Folks are concerned that nvidia won't support these efforts if it gets models running on competing hardware. Two responses:<p>- The projects started without HF/nvidia involvement and were massively successful BECAUSE folks want to run models on their own devices.<p>- With highly capable agents, "hard to implement" should be less of a barrier. The inference market should in some sense become more efficient, as agents should make it easier to transition between software and hardware solutions. Sure there might be less training data for integration platforms, but if we've learned anything in the past few weeks, it's that agents can be remarkably persistent.
While true, enshittification should be mourned every time it's renewed until some real kind of block (regulatory changes to the industry) exists.<p>This is like a company buying your local public pool, and you trying to convince everyone that things won't slowly turn to crap.
Translation: Gervanov's ggml.ai was acquired by Huggingface in Feb 2026, so he is now "excited about the journey" after the Huggingface acquisition by Nvidia.<p>Can we take this as an official statement that Nvidia supports local models?<p>Why would Nvidia increase GPU efficiency for local models? Surely they'll operate like athletes and only establish a new record from time to time when necessary.
Why would they not? The cost to do that is 0.000001% of whatever numbers they usually work with. The upside is that it creates a community of hobbyists, most of whom will us (consummer grade) Nvidia GPUs. Which are not a significant source of revenue for Nvidia anymore, but it’s nice to have a bit of insurance.
A company that calls Israel it's "second home" bought HuggingFace. I know how this story is going to end. I regret relying on HuggingFace and llama.cpp because of this acquisition. I'm actively exploring other options before this becomes part of another geopolitical arms race.