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Nvidia has been in talks to acquire Hugging Face for more than $13 billion
(www.businessinsider.com)
this post was submitted on 27 Aug 2026
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Technology
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Sounds like there's a market for a hugging face comeptitor.
Nvidia is seeing the writing on the wall that local models are the only viable future for AI. They have to try and squash that now before it's too late. I mean, it is already is too late, as they've hooked there wagons to these AI companies, and that shit's going to end and nividia is going to be a bag holder. I can't wait.
NVidia wins if people run local models on their hardware too. Hugging face is not a competitor, it is an enabler.
Maybe, but CPUs are coming out with LLM tuned chips, and I can run a basic model on an i5, 8gb ram, and no dedicated card. It's not super powerful, but for most users, it's more than enough for what they use the big models for. Also, if it does take a discrete card to get that needed boost in performance, then at least consumers would be able to get GPUs again.
I think as hardware improve and is further designed around LLM efficiency, and local models are tuned for specific uses and being able to run on lesser hardware, it will make Nvidia obsolete for large swaths of the population. A good GPU will still be necessary for high performance, graphic/physics intense gaming, but that's a really small subset of all users.
Hopefully Nvidia just shits and has to grovel back to the consumer to get there marketshare back when all of the DCs go tits up.
Bitcoin went through a similar trajectory. Graphic cards to specualty chips.
AI won't really do that, because it's a very different workload.
Mining Bitcoin needs very little memory but extremely complex maths calculations. It has a built-in difficulty mechanism that makes the math more difficult if mining goes too fast.
ASICs are unbeatable when it comes to super-fast pure calculation.
AI work is mostly memory-bound. You need huge amounts of RAM (24GB for a somewhat decent model, a few TB for frontier models) and then you perform pretty simple vector math on that huge amount of data in memory. So the main limiting factors are RAM size and bandwidth. The computation power of a GPU doesn't matter that much. Also GPUs contain ASICs for vector math already.
Standalone ASICs have no advantage over GPUs for this workload. The only thing you could do with a specialty chip is remove some unnecessary parts of a GPU to save a bit of cost and energy, thus turning a GPU that can be repurposed or resold if the AI bubble pops into a single-purpose device that has to be discarded if the bubble pops.
You bring up some good points. Just FYI my research was specifically on ai, but a very specific branch in college. So not llm but only somewhat llm flavored.
The are already including ai chips in consumer hardware. And your thinking of llm specific limitations. But the smaller models can absolutly be thrown into hardware. Its just the algorithms are moving so fast that the hardware needs to be flexible enough. Thars the biggest reason we dont see more hardware faster than gpus. Hooe that makes sense!