They have various paid services that relate to AI—paid inference hosting; paid accounts aimed at AI development with GPU rentals [credits plus paid overages, I believe], more private storage and public storage than free accounts; and many of the community a and some other benefits on individual/team/enterprise tiers; additional paid storage above the base quotas for the paid account tiers; on demand rentals of HF managed containers on GCP and AWS, and some other things.
I’m asking more to work out what is the basis of this valuation. Why would Nvidia spend $13B for what seems to be a services that gives things away for free.
I dunno. You can argue over whether they're overpaying, but it's not like Huggingface is Clinkle. They hit $150 million in ARR this year, they have tons of runway, and according to reports, have just started to even burn the money they raised a few years ago.
I get that it's fun to be glib about the stupidity of tech elites and investors in general, but Huggingface have been pretty open about their financials and are, in my opinion as a practitioner in the field, one of the most responsible orgs in our space. They've been a pillar of open source ML for years now and have made a very positive impact on our ecosystem.
Nvidia is getting a real business generating revenue, and the center of the universe for open models. Both seem like pretty valuable attributes, from Nvidia's perspective.
Solid point. I'm sure Nvidia went into this deal expecting completely flat growth and no other benefits to their core business. Sorta like how Meta never increased Instagram's revenue from $0 and is still waiting for it to pay off that billion dollar acquisition price.
Or like GitHub, which was generating something like 200 million in ARR and had never hit profitability when Microsoft bought it for $7.5 billion back in 2018. I'm sure it has come as nothing but a happy surprise to Microsoft that GitHub generated $1 billion in 2023. They had initially penciled it in for 38 years til ROI.
With all the spending and valuations in the AI space being so reasonable and grounded in reality, sure. Huggingface will surely be exactly like GitHub and Instagram with their extremely low and stable per-user overhead, and large, extremely dedicated user bases.
I just feel like you're saying things based on a general vibe about "AI companies" but didn't pause to look up the particular company being discussed.
Huggingface reached profitability 2 years ago. They've reportedly just started to touch the cash they raised 3 years ago. They have a tiered pricing model with metered pricing on resource heavy services. Seems pretty stable?
Your point about their user base is even weirder. They play essentially the same role for the ML community that GitHub does for software engineers, so I mean, yeah I'd pretty much expect their relationship with users to be "exactly like" GitHub's for the most part? They're where everyone has published their models for the last 6 years at least, going back to pre ChatGPT and the recent AI boom. There aren't realistically any other major model hubs, certainly not with anything near their footprint.
You’re right. I didn’t really dig into huggingface specifically even though they’re obviously different from the examples I used in my mental model. I’m not going to say I’m wrong because I haven’t personally looked into it, and regardless of HF’s position in it, this industry regularly squeezes out enough bullshit to smother an active volcano. That said, I was clearly speaking glibly using likely flawed references.
Basic napkin math: 5% IRR means they'd only need to 4.5x their revenue to make this roughly work. If they can finance this cheaper and/or do not have better options for their cash, it's even less.
It's not too dissimilar from GitHub, but geared towards ML. They have a 9/mo pro plan for individual users for upgraded storage/usage, and an enterprise version of Hub that larger orgs can pay for. I think the enterprise has some contract minimum + 50/mo per seat. https://huggingface.co/pro
They also have inference endpoints with metered prices, and their spaces product (though i'd imagine this is a smaller portion of revenue).
I’d be curious to see how much that’s subsidized. Maybe they’re different, but when I see ML and monthly plan in the same sentence, I see zero sustainability.
Why I don’t think it’s sustainable to run a compute-intensive business on low monthly plans? If they’re like OpenAI and Anthropic, monthly plan users can spend many times the amount of money in a month than they pay, and unlike regular lower-compute SaaS businesses, overhead increases significantly with usage. That means the more users many of these services get, the more money they lose. Some surmise OpenAI was hiding the actual cost in marketing expenses to make their business look less unprofitable. Anthropic was smart enough to focus on customers most likely to be willing to pay for API pricing, so they’re in a better position. If hugging face is primarily focused on selling monthly accounts rather than token based billing which bills more as the company’s expenses increase, its not likely to ever be sustainable without significant changes.
I think maybe just click around Huggingface's site a bit? They don't run a compute-intensive business on low monthly plans. You're describing frontier labs that sell access to their enormous models with subsidized subscriptions, but that's just an entirely different company/model than Huggingface. They've been around in their current form since around 2018. They provide a GitHub like service for hosting and sharing models primarily, and they also provide infra for optimized compute (for training and inference) that you can purchase through them, but you pay as you go for the compute and they have their premium baked into the price.
Most of HF's revenue comes from their enterprise customers; you essentially get direct access to their MLEs (Slack) as well as their software stack, hence the per-seat cost and annual minimum contract price. In that sense they are not particularly "compute-heavy" in terms of what they "really" sell.
Yeah the people that say things like this aren’t looking for valuable companies, or to create value, or for sustainability — they’re looking for short-term growth.
Nvidia has a market cap of $5T USD today, and a decent chunk of that is due to LLM speculation.
Does Nvidia want their stock price to be at risk of being tanked by a download service being in the news? No, they want to make sure the party keeps going and is under their direct supervision, and part of that is making sure Hugging Face isn't bought by a competitor or runs out of money.
If their stock moves up more than third of percent due to this they have won... The math at these valuations get somewhat extreme. But there is some logic in it.
There is a brand and that brand is really a type of call option that is very hard to price.
If you are doing the valuation using the tools of Ben Graham it is not really going to work.
Damodaran's the Dark Side of Valuation is the text for this.
Absolutely none of this makes sense and the thing that amazes me is how long it has continued. Future historians will just laugh at how stupid and obvious the crash was.
Yeah, great point. Nvidia could potentially be overpaying. Not sure how that equates to Huggingface being "a file download mirror with a couple of side features dangling off".
While it's probably easier to say this in retrospect, they were eliminating a direct competitor to their core business. Something so advantageous it should have been blocked by regulators.
I can't see this as being as good of a purchase, especially when it's 13x the price of what was seen as an absurdly large amount back then.
I guess we may have to start paying to download models.
Or perhaps they will start throttling downloads for free users.
I don't know what they business case is, it might be to shut them down: I suspect good free models on local hardware is a threat to Nvidia's investments in OpenAI/Anthropic.
NVidia has been expending energy helping improve local models and inference platforms for them targeting NVidia GPUs; good free models that users can run locally rewards Nvidia’s investment in product lines for local inference (DGX, RTX PCs, etc), as well as—given their continued dominance in the space—the premium over competitors of their consumer and workstation GPUs.
Maybe they're trying to see how big the market actually is before considering shutting it down, maybe or getting lawyers and politicians to try and outlaw or restrict open models if they see a big enough opportunity.
