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What's the strongest AI model you can train on a laptop in five minutes?
- b112 1y agoPerhaps grimlock level: https://m.youtube.com/shorts/4qN17uCN2Pg https://m.youtube.com/shorts/4qN17uCN2Pg
- treetalker 1y ago"Hadn't thought of that …" "You're absolutely right!"
- lamuswawir 1y agoThanks.
- zarzavat 1y agoInstead of time it should be energy. What is the best model you can train with a given budget in Joules. Then the MBP and the H100 are on a more even footing.
- NooneAtAll3 1y agoit's not about efficiency - it's about availability H100 is not an everyday product. Laptop is
- KeplerBoy 1y agoStill, I don't think the m4 is going to be far off from the h100 in terms of energy efficiency. edit: fixed typo
- Der_Einzige 1y agoAt this point, given how many H100s there are in existence, it’s basically an everyday product.
- logicchains 1y agoI envy you if $25k is an everyday product cost.
- jeroenhd 1y agoFor what it's worth, most of the world can't afford an M4 Macbook either.
- wongarsu 1y agoAnd renting an H100 for an hour is a lot easier than renting an M4 MacBook for an hour.
- falcor84 1y agoMaybe not to buy one, but to rent one. Like how barista-made coffee is an everyday product even though most people can't afford a fancy professional coffee machine.
- bee_rider 1y agoReasonably high quality coffee machines are very widespread. Or you can do pour-over. I don’t think the cost of a machine is a limiting factor for many people, it is just convenience. Maybe an analogy could be made to espresso, nice espresso machines get costlier. But, you can still get quite good results out of a manual machine like a Flair. I think this is why the suggestion to rent a machine is not to helpful. In this analogy we’re on BaristaNews, we all know about the industrial machines, lots of folks use them at work. But, the topic of what sort of things you can do on your manual machine at home has come up.
- inetknght 1y ago
- Sharlin 1y agoH100s are almost-instantly available to anyone with a credit card and access to the internet. Without even having to lift their butt from the seat. And you get plenty more than five minutes of compute for the price of an M4.
- jsperson 1y agoFor the orgs where I've worked the important thing isn't availability of compute it's security. Using what we have on our local network is much easier from a governance and approval standpoint than whatever is available on the internet.
- Sharlin 1y agoMany orgs have no problems using cloud envs for most things. The usual suspects offer just as secure compute envs as everything else. Anyway, I was assuming personal use, like the messing-around experimenting that the article is about. (Or who knows, maybe it was part of the author’s job.)
- potatolicious 1y agoAnd yet just about any intro-to-programming tutorial gets something running on your local machine, and local machine development continues to be the default for most people, even though devving on a cloud machine is eminently reasonable. "Pull out credit card, sign up for some thing and pay a bit of money" is a non-trivial bit of friction! Extremely non-trivial! Especially in a corporate context - you have to get the expense approved. It's not clear if you can put company data onto the machine. Whereas generally running local things on corporate laptops is far less controversial. "Download this tool and run it." is still an extremely powerful pitch. Pretty much the only thing that beats it is "go to this website which you can use without any signup or payment".
- Sharlin 1y agoSure, if you already have said local machine. Which I guess in HN’s context many/most do.
- nickpsecurity 1y agoAlso, my laptop running Linux and its outputs are probably mine and private. If I use cloud GPU's, I need to be a lawyer to be sure what they can or can't do with my data or models. There's also no overages or hidden charges with a laptop. Past simply breaking it. You know the replacement cost ahead of time, though.
- giancarlostoro 1y agoMac is more competitive on power consumption though since its not ever pulling as much as a Nvidia GPU is my understanding. On that note you can rent an H100 for an hour for under $10 which might make for a slightly more interesting test, whats the best model outcome you can train in under an hour.
- dtnewman 1y ago> you can rent an H100 for an hour for under $10 Far cheaper these days. More like $2-3 for a consumer to do this. For bulk deals, pricing is often < $2.
- giancarlostoro 1y agoI couldnt remember offhand the exact amount but figured noting that under $10 is still impressive for one high end GPU for an entire hour.
- bigyabai 1y agoIt depends. If you're bottlenecked by memeory speed, the Mac typically comes out on-top. In terms of conpute efficiency though, Nvidia still has Apple beat. Nvidia wouldn't have the datacenter market on a leash if Apple was putting up a real fight.
