know if we should assume this is the only optimization that will happen. Given that increasing model size doesn't yield proportional increases in intelligence, there is a world where these datacenters don't have a positive ROI if we make these models even a fraction as effective as the human brain.
- boshalfoshal 6mo agoWell considering basically the entire market was down these past few days, Google included, its unlikely attributable to this paper alone. Its most likely correlated with general war/trade route restrictions/potential recession fears, or at least, more correlated with those than it is with this paper. This paper was released a year ago and was probably part of how google got to 1m context before other labs.
- Yokohiii 6mo ago> 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.
- barbegal 6mo agoDoes 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.
- zozbot234 6mo agoFor long context, yes this is at least plausible. And the latest models are reaching context lengths of 1M tokens or perhaps more.
- simne 6mo agoSure, 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.
- deleted 6mo ago[deleted]
- Bydgoszczo 6mo agoAnd maverick 2
- exabrial 6mo agoI was thinking it needs speciality hardware. Sort of like how GPUs were born…
- chr15m 6mo agoIs this something that will show up in Ollama any time soon to increase context size of local models?
- imjonse 6mo ago"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 https://openreview.net/forum?id=tO3AS KZlok ). We would greatly appreciate your attention and help in sharing it." https://x.com/gaoj0017/status/2037532673812443214 https://x.com/gaoj0017/status/2037532673812443214
- _0ffh 6mo agoOpenreview link is not working, was split apparently. https://openreview.net/forum?id=tO3ASKZlok https://openreview.net/forum?id=tO3ASKZlok
- zug_zug 6mo agoI guess I'm trying to understand. I'm hearing this paper has been around for a year -- I would think that many companies would have already implemented and measured its performance in production by now... is that not the case?
- zug_zug 6mo agoOkay, I spent about half an hour reading about this and asking gemini I guess my best understanding is this: The main breakthrough [rotating by an orthogonal matrix to make important outliers averaged acrossed more dimensions] comes from RaBitQ. Sounds like the RaBitQ team was much more involved, and earlier, and the turbo quant paper very deliberately tries to avoid crediting and acknowledging RaBitQ. My understanding is that the efficacy of these methods isn't in dispute, what turboquant did was adapt the method that was being used in vector databases and adapted it for transformers, and passed it of more as a new invention than an adaptation.
- PaddyLena 6mo agoI 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
- signa11 6mo agowhy not, you know, just use LLMs to do this job ?
- SphericalCowww 6mo agoI 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.
- redanddead 6mo agoshare 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
- SphericalCowww 6mo agoI put all the conceptual ideas here (spiced with far-fetched claims): https://github.com/SphericalCowww/ML_LunaLoRA https://github.com/SphericalCowww/ML_LunaLoRA But I think my hn level is too low, would really like some expert's opinion, though, whelp... Meanwhile, let me implement some basic models first.
- fittingopposite 6mo agoRe continuous fine-tuning: how do you avoid catastrophic forgetting in your proposal?
- effnorwood 6mo agothis is exactly correct.
- Skunkleton 6mo agoThe 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.
- am17an 6mo agoThere 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.
- mxmlnkn 6mo ago> 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.
- convexly 6mo agoThere's a bunch of research showing that more/better information doesn't reliably improve judgement, but better feedback on your existing predictions does. Makes me think of Soros and his whole thing about reflexivity.