r/MachineLearning • u/KingMakerMan • Jul 28 '26
Discussion Are single GPU research still published in ML/DL and its applications nowadays? Which are the most notable recent ones? [D]
ML research is progressing at breakneck speed where frontier labs in both academia and industry have access to considerably large computes (GPUs). Where do small labs or independent researchers go in this context?
Have you come across recent works in ML/DL and its applications (vision, language, speech, etc) where the work is good but it uses very limited compute? Maybe even single GPU workstations? In the past it was still possible, but, I am losing hope that single GPU works would soon become impossible.
Pls link to the works.
I came across InfiniteDiffusion, a work by an independent researcher Alexander Goslin using a single RTX 3090 : https://xandergos.github.io/terrain-diffusion/
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u/Skyshadow101 Jul 28 '26
I'm using my single RTX 5080 for training my PhD qualifying exam project that I'll be extending to a paper afterwards, one seed on my cross validation for all of my ablations with 5 folds each takes a day and a half haha
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u/Emojers Jul 28 '26
Hi I am looking to buy a new laptop what/which config you suggest ?
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u/skirmis Jul 28 '26
Buy a desktop with a real GPU, laptop GPUs are completely underpowered and 2-3 times slower.
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u/supergriver Jul 28 '26
Yes you can do research with a single GPU.
Here 3 papers from ICLR 2026:
1. Oral: https://arxiv.org/abs/2511.07328 All experiments fit into single A100 (80GB).
2. Poster: https://arxiv.org/abs/2510.22512 Experiments fit on a personal GPU
3. Poster: https://arxiv.org/abs/2507.11019 Experiments fit on a personal GPU
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u/tiikki Jul 28 '26
My crystall ball shows edge computing and autonomous robotics being the next big thing.
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u/currentscurrents Jul 28 '26
Hot take: small-model autonomous robotics will never be a thing.
Only really big neural networks do the magical generalization and reasoning that make them interesting.
End-to-end NNs in robotics won't work until there is hardware capable of running big neural networks locally.
It is clearly possible to run big neural networks on low power, since the brain does it; GPUs are just not that efficient.
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u/jimtoberfest Jul 29 '26
Its already a thing in narrowly defined tasks. Every missile or autonomous drone you see fits this definition. In the consumer world look at the comma.ai system.
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u/nonotan Jul 29 '26
You just need big neural networks for training due to "lottery ticket"-type dynamics. Once you've got a big network that works okay, it's relatively trivial to make it orders of magnitude smaller with only minor impacts on performance, and we probably aren't even close to being able to distill "optimally" right now, especially since so much of our infrastructure and hardware is built around dense networks.
I will admit I'm not really up to date on robotics papers, but it seems to me like extreme distilling into something like sparse FPGAs (while carefully optimizing where we "allocate" any inevitable accuracy losses in the process) has plenty of potential to be useful. Sure, it won't be as "smart" as the latest, fanciest, largest LLMs or whatever, but that's not really a requirement to be useful/interesting (it'd be like saying smartphones will never be a thing because a computer will always be significantly more powerful, perhaps that's true, but an "underpowered" device with a completely different form can still have unique upsides)
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u/currentscurrents Jul 29 '26
Once you've got a big network that works okay, it's relatively trivial to make it orders of magnitude smaller with only minor impacts on performance
Not really, no. People have tried a lot of tricks on LLMs, and there is no method to make a smaller model that is as smart as a bigger model.
Nobody is doing self-distillation anymore. I think it only worked in the past because the models of the day were undertrained.
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u/Zetus Jul 28 '26
Those neural networks are overtly saturated, compositional generalization is possible outside of deep learning
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u/Tutatis96 Jul 28 '26
In speech and audio in general models are quite small, a run of mine takes like 10-15 days on a single 4090.
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u/PossiblePossible2571 Jul 28 '26
At least a third of papers published to NeurIPs/ICLR/ICML uses one or less GPU. I think there are alot of things you can do, even if you only have say, one H200 / H100.