- giancarlostoro 1y agoYeah, this is correct. My 3080 will render quicker than my M4 but my M4 will outcompete on being able to load larger models.
- netcan 1y agoThey're all good. Being somewhat arbitrary isnt a bad thing.
- jvanderbot 1y agoBro por que no los dos We can / should benchmark and optimize this to death on all axes
- motorest 1y ago> Instead of time it should be energy (...) Then the MBP and H100 are on a more even footing. What exactly is your point? That instead of expressing workloads in terms of what a laptop could do, you prefer to express them in terms of what a MacBook Pro could do?
- zarzavat 1y agoThe point is that "best model you can train in 5 minutes" is hardware dependent, the answer will be different depending on the hardware available. So it's necessarily a single-player game. "Best model you can train with X joules" is a fairer contest that multiple people could take part in even if they have different hardware available. It's not completely fair, but it's fair enough to be interesting. Training models with an energy limit is an interesting constraint that might lead to advances. Currently LLMs implement online learning by having increasingly large contexts that we then jam "memories" into. So there is a strict demarcation between information learned during pre-training and during use. New more efficient approaches to training could perhaps inform new approaches to memory that are less heterogenous. tl;dr: more dimensionally correct
- deleted 1y ago[deleted]
- evrennetwork 1y ago[dead]
- hodgehog11 1y agoI love seeing explorations like this, which highlight that easily accessible hardware can do better than most people think with modern architectures. For many novel scientific tasks, you really don't need an H100 to make progress using deep learning over classical methods.
- tootyskooty 1y agoI suspect one can go a lot further by adopting some tweaks from the GPT-2 speedrun effort [0], at minimum Muon, better init and carefully tuning learning rate. [0]: https://github.com/KellerJordan/modded-nanogpt https://github.com/KellerJordan/modded-nanogpt
- nottorp 1y agoBut supposing you have a real specific need to train, is the training speed still relevant? Or do the resources spent on gathering and validating the data set dwarf the actual CPU/GPU usage?
- wongarsu 1y agoIf training is trivially fast that allows you to iterate on architecture choices, hyperparameters, choices which data to include, etc Of course that only works if the trial runs are representative of what your full scale model will look like. But within those constraints optimising training time seems very valuable
- l5870uoo9y 1y agoThe most powerful Macbook Pro currently has 16 CPU cores, 40 GPU cores, and 128 GB of RAM (and a 16-core “neural engine” specifically designed to accelerate machine learning). Technically, it is a laptop, but it could just as well be a computer optimized for AI.
- alberth 1y agoThe Mac Studio has: 32 CPU 80 GPU 512GB RAM https://www.apple.com/shop/buy-mac/mac-studio/apple-m3-ultra-with-28-core-cpu-60-core-gpu-32-core-neural-engine-96gb-memory-1tb# https://www.apple.com/shop/buy-mac/mac-studio/apple-m3-ultra...
- Joel_Mckay 1y agoFrom https://opendata.blender.org/ https://opendata.blender.org/ : Apple M3 Ultra (GPU - 80 cores) scores 7235.31 NVIDIA GeForce RTX 5090 Laptop GPU scores 7931.31 Note the memory constraints of NVIDIA are not like Apple silicon which tends to also be less i/o constrained. YMMV https://www.youtube.com/watch?v=d8yS-2OyJhw https://www.youtube.com/watch?v=d8yS-2OyJhw https://www.youtube.com/watch?v=Ju0ndy2kwlw https://www.youtube.com/watch?v=Ju0ndy2kwlw Apple m3/m4 silicon is certainly good in some ways, but the bottleneck is often a lack of CUDA software support and price (could buy >4 times the GPU raw performance on a dual rtx 5090 desktop.) =3
- pstuart 1y agoNot just GPU performance -- the M3 Ultra has memory bandwidth of ~800GBps vs ~1,800GBps for the 5090. I would wager that Apple recognizes the value prop for the mac to be used for AI and will up their memory bandwidth to stay in the game.
- Joel_Mckay 1y agoThe key features of the m3 ultra is 512GB of shared GPU/CPU ram, and ultra fast LAN over peripheral cabling. Once an NVIDIA card caches a model into its VRAM, than it doesn't get hit with the memory data copy cost over the bus. Yet as many people have noticed, who cares if the m3 ultra takes four times as long if the faster alternative simply won't fit the larger models. YMMV =3
- LorenDB 1y ago> Paris, France is a city in North Carolina. It is the capital of North Carolina, which is officially major people in Bhugh and Pennhy. The American Council Mastlandan, is the city of Retrea. There are different islands, and the city of Hawkeler: Law is the most famous city in The Confederate. The country is Guate. I love the phrase "officially major people"! I wonder how it could be put to use in everyday speech?
