Comment by maz1b
3 hours ago
Didn't they just recently invest in them? Curious about their strategy, considering NVIDIA already does Nemotron and i think diffusion models as well.
3 hours ago
Didn't they just recently invest in them? Curious about their strategy, considering NVIDIA already does Nemotron and i think diffusion models as well.
Their strategy is to prevent open models from proliferating, so their massive investments in these AI frauds are not completely unwound.
That's my take, at least.
Nemotron is TERRIBLE, and purposefully so. It must be.
They cannot be THAT BAD at training AI models. I don't believe it.
I think almost the exact opposite. They want open models. Without credible open models, they only have a few customers, and those customers have leverage against nvidia. With open models, they have tons of customers, and nvidia has all the leverage.
Smaller customers are also less able to develop their own hardware and threaten NVidia's business.
Sure but it also means these companies nvidia buys don’t work with AMD and other competitors anymore. There are myriad motivations and it’s not all one or the other but this is a classic component of Silicon Valley acquisition strategies.
They want LEVERAGE.
They can't ride the hyperscaler gravy train forever; at some point between Google, AMD, and Apple NVIDIA is going to lose its monopoly on serving large customers.
At that point, it would be useful if a few open models existed which were only a couple of months behind the frontier.
But it's very important that the open models never be TOO good, because the AI companies are buying compute on the assumption that their software will add value. If it becomes a commodity business with frontier open models, NVIDIA won't be able to get away with such a crazy markup.
1 reply →
Have you used Nemotron-3.5-lightening? I don’t use it as much as Poolside’s (excellent!!) Laguna XS 2.1 6bit, but the new Nemotron model is good.
I think NVIDIA does want small open models running on-prem to explode as a market! Lots of smaller GPU installations for companies who wisely want on-prem inference.
Of course NVIDIA will also keep making a ton of money selling to hyper scalers, but not forever: Chinese chips are getting better, Google, Microsoft, Amazon, etc. designing their own inference chips.
NVIDIA is handling this brilliantly.
I think this is a classic [Commoditize Your Complement](https://news.ycombinator.com/item?id=17047348) - Nvidia wants open models because their business is hardware and it's complement is AI models, so they want AI models to be commoditized so that hardware is the industry with leverage. OpenAI/Anthropic/etc want closed models so that the AI model development/data has leverage over the hardware providers.
Nvidia is trying to increase its customer base. Look at the Mag 7, Amazon, Google, Microsoft, and even Meta are all working on their own inference chips. I don't think they can completely ditch Nvidia for LLM training, but they can make their own chips for inference. I believe that's also why OpenAI made its own.
No one wants to pay the Nvidia tax
If you make your own chip don't you need to make your own software stack too. Which is what I thought kept everyone using Nvidia.
I believe NVIDIA's long term strategy will be to pivot from the data center to the public, and the public will utilize open models on NVIDIA hardware at home. This will come after the RAMpocalypse completes (when the new fabrication plants (China, Tesla/SpaceXAI/Intel) fully ramp up and start selling their RAM for cheap in the next few years). Data centers will be for training mostly.
There’s a lot about this that would make sense.
But, not really at the current technologies. Kimi and GLM are fucking awesome, but I don’t have 3TB of VRAM to run them, and I don’t expect to even when ram prices drop.
So now you’re back to the scaling issue before talking about power and compute distribution.
Why do you think the public wants to self host models over using a cheaper solution hosted in the cloud?
2 replies →
I doubt Nvidia wants to be fully dependent on the success of two highly unprofitable companies that could implode at any moment. It makes much more sense for them to commoditize LLMs so that their target market grows to every mid-sized or larger company.
Maybe as an LLM it is, but I constantly use their streaming ASR model (called Nemotron Streaming) through Handy and it works wonderfully well.
The other way. Nvidia would love open source jevon paradoxed ai - that would run inference on their chips.
