Nvidia agrees to acquire Hugging Face for $13B
https://www.businessinsider.com/nvidia-in-talks-to-buy-hugging-face-13-billion-dollars-2026-8Don't expect things to go differently this time around. Nvidia wants control over the software stack. Acquiring HF fits in perfectly. The play is long term.
They even share many of their pre-training and even post-training datasets for Nemotron on HuggingFace; for example: https://huggingface.co/datasets/nvidia/Nemotron-Post-Trainin...
Which other lab shares this?
Yes, there is no question NVIDIA wants to lock you into CUDA and their hardware. But also, they’ve consistently demonstrated the most openness when it comes to model training, datasets, and research; even before the LLM era (e.g. StyleGAN).
There’s also modelscope.cn (china’s huggingface) which is worth checking out. I would not be surprised if one day, we have to use China VPNs to download open weight models.
Of course they are. They're commoditizing their complement.
I want to own the hardware, not play around in an nvidia fiefdom full of nvidia rules.
Nvidia probably likes that ASML is a monopolist (it's called a monopsony). The price is high and Nvidia can't scale as hard as they want to (more general: capital intensive market). This monopsony makes sure that other chip companies can't rapidly scale up and try to beat Nvidia. That there only ever was one other GPU firm (I'm prehistoric; once there were more) and they bungled it on software, is pretty sweet for Nvidia.
On the side of their customers. It would be best for Nvidia if there is free competition for the outputs generated from there GPUs. This maximizes consumer surplus and thus demand. Maximum demand for tokens, is maximal demand feeded in their monopoly. If there is a monopoly right from you, demand is curtailed, and your value is limited.
And that exactly is why you see interest from token generators for chips. Bridge that moat and gain a larger value surplus. Both NVidia and, say, China actively undercutting the token-supplier value chain is quite interesting to watch. It's like the Opium wars with us as somewhat happy customers.
In this same vein, why isn't ASML raising thousands of billions for building their own (subsidiary) chip foundries, while raising prices and starving the market (a little) for their machines.
If you don't want to use CUDA, they expose the PTX bindings to write your own CUDA alternative too: https://docs.nvidia.com/cuda/parallel-thread-execution/index...
Also, when running OpenCL, NVIDIA hardware disables multiple DMA engines, and allows only one memory transfer at a time to prevent OpenCL running as fast as CUDA.
Did NVIDIA finally allow open source drivers to access all parts and features of the card to allow feature parity? Last time I checked they were considering a plan for planning a solution to that.
I don't think bits like the GSP firmware will ever be open-sourced, but the opportunity to write better OpenCL drivers has always existed. Some of Nvidia's other proprietary driver backends (eg. GBM, Vulkan) are also decently neglected, but mostly out of disuse rather than malice. I don't think any of these things mean you don't own the hardware.
Evidently we should, because Linus has been more positive about Nvidia in the last 2 years [0]. I've been using the open driver for years now, for both gaming and CUDA.
[0] https://binarymusings.org/posts/talks/linus-on-ai-linux-in-k...
> This is actually one of the benefits brought by AI; it has made Nvidia a good participant in the Linux kernel space. [...] Now, when Linux is so important for AI clouds, Nvidia suddenly cares very much about Linux.
They're trying to mix up the competitive landscape(that doesn't impact their bottom line, and I don't think opensource is eating their lunch), so I don't think this is fake, at least that's my initial take.
Modular on the other hand creates the Mojo compiler gets criticised for not open sourcing it immediately and now once they do, no-one cares anymore.
Huggingface was not just a target for open source, but as a force to have open weight models run better on Nvidia against the rest.