- api 1y ago[flagged]
- emeril 1y ago[flagged]
- chias 1y agoThis is not true. I watched the clip. She referred to AI as AI. When she said A1 she was very clearly referring to America First.
- quaristice 1y agoSnopes confirmed that McMahon began by referring correctly to “AI development,” but in the same response, twice said “A1 teaching,” clearly meaning artificial intelligence. Not steak sauce. Multiple outlets including Gizmodo, Newser, Yahoo News, Mediaite, and Cybernews all reported the slip-up as genuine: she erroneously said “A1” when she meant “AI”.
- chias 1y agoDid you watch the clip yourself? I assume not, so here you go: https://www.youtube.com/live/lxrg28zBv94?t=7562s https://www.youtube.com/live/lxrg28zBv94?t=7562s She was the chair of the board of the America First Policy Institute. She's not talking about AI, she's talking about pumping ultra-nationalist, Nazi-adjacent propaganda into Red state education.
- wowczarek 1y agoNot the point of the exercise obviously, but at five minutes' training I wonder how this would compare to a Markov chain bot.
- mhogers 1y agoAny reason to upgrade an M2 16GB macbook to a M4 ..GB (or 2026 M5) for local LLMs? Due an upgrade soon and perhaps it is educational to run these things more easily locally?
- ionwake 1y agoI did just that , got the r 32gb ram one so I could run qwen. Might still be early days I’m trying to use the model to sort my local notes but I don’t know man seems only a little faster yet still unusable and I downloaded the lighter qwen model as recommended. Again it’s early days maybe I’m being an idiot I did manage to get it to parse one note after about 15 mins though.
- dpoloncsak 1y agoHave a 16GB one, just setup ollama yesterday. gpt-oss-20b eats too much ram to use for anything other than an overnight task. maybe 3tok/s. Been playing around with the 8b versions of qwen and deepseek. Seems usable so far. YMMV, i'm just messing around in chat at the moment, haven't really had it do any tasks for me
- sandreas 1y agoFor LLMs, VRAM is the requirement number one. Since MacBooks have unified RAM you can use up to 75% for the LLM, so a higher RAM model would open more possibilies, but these are much more expensive (of course). As an alternative you might consider a Ryzen Pro 395+ like in the Framework desktop or HP Zbook G1a but the 128GB versions are still extremely expensive. The Asus Flow Z13 is a tablet with ryzen 395+ but hardly available with 128GB
- schaefer 1y agoYou could train an unbeatable tic-tac-toe ai on your laptop in five minutes. It doesn’t get any stronger than that. — I know, I know. I’m intentionally misinterpreting the OP’s clear intent (the stuff of comedy). And normally a small joke like this wouldn’t be worth the downvotes… But, I think there’s a deeper double meaning in this brave new world of prompt engineering. Most chat isn’t all that precise without some level of assumed shared context: These days the meaning of the phrase ai has changed from the classical definition (all algorithms welcome), and now ai usually means LLMs and their derivatives.
- silverlake 1y agoI’m actually working on just this. What’s the smallest training data set required to learn tic-tac-toe? A 5yo doesn’t need much training to learn a new game, but a transformer needs millions of samples.
- rkomorn 1y ago> A 5yo doesn’t need much training to learn a new game A 5yo also has... 5 years of cumulative real world training. I'm a bit of an AI naysayer but I'd say the comparison doesn't seem quite accurate.
- silverlake 1y agoIt’s a glib analogy, but the goal remains the same. Today’s training sets are immense. Is there an architecture that can learn something with tiny training sets?
- rkomorn 1y agoI'm certainly not challenging anything you're writing, because I only have a very distant understanding of deep learning, but I do find the question interesting. Isn't there a bit of a defining line between something like tic-tac-toe that has a finite (and pretty limited for a computer) set of possible combinations where it seems like you shouldn't need a training set that is larger than said set of possible combinations, and something more open-ended where the impact of the size of your training set mainly impacts accuracy?
