Nvidia's been pretty terrible for open source / free software. No need to quote Linus Torvalds here. They want to control what runs on their hardware. They want to you write code against their proprietary drivers and APIs, not directly against the hardware (which these days of course also contains plenty of software, but still).
Don'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.
NVIDIA is one of the most open labs.
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.
> NVIDIA is one of the most open labs.
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.
I'll expand on the economics somewhat. My intuition is that you can like a moat left of you in the supply chain, but dislike moats to the right of you. (I'm using left and right as I picture this horizontally drawn. It is usually called vertical integration by economists.) But free competition in your market is worst of all. Free competition right outside your moat is pretty sweet, and that's were parent is commenting on.
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.
While nvidia 's drivers are closed source there's enough interest in running LLM's that you are not locked in using nvidia. Strix Halo chips have been around before dgx spark came out and deliver very similar performance.
You do own the hardware. Nvidia GPUs have FOSS driver alternatives (Nouveau, NOVA) and have supported OpenCL on the proprietary drivers since 2009.
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...
When last looked at it, NVIDIA was not supporting OpenCL beyond 1.0.
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.
People love to blame NVidia on OpenCL failure, maybe Intel and AMD should actually deliver something compute researchers want to use?
OpenCL up to 3.0 is ostensibly supported: https://developer.nvidia.com/blog/nvidia-is-now-opencl-3-0-c...
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.
I wish OpenCL was usable today like your implying...
Yeah, OpenCL fandom always blames NVidia instead of Intel and AMD for doing a shitty work.
nothing stopping you from doing that
Are you joking? They just bought HF for $13bill and didn’t break a sweat. They are dominant and spreading.
Can't buy a decent modern Intel gpu to run AI on because of nividias "donation"
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Nvidia has closed drivers, which makes them light years behind AMD in trust.
Also if we were just discussing labs, Ai2 opens ~everything with dramatically less resources than Nvidia.
> NVIDIA is one of the most open labs.
Nvidia’s history with linux shows the opposite. And as a user running models on a linux/AMD stack, this information does not fill me with hope.
_why_ have people started to call companies “labs”?!
Because making new AI models is a research activity, so the group within Nvidia (or other companies) that does that activity is called a research lab, or just "lab" for short. I don't think anyone is saying that all of Nvidia is a lab (that's just shorthand I guess).
I'm with Torvalds here, fuck you Nvidia.
> NVIDIA is one of the most open labs.
Dude, where's the src for GPU drivers and the firmware blobs?
Sorry, you can't say it is one of the most open labs without qualifying a proper response to the question above.
"Labs" here specifically means "AI/LLM model lab", which that part of Nvidia is unquestionably one of the most open.
Nvidia is also one of the most closed hardware developers around. Two things can be true.
Then this whole comment thread was a diversion though, as it was specifically about the hardware parts and not model training.
"The proprietary shovel seller has some excellent tutorials on how to dig gold. Nobody else has such good step by step guides. Therefore them buying a shovel-agnostic tutorials and techniques method (that has a lot of info on using other shovels effectively) is justified."
They are sharing stuff that makes you consume their stuff. Their position with big ai companies is always vulnerable (asics/direct tsmc relations)
> They are sharing stuff that makes you consume their stuff
No shit Sherlock. Name one company that shared their code to make you NOT to consume their stuff?
NVIDIA is a lot more than a “lab”.
lol “Everything you said is correct but you’re wrong because I’m thinking about something other than what you were talking about”
CUDA is proprietary for a pretty understandable reason. There's no good way for Nvidia to standardize it.
They supported OpenCL when Khronos floated the idea of a GPGPU standard to manufacturers, but OEMs didn't want to design scalable hardware or sponsor the software.
>No need to quote Linus Torvalds here
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...
He definitely sounds more pragmatic than before, which is, in a sense, more positive.
> 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.
Nvidia releases some of the most open open weights models, Nemotron 3, which have the full training code open, and most but not all of the training datasets.
Nvidia is a big company. They are good about some things and bad about others.
I think they really do like open weights because they make some of the best hardware for training, and the more open weights models there are, the more people are training and fine-tuning them, mostly on Nvidia hardware.
I feel like Nvidia is one of the better choices for buying Huggingface. Not perfect, but definitely far from the worst.
The worry is not that Nvidia isn’t open about their models.
The worry is that Nvidia is trying to control the way you run those models. Trying to bake CUDA assumptions into model design, and pushing the software ecosystem to be as Nvidia first as they can.
What would be the worst three?
My pick:
1. Oracle
2. A16Z
3. GameStop
As a AMD GPU user, I feel as if trying to mandate CUDA only or some other way to increase NVIDIA lock in like that would be met with people leaving the platform or at lest simply working on the non NVIDIA formats away from hugging face. Though requiring a lot of space it's quite easy to spin up a competitor at least for hosting weights. And if they try some legal shenanigans then many countries outside the US will still be happy to host I am sure why not China?
