5 ms·
One major consequence of the ramapocalypse, I think, is an even higher focus on small efficient models. I personally believe that the multi-trillion parameter m
by jmward01 2mo ago
One major consequence of the ramapocalypse, I think, is an even higher focus on small efficient models. I personally believe that the multi-trillion parameter models are fundamentally missing things and the push to smaller, more efficient will drive evolutionary structural changes that will lead to future gains
- schainks 2mo agoI am literally betting my company on this being true.
- jmward01 2mo agoWhat company? I am 100% focused on this as a concept in my own internal research.
- schainks 2mo agoworkingmemory.ai!
- oblio 2mo agoIt's a bad bet, historically. I'm having an extremely hard time thinking of companies that have prospered due to software optimization. Most of them were swept away by hardware advances, instead.
- jmward01 2mo agoThe 1980's US car industry comes to mind. Nearly wiped out because they refused to make efficient vehicles. SpaceX is arguably showing how a rethink towards efficient can take over an entire industry. I am sure there are strong examples in software as well but they aren't coming to mind. I think when successful, optimization really just means 'finally built right' and people forget the ridiculously inefficient ways before.
- sgc 2mo agoI would argue about 30% of Apple's success was just from not being slow annoying bloatware. I could never stomach it, but I know a lot of people who basically rage-quit Windows for the Apple ecosystem. I did the same, but for Linux.
- somethingweird 2mo agoMany of the current internet titans started by making things more efficient and accessible. Google for search, Facebook for connecting to people online, Microsoft for working with PCs at a reasonable price, Amazon for buying online as well as AWS. There are examples in other industries as well, Toyota is famous for it for example. There are probably counter examples but efficiency gains can be a huge deciding factor making companies successful.
- bravura 2mo agoYou just listed a bunch of 0 to 1 companies, not 1 to 10 companies. They weren’t quantitatively better than previous companies. They were qualitatively better.
- cootsnuck 2mo agoI think finding significant efficiency gains with LLMs and the like may lead to qualitatively better products. Looking at people's experiences to DSV4F makes me believe that even more than before too. I don't think people are realizing that speed can allow for categorically different user experiences that are more than just "worse than frontier capabilities but faster".
- polymer8563 2mo agoIBM wants a word
- oblio 2mo agoOn what? OS/2 didn't fail due to lacking software optimization.
- hgoel 2mo agoThe headroom for hardware advances is a lot lower now than it has been for most of the industry's existence, when Moore's law held strong. Now we find ourselves limited by cost, physics, fab capacity, and complexity of spinning up more fab capacity.
- cootsnuck 2mo agoBetting on innovation continuing to figure out ways to squeeze more out of less has historically been the right move. Look at Apple. And I'd argue "hardware advances" are more proof of optimization.
- itsmeduncan 2mo agoMe too. I think there are a few waves we can ride here. Let's collaborate?
- NBJack 2mo agoI honestly hope to see this across all applications, games, services, operating systems, etc. We've been in a period of wasteful RAM usage for over a decade. Constraints, whatever their origin, can be a good thing.
- pjmlp 2mo agoSame here, back to when algorithms and data structures mattered.
- oblio 2mo agoIf China makes half decent RAM I would bet more on things like 128GM of RAM being the default on low spec laptops 10 years from now. While I do love optimized software, the hardware side, especially for PCs, has been stagnating for way too long. At least now we have a valid use case for doubling available RAM every 2-3 years again. I had a reasonably beefy Lenovo consumer line laptop that I bought in 2011, 8GBs of RAM. Its screen hinge broke and I couldn't repair it but I'm fairly sure it was otherwise still usable in 2023-24, once the HDD was replaced with an SSD. I think even now entry level laptops are sold with 8GB of RAM. By comparison a PC from 2000 was utterly unusable in 2012-13.
- KaiMagnus 2mo agoI count on a 128GB baseline in 10 years. Beyond the current atmosphere of despair, I really want to see what Apple especially is cooking. Local AI is right up their alley and the current scarcity is unacceptable for them in so many ways. I got the feeling laptops gonna feel very different in 2036.
- drob518 2mo agoGiven local AI requirements, I expect minimum configurations for higher end machines to accelerate quickly. Yes, an entry level MacBook Air might be at 32 GB, but the entry MacBook Pro should quickly have 128 GB as a baseline. All this assumes we can actually make all the RAM we need. Until then, we’re going to be artificially capped.
- cootsnuck 2mo agoI would say even without rampocalypse there would still be the strong incentive to innovate at the edge and under more extreme constraints. The incentives are just even stronger now. I'm looking forward to seeing what types of new things people create over the coming years once there is less obsession with massive unwieldy LLMs. I think the incentives are just too strong to ignore.