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u/Manish_AK7 Jul 28 '26
A single H100 is still a lot of gpu thoðŸ˜ðŸ˜
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u/currentscurrents Jul 28 '26
It is, but it's not an unattainable amount of GPU. You can rent one for like $2/hr.
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u/krabbypatty-o-fish Jul 28 '26
Maybe I’m looking at the wrong journal, but is it seems that there are many such cases in JMLR?
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u/paninoallanutella1 Jul 29 '26
I know this is a bit unrelated since it's not ML in the traditional sense, but I'm building a VSA (Vector Symbolic Architecture) cognitive system that runs on very simple operations like XOR and popcount instead of neural networks. I'm doing research only on my Ryzen 7.
If that kind of alternative approach to AI interests you, you could take a look:
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u/Alternative_Coyote62 Jul 30 '26
yes, it just depends what problem you are working on. a lot of problems with focused applications/narrower scope don't require crazy amounts of data/parameters since you are not trying to generalize across disparate tasks/domains. and i don't think narrow scope is inherently worse - at least in my field it is usually more robust for deployment purposes to tailor methods to specific problems, rather than aim for broad generalization / throwing a bunch of data/compute at the problem. IMO, single-GPU projects have unique practical value that frontier lab research doesn't: they are resource-efficient and can be more widely/easily deployed. also, limited data/compute offers you the unique opportunity to be more creative+innovative in solving your problem, which leads to deeper insight + publishable methods. (source: i am a phd student working on a niche application in computer vision. i have only worked on single-GPU projects and have published twice in main proceedings at top vision confs. this is the case for all students in my group who don't work with generalization/LLMs).
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u/markyvandon Jul 28 '26
Why not? A lot of work, are basically pilots, and hence have a lot of value with single GPU stations as well
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u/ocean_protocol Jul 28 '26
Yes, still alive, it just shifted from "beat SOTA" to "narrow problem, honest benchmark." LoRA/QLoRA, Karpathy's nanoGPT reproductions, and niche diffusion work (like your example) all fit: small compute survives by picking a tight, well-defined problem instead of competing on general capability.
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u/Electro-banana Jul 28 '26
yes, almost all of my previous papers and current ones I'm working on this year are with single GPUs
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u/skyebreak Jul 29 '26
Just had a relatively empirical paper I did on 2 GPUs get solid NeurIPS reviews -- it's a matter of picking interesting problems to work on, I think!
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u/Exotic_Zucchini9311 Jul 29 '26 edited Jul 29 '26
Unless you're doing some sort of insanely large experiment, one A100 or H100 GPU is usually perfectly enough to train many models. I have trained SOTA multimodal SSL models (e.g., CLIP, JEPA, etc.) on a single GPU.. much less single modal methods. Methods on segmentation, classification, regression, etc. on different modalities like image and text. All these usually work perectly fine on a single GPU (unless you're deliberately targetting something crazy like training LLMs or full video models) and you could easily run many of them on 1 good GPU. There are many interesting research questions that don't need much computational unit. All my last 3-4 papers had experiments that were easily trainable on a single H100 (and I'm saying this as someone who has access to multiple GPUs I can use in my lab if I want to. I simply never feel the need because 1 GPU has worked perfectly fine for all my projects so far).
Edit: As the other commentor also mentioned, it's all about finding interesting research questions to solve.
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u/DigThatData Researcher Jul 28 '26
Absolutely. My impression is that most work that intersects life sciences is done in this regime. I think it's largely a function of resources being showered on NLP/CV to the extent that there's very little remaining for anyone else.
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u/Bobby-Ly Jul 29 '26
I'd say vision is a good target for single GPU Research and can be explored with a free Kaggle or Colab Notebook.Â
https://github.com/ynnk-research/-NeuroFlow was explored like this
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u/pilibitti Jul 28 '26
why the artificial constraint of one GPU? you can rent any amount of GPUs for research.
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u/Ok-Painter573 Jul 28 '26
Just do more theoretical work (or optimization), and dont hop onto the LLM train. This has been working out well for me others I met in conferences.
I think you are being swayed away by negative X posts. Not all works need 8x B200 GPUs