- highfrequency 1y agoThis is awesome - thanks for sharing. Appreciate the small-scale but comprehensive studies testing out different architectures, model sizes and datasets. Would be curious to see a version of your model size comparison chart but letting the training continue until perplexity plateaus / begins to overfit. For example: are your larger models performing worse because they are overfitting to a small dataset, or because you are comparing model sizes at a fixed 5 minute computation time - so that the large models just don't get to learn very much in that time. (Also interesting would be learning curve comparisons between architecture/param count)
- Aperocky 1y agoAt which point is a simple markov chain same/better?
- visarga 1y agoOutput text is word salad every few words apart. You can't scale n-gram counting enough to make it work.
- sadiq 1y agoYou might find https://arxiv.org/abs/2401.17377v3 https://arxiv.org/abs/2401.17377v3 interesting..
- JPLeRouzic 1y agoOnly if you have access to corporate-level hardware: "It took us 48 hours to build the suffix array for RedPajama on a single node with 128 CPUs and 1TiB RAM"
- protomikron 1y agoIt's okayish. Considering 64G to 128G are available for (nerd) high-end consumers you're just off with a factor 5 (if we can squeeze out a little bit more performance). Thas is pretty astonishing in my opinion.
- JPLeRouzic 1y agoNot exactly a few words in my experience, I would say every 100 words, if you sophisticate your Markov Chain (n-gram = 3 at minimum, using a good tokenizer, making it tailored to the training data, large training set (500Kbytes or +), intelligent fallback instead of random, etc.).
- Nevermark 1y agoIt is the other way around. Neural-type models have long passed the point where markov chains made any sense by many orders of magnitude. Markov models fail by being too opinionated about the style of compute. In contrast, a linear tensor + non-linear function has incredible flexibility to transform the topology of information. Given large enough tensors, two such layers, with recurrence, can learn any mapping, static or dynamical. No priors (other than massive compute) needed. All other neural architectures then are simply sparser arrangements, that bring compute demands down. Where the sparseness is fit to the type of problem. Sparseness can be deeper but narrower information flows (thus “deep” learning). Or in lower numbers of weights to weight application (I.e. shared weights, like convolutions).
- pjmlp 1y agoWhich laptop, though?
- jebarker 1y agoOptimized small model training is not only important for availability but also for the scientific study of LLMs. It’s like the use of simple organisms like yeast for biological studies - we also need to study the simplest possible transformers that exhibit behaviors of interest from the larger models if we hope to ever understand LLMs and have more control over their behavior.
- biophysboy 1y agoIt’s a fun analogy because the data “environment” of the model being trained matters a great deal
- jebarker 1y agoExactly. YOLO runs of frontier models with a single random seed/data shuffle are pretty limited for trying to study the “molecular biology”. I actually like to think of LLM understanding as being like biology in the 1850s. There's lots of inspiration to be found in how biology has advanced since then and the types of experiments we might run to better understand LLMs.
- biophysboy 1y agoIts something I keep thinking about when I see all these deep-dives by Anthropic on the "genetics" of LLMs. I see the emergent properties of LLMs as inseparable from their data environment. If the organization/prevalence of text online was different, I think Anthropic would see different "genetics". As the amt of LLM-generated text grows, I think it will become more clear that the "fundamental unit" is their relationship.
- willvarfar 1y ago(there are also lots of private company datasets like e.g. user purchase history that can be used with small models to solve real business problems. All the advances in 'large' language models can be leveraged and applied to small problems if the input sequences can be represented as a special custom language.)
- 1y ago
- aniijbod 1y agoLet the AI efficiency olympics begin! On a laptop, on a desktop, on a phone? Train for 5 minutes, an hour, a day, a week? On a boat? With a goat?
- visarga 1y agogoats have too many parameters, they are like GPT-4
- hinkley 1y agoGO4-T
- rPlayer6554 1y agoI’d pay for GoatLM
- Nevermark 1y agoOn a maxxxed out Mac Studio M3 Ultra 512GB. That boat will float your goat!
- evanmoran 1y agoThis literally is fast enough to really whip the llamas.. well, you know. And for the younger folk, this mp3 player was the precursor to Spotify: https://youtu.be/HaF-nRS_CWM?si=d7WHzkV7CFHJ2hGg https://youtu.be/HaF-nRS_CWM?si=d7WHzkV7CFHJ2hGg
- lifestyleguru 1y agoHonestly AI is a trick to make us buy new expensive computers. I'm writing this from over 10 years old one and the computers offered in a leaflet from nearby electronic store aren't much better.