- cogman10 2mo agoI'd assume the closed weight models are all working on shrinking their parameter counts anyways. They too benefit from smaller models. It'd be foolish for these SOTA labs to not be working at reducing parameter counts.
- literalAardvark 2mo agoThe incentive is there, but their money making niche is to solve problems you can't solve locally with a 30b, so they're unlikely to stray into territory owned by ultra cheap to run open weights models.
- jrflo 2mo agoLet's not forget the Bitter Lesson. Small models sound really nice but at some point you're just fighting the laws of information theory. Efficiency gains on the small model side are nice, but efficiency gains + giant model tends to be even better...
- TeMPOraL 2mo ago[dead]
- boredatoms 2mo agoHow many bits of information are in a real brain?
- jrflo 2mo agoYou're totally right that we're far away from brain-level efficiency, but I'm just saying any efficiency gains we make towards small models will likely be felt on large ones as well, and we'll all move the goalposts to what the frontier can do. Hypothetically, getting GPT 5.6 performance in a 30B model would be amazing, but just imagine what you could do with a GPT 5.6-sized model at that point.
- wslh 2mo agoDon't know but a single neuron as a computation unit is far more complex than an ANN [1]. [1] https://christofkoch.com/biophysics-book/ https://christofkoch.com/biophysics-book/
- anon373839 2mo agoI feel like the industry has quietly moved past the Bitter Lesson. In 2023 the story was naive parameter/data scaling and “emergent” intelligence properties. But there wasn’t enough data or compute to keep pushing in that direction, and the gains from it have been sublinear anyway. Now, the labs spend enormous effort curating data pipelines to fit the models to a large assortment of very specific tools, tasks, harnesses, domains, etc. They also kind of fit to benchmarks by creating loads of synthetic training data that resembles benchmark tasks. None of this feels like the “scale up primitive methods and turn off your brain” message Sutton originally delivered.
- XCSme 2mo agoThis is just temporary though, right? With the benefit of LLMs already being proven, in a couple of years we will have vastly better hardware for inference I guess. I feel like now hardware is stagnating a bit, because the software side has moved too fast for the hardware to catch up. Once we settle on some good, optimal software architecture for the models, dedicated hardware will easily increase throughout by 10x or 100x, for a fraction of the cost. LLMs seems quite simple, maybe we'll be able to print/assemble at home our own chips with the desired models/weights. Maybe we'll have model weights being shared like game cartridges.
- jmward01 2mo agoI personally think of this like sorting algorithms. Quick sort does the same thing bubble sort does so why do we need quick sort? Pushing for efficiency drives innovation. It does this for many reasons but a big one is that putting a cap on a resource forces you to consider the others available and often you find that all it took was a little effort and suddenly the alternate path that looked a little worse is actually better than you realized. This has a lot to do with how MCTS works BTW. The current best path is often only the current best path because a lot of investment has been sunk into it. If you were to put equal resources into a different path you may find that it was actually far better. It is just that the early rollouts favored the other 'best path' so you sunk a lot of resources into that one. We are very early in our exploration of LLM architecture. I highly doubt we are anywhere near the best path right now.
- XCSme 2mo agoDefinitely, LLMs are highly ineficient now. The diffusion models are interesting, but those also seem hacky. I think the next form of AIs will be simpler and more abstract. The building blocks of our brain don't have the notion of a "token" embed into them, it's lower level that that. I think first step is to find a better way to represent information. LLMs shouldn't "compute" stuff using language tokens, but some other, more efficient logical mechanisms. LLMs should first "feel" the solution, reason internally in that optimised space, then, only when interacting with a human should it convert all that into actual tokens/language.
- whimsicalism 2mo agoI think the path of least resistance will end up being the cheapest and that is scaling up the parameters a ridiculous amount until you get highly capable models that can develop/distill/design the RAM efficient models. Going straight for low param is foolish and just a cope by smaller labs because they don't have the compute/talent to train the large ones. This is 100% true for pretrains, likely true for RL as well although maybe there is some benefit to smaller activated params there. There is of course 0 benefit to small dense models relative to large sparse ones that are equally as memory efficient if you have enough computers. Many on HN are in deep denial about this imo.
- mortsnort 2mo agoReally? I feel like because nobody has RAM they're being pushed to the cloud frontier models. If we could all have our own 64GB+ VRAM GPUs, I feel like the open weight model scene would be even stronger.
- ashu1461 2mo agoRight now there is a stark difference between what smaller models can do and bigger models can do. Smaller models are suitable for simple tasks like classification / summarisation while larger models are better in agentic capabilities.
- astrobiased 2mo agoDepends on the type of agentic task though. For simple operations, a small model can be quite beneficial.
- b3ing 2mo agoI hope ssd streaming gets more popular, maybe more breakthroughs like that will help change things