Hi everyone, I did research about how much money is spent to Open-Source models versus closed-source. Firstly I’ve parsed how many models have been released to huggingface, made differentiation (base models vs adapters and fine-tunes) and types (LLM / diffusors) I have a formula by which I firstly tried to calculate how much FLOP is needed to train a model, and basically formula is 23N*D (where N – number of parameters and D – tokens in dataset) Then I take some GPU model, like H100 get it’s capacity in FLOP/S and calculated how much time is needed to train some model from scratch. Then I’ve multiplied amount of time to rent price of GPU per hour and get final result In case of adapters I’ve just assumed that it’s 1% re-training from original weights. So it’s looked like OS models is only 3.2% market share from whole AI training spent per year, it’s about 250M$ spent on each segment (text LLM vs diffusors) and only 56M$ is spent on adapters. Now come the question – how do you think, people who train their models – are they rent their hardware in cloud or build their own datacenters? I had a dissuccion with some friend of mine and he is sure that everybody just bought their own hardware (Like Elon Musk), train their models and then use same hardware for inference. The problem is – it doesn’t actually look…profitable? I mean if you wanna train 70b model from scratch then it would cost around 1.6-2.1M$, but if you decide to buy your own hardware to train model it would cost around 30M$, which is x15 from cloud prices. And as we all know, that inference is low-margin business it just doesn’t seem like they are gonna break even. I mean it would take at least 21 month with 100% utility to just break even. And the hardware a gonna become obsolete sooner then 21 month… It’s reminding me situation about bitcoin mining and ASIC’s from 10 years ago, but then if you bought a mining rig it will justify the paid price in about 3 month, not 21 month. So yeah, the reason why am I asking this – I am currently building P2P GPU marketplace with Crowdfunding feature, where you can start crowdfunding campaign to train new LLM or adapter and then spend collected money on the same marketplace renting GPUs for training or inference. My friend told me that market is to small. Only 0.6 billions is spend on open-source models in 2025 and all closed-source models is training on hardware belonging to developers itself (i.e. OpenAI do not rent resources from NVIDIA but built their own datacenter, and all big players are doing the same). And he said it’s just not possible to enter to this market, it’s already monopolized by big players. What are your thoughts about it? submitted by /u/0xBekket
Originally posted by u/0xBekket on r/ArtificialInteligence
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