- voidUpdate 1y agoI mean, gaming is the big pusher of new hardware these days, and web is basically the reason you can use a 90s computer in the modern day. I happily survived on roughly 10 year old components all the way through university because I wasn't playing AAA games
- yunusabd 1y agoNow imagine what you could do in 6 minutes! But honestly I really like the short turnaround times. Makes it easy to experiment with different parameters and develop an intuition for what they do.
- pilooch 1y agoI'd be interested in what implementation of D3PM was used (and failed). Diffusion model are more data efficient than their AR LLM counterpart but les compute efficient at training time, so it'd be interesting to know whether with more time.to.converge the diffusion approach does succeed. I guess I'll try :)
- yalogin 1y agoThe bigger question or may be even realization is that with this architecture there is no way to build a capable model to run on the laptop or phone, which means there will never be local compute and servers became ever more important. In general thinking about how ML itself works, reducing model size while retaining capability will just never happen.
- simonw 1y agoThis post is about training, not inference. The lesson here is that you can't use a laptop to train a useful model - at least not without running that training for probably decades. That doesn't mean you can't run a useful model on a laptop that was trained in larger hardware. I do that all the time - local models hit really good this year. > reducing model size while retaining capability will just never happen. Tell that to Qwen3-4B! Those models are remarkably capable.
- grim_io 1y agoIt's always a question of "compared to what?" Local models are no where near capable compared to frontier big models. While a small model might be fine for your use case, it can not replace Sonnet-4 for me.
- simonw 1y agoSure, Qwen-3-4B - a 4GB download - is nowhere near as capable as Claude Sonnet 4. But it is massively more capable than the 4GB models we had last year. Meanwhile recent models that are within the same ballpark of capabilities as Claude Sonnet 4 - like GLM 4.5 and Kimi K2 and the largest of the Qwen 3 models - can just about fit on a $10,000 512GB of RAM Mac Studio. That's a very notable trend.
- grim_io 1y agoIt doesn't feel like that the gap is closing at all. The local models can get 10x as good next year, it won't matter to me if the frontier models are still better. And just because we can run those models (heavily quantized, and thus less capable), they are unusably slow on that 10k dead weight hardware.
- faangguyindia 1y agoThe best LLM on the planet right now is Gemini Pro 2.5 and Gemini Flash 2.5, nothing comes close to these. Once you setup a good system prompt on these, nothing really compares. Most of the models you see with high benchmarks are not even comparable on real tasks. qwen3 or deepseek r1, they aren't even 1/10 as good as Gemini Pro2.5
- howmayiannoyyou 1y agoThen they are not the best. Most users aren't prompt engineers and grew up expecting to enter search terms into Google and get a result. If its the case OpenAI or Anthropic are best able to interpret user intent there's a good argument to be made they are the best.
- faangguyindia 1y agothis is something people do not understand. If model trusts the users, and if user is dumb model will "weigh" user's input much higher and end up with flawed code. If the model is more independent, it will find the right solution. If just want a dumb model which says yes to everything, and follows you when u are not at smart enough then you'll never end up with good solution if not by luck.
- dvrj101 1y ago> not even comparable on real tasks. care to elaborate how gemini did completed this task successfully and how other models fumbled ?
- faangguyindia 1y agoI am using AI to write full projects, complete code generation and haven found any model which comes close to Gemini Pro2.5 in code generation reasoning and generation. While other models like qwen3, glm promise big in real code writing they fail badly, get stuck in loops. The only problem right now i run into gemini is i get throttled every now and then with empty response specially around this time.
- hnfong 1y agoHere's an Obfuscated C Contest entry that trains a toy model using LSTM: https://www.ioccc.org/2019/mills/index.html https://www.ioccc.org/2019/mills/index.html I suppose if you only have 5 minutes this is probably about the level you'd get.
- fswd 1y agoRight now, Qwen3 4B
- chasd00 1y agoAI is a broad term, the zero-to-hero series by Karpathy trains one in a Jupyter notebook. You can make some pretty powerful networks to de-duplicate database rows right in your laptop too. Data de-duplication and general MDM is pretty useful in large businesses.
- fontsgenerator 1y agoProbably something like a small logistic regression or a tiny GPT-2 variant (117M parameters) on a small dataset—anything beyond that will choke on RAM, VRAM, or time. Five minutes on a laptop = toy models, not miracles.