The only thing I can see them being able to get away with is increasingly bending the hugging face python API and any other features of that sort they develop to NVIDIA only. I personally don't use that and don't see a reason to and I am not sure how many people do use the hugging face python library.
I think this is part of an open-source play. I'm not arguing your other points, I think they're true.
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.
As opposed to whom else? Do you think any of the other labs or acccelerators are clamoring for open source hardware?
Nvidia has hardly open sourced the nvcc compiler, yet no-one here is complaining about why it is closed source.
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.
> as a force to have open weight models run better on Nvidia against the rest.
this is the crux - if nvidia makes it so that open weights end up running better on nvidia hardware than competitor's, then it's going to prevent hardware innovation and competitiveness in the entire sector.
It's like as tho General Motors buys out oil refinery to make gas for all, but the gas somehow runs smoother in GM cars.
If you don't own the customer relationship, you don't own the profit. See App store, FB.
NVidia is delivering commodity hardware to the hyperscalers, and would eventually get commodity margins (when hyperscalers make their models work on their own hardware).
Amazing article on relevant economics of squeezing vendors - actually about antitrust ad-models but:
https://www.thediff.co/archive/ad-supported-platforms-are-a-...Perhaps not relevant to huggingface, sorry.
It's a clever move if that's what they're doing. They're restricted in China, and are likely to face stiff competition from Chinese chipmakers in the coming years. Acquiring the largest repository of trainable models and ensuring they run better on Nvidia hardware is probably one of the few moves they have for keeping ahead of the competition. I mean, it would be terrible for the consumer, but it does make me think that NVDA is a decent investment.
I assume it's because nvidia is a hardware company that produces world class hardware, and it's a compiler to target that.
Whereas mojo is a general purpose language, and we're absolutely spoiled for choice on modern languages with open source compilers.
I'm not saying it's fair or right, I still think mojo is neat, but isn't exactly comparing apples to apples.
We can stop with these weak excuses since AMD and Intel have done more for open source than Nvidia has, including their GPU drivers for Linux.
Nvidia on the other hand has not and the best they have done is a bunch of closed-source blobs which they do more closed source releases than the rest.
Mojo is open source and targets all GPU architectures for their compiler regardless of the vendor and nvcc targets their own (and both that and CUDA are closed source).
So this is directly an apples to apples comparison.
I'll give credit to Intel, they've always been open source friendly and I prefer to buy them because I'm not a gamer, so don't need the best.
AMD is another story. Their binary closed source blobs were even worse than nvidias for many years. So much so people used terrible performance open source tries just to avoid the headache. That they finally slopped together an open source version after they'd lost the GPU race isn't exactly noble. But as an open source supporter in general, I commend the effort still.
> no-one cares anymore.
possibly because it took qualcomm buying them to make that happen.
Just say you don't know.
Mojo was partially open source before Qualcomm bought them, and they were going to do open source it anyway.
Was NVCC or CUDA ever open source since the lifetime of its development?
> they were going to do open source it anyway.
uh huh.
> Was NVCC or CUDA ever open source since the lifetime of its development?
you ever ask Nvidia why? i did.
I for one welcome our new robot overlords.
I think it's a plausible play that keeps the AI boom in session for a year or two longer.
Or at least, $13b to stay at the head of the race (or keep the race running) must be worth it to someone's desk.
There's only $50b in datacenter buildout nationally (Source: Gemini, 2026).
So it is a bit of a puzzling choice for what amounts to a pile of software, in my opinion. but I don't know shit.
Did you just quote an LLM as a citation? That's not how citations work.
"That's not how citations work." (Dude on the interwebz, 2026)
But more seriously, this is my first time seeing that as well, and I'm not sure I like it. Citing an LLM is a little like citing Wikipedia to me, you cite the primary source the LLM is quoting directly, not the secondary source.
A Wikipedia link can be nice when you get several different sources provided.
It’s the formatting and formality that bother me. Say “I looked it up with Gemini,” don’t give me some weak attempt at “proper citation” to legitimize the bare minimum effort you put in.
Why waste keystrokes when less do trick?
Even worse is that Gemini's knowledge cutoff is still Jan '25.
2026 equivalent of “my Mama told me”
> There's only $50b in datacenter buildout nationally (Source: Gemini, 2026).
Your reference lacks authority, veracity, and reproducibility.
It came to me in a dream.
They’re buying a brand, some employees, and some momentum — not any of their software. HF probably does have propriety goodies to make it all run efficiently, but certainly not a billion dollars worth, much less 13!
We are so used to these numbers being thrown around in the AI era that something one needs a reminder that this is 13 billion, not million. Insane exit by HF.
80x its yearly revenue. Clearly they aren't buying HF for margins.
In fact, has HF ever wished to make money? They probably pay AWS more infra cost than they earn. The exit was planned all along.
Was looking for this comment, the tech world has gotten completely insane with ”valuations”. I would love to hear why it was 13 and not 5. It would still be completely insane at 5 billion, but someone though they should add another 8…
Yeah, the price is weird as fuck.
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