- initramfs 1y agoI looked up the most expensive laptop with an RTX 5090: https://marketplace.nvidia.com/en-us/consumer/gaming-laptops/?locale=en-us&page=1&limit=12&gpu=RTX%205090,RTX%205080,RTX%205070%20Ti,RTX%205070,RTX%205060&sorting=hp https://marketplace.nvidia.com/en-us/consumer/gaming-laptops... $5599.00 https://marketplace.nvidia.com/en-us/consumer/gaming-laptops/msi-titan-18-18-0-geforce-rtx-5090-laptop-gpu-2-1-5-5ghz-32gb-2-memory-2tb-nvme-ssd-gen5x4-4tb-2tb-2-nvme-ssd-gen4x4-ssd-windows-11-pro-gaming-laptop-120-hz-mini-led-titan-18-hx-ai-a2xwjg-614us/ https://marketplace.nvidia.com/en-us/consumer/gaming-laptops... Although you can get them with fewer specs and the same GPU for $3,899.99 https://marketplace.nvidia.com/en-us/consumer/gaming-laptops/gigabyte-aorus-master-16-bzhc6use65sh/ https://marketplace.nvidia.com/en-us/consumer/gaming-laptops...
- nehal3m 1y agoThe same SKU on a GPU can perform differently depending on how the manufacturer powers and cools it [0], and nVidia's naming shenanigans don't help either [1]. [0] https://www.digitaltrends.com/computing/laptop-gpu-power-limits-tgp-nvidia-amd https://www.digitaltrends.com/computing/laptop-gpu-power-lim... [1] https://coconote.app/notes/4c75b7a0-eb41-435d-85ee-55ae2dd8d6e9 https://coconote.app/notes/4c75b7a0-eb41-435d-85ee-55ae2dd8d...
- zipy124 1y agoAnd even worse it's surprisingly hard to find out what power budget is assigned to the GPU/ CPU or combined on spec sheets.
- bryanrasmussen 1y agoI like this scenario for a future James Bond movie. Bond has to have an AI in chat pretend to be him to stall the bad guys while he is sneaking around the back, but the state of the art Bond persona bot that Q gave him in its own hardware enclosure has been smashed up in the previous fight scene. Bond has only minutes to train a strong enough AI model to pretend to be him and fool his targets long enough for him to gain entry to their impregnable fortress. Can he do it?!?
- rsyring 1y agoBut...they need to show him "training" it by smashing away at the keys frantically. A touch of sweat rolling down his face while a progress meter inches across the screen to suspenseful music.
- bryanrasmussen 1y agono that is a cliche from lesser brands, Bond will get drunk while it trains and shoot somebody with amazing accuracy.
- hinkley 1y agoWe’re gonna need a montage.
- blitzar 1y agoThe product placement spots will sell themselves.
- deleted 1y ago[deleted]
- panarchy 1y agoThis would be more interesting if it wasn't about (L)LMs
- jasonjmcghee 1y agoThe idea of tracking and optimizing this reminds me of similar efforts a few years ago especially for image models via DAWNBench. https://dawnd9.sites.stanford.edu/dawnbench https://dawnd9.sites.stanford.edu/dawnbench
- simianwords 1y agoAn idea worth exploring: if specialized models on datasets can be trained quickly, it can be used as tools by bigger models.
- Razengan 1y agoI'd be happy with an AI that can just "train" on me: Just see what I do, learn from the repetitive tasks I do, and then do them quicker. An agent that is basically me x 10. Start blank with no corporate-controlled/crippled state and just become me. In fact, that might be the only way to let computers appear to grow faster into the future, even if their internal hardware only gets minor incremental improvements: Have your shit done before you sit down to do it.
- jl6 1y agoFeels like there should be value in building smaller, more specialized models - maybe even doing so on-demand. I don’t always want a model that knows Polish and astrophysics and Shakespeare, I want one that runs really fast and is laser-focused on the domain that I’m working on. I want to be able to say to a large general purpose LLM: “write a script that trains a model that is optimized for <useful task>” and then run that model. Edit: well gosh darn. Within the edit window for this comment, Google goes and launches Gemma 3 270M.
- indoordin0saur 1y agoWhat about overnight on a desktop with a higher-end Nvidia gaming GPU? Asking for a friend.
- heyoleftycunts 1y ago[flagged]
- erikqu 1y agoI would've liked to see some xlstms
- Animats 1y ago"Paris, France is a city in North Carolina. It is the capital of North Carolina." If only we had a technology that didn't hallucinate and reported "I don't know". Then small models would be far more useful. Part of the need for insanely huge LLM models is to get coverage so broad that they don't have to make up stuff. It would be nice to be able to train a customer service bot on a laptop in a reasonable length of time. But it will screw up badly outside its area of competence, which will happen frequently.
- Closi 1y agoI don’t think we should use an AI trained in 5 minutes on a laptop to infer what small models are capable of… Sure they still have massive problems with hallucination, but this article doesn’t give us any more insight into that I don’t think!
- gambiting 1y agoWhy not? And I'm not being flippant, but like....isn't that the whole point of small models?
- kevinventullo 1y agoAs I understand it, the most effective small models are synthesized from larger models.
- remexre 1y agoFor one thing, the model is trained on a language modelling task, not a question-answering task?
- Closi 1y agoI guess different small models will have different points/goals, but you can still have a small model with lots of training effort or a large model with little training effort. I think the point of most (frontier) small models is usually to provide the best answer possible given small inference resources, rather than to reduce training time. This is more of a toy model, so fun and an interesting project but it doesn't necessarily tell us what the art of the possible is for small models.
- jarmitage 1y agoAI is sorely lacking a demoscene
- jononor 1y agoThe TinyML niche, doing ML on microcontroller grade hardware (usually for sensor data analysis), is close in terms of constraints. Models measuring in kilobytes of RAM/FLASH. But usually missing the flair and showmanship of demoscene, and the just-because-we-can attitude. So I agree, need more demoscene style! As the maintainer of an open source library in this space (emlearn), I would be interested in contributing to such an effort.
- djmips 1y agoI should explore this niche. What are some good articles about wins or good use cases?
- andrewstuart 1y agoWould have been useful to see exact steps taken to replicate the result.
- iamgopal 1y agoIf only AI models are trained to connect to data (sql) and use that to answer some of the questions using data source instead of just train on them, it could reduce model size a lot.
- CharlesW 1y agoThat's what tools are for. (see MCP: https://modelcontextprotocol.io/docs/getting-started/intro https://modelcontextprotocol.io/docs/getting-started/intro)
- scubbo 1y agoWould RAG also be an approach here? My intuition from some small investigation is that RAG is more formal and structured to set up, but more efficient, whereas MCP you can just point an LLM at an MCP server and tell it to figure shit out (and also MCP can be used to _do_ stuff, not just to acquire more information).
- charcircuit 1y agoA trick that would be useful would be to start with an existing model instead of trying to generate it from a random starting place.
- lsb 1y agoThis is evocative of “cramming”, a paper from a few years ago, where the author tried to find the best model they could train for a day on a modern laptop: https://arxiv.org/abs/2212.14034 https://arxiv.org/abs/2212.14034
- quux 1y agoDepends on how much weight you can support on your lap
- deleted 1y ago[deleted]
- profsummergig 1y agoReaders: I'm looking for toy, quick AI exercises that can be trained on a laptop, and help the doer increase their confidence in AI concepts (learning by doing, and all that). The OP fits the bill. If you can suggest other such exercises, please share in reply to this post. Thank you.
- dileeparanawake 1y agoSiri.
- raindear 1y agoHow far can you go by improving the curriculum? Start simple. Find a shorter and shorter sequence of examples that gives you thd best result. What is the shortest sequence to get to some perplexity? Why?
- remexre 1y agoAm I missing where the GitHub link is for this, or did the author not release sources? It'd be fun to reproduce this on a different machine, and play around with other architectures and optimizers that weren't mentioned in the article...
- trhway 1y agoThere was https://sortbenchmark.org https://sortbenchmark.org, and now we need a similar for AI - best per joule, per 1 cent, per minute.
- ironfootnz 1y ago@Sean you can try this https://x.com/unslothai/status/1956027720288366883?s=46&t=Bog9IjoEPWsRNL5RT8LrkQ https://x.com/unslothai/status/1956027720288366883?s=46&t=Bo...
- kragen 1y agoI feel like such a 2-million-parameter model might make a much better keyboard autocorrect or IDE autocomplete than what I'm using now.
- business_liveit 1y agoOn a laptop, you can fine-tune or train small transformer models or classical ML models, but large-scale deep learning models require GPUs, hours, or cloud resources.
- Sharon_Q 1y agoIt is difficult to make it small and powerful at the same time. Unless there is a major technological breakthrough, the L3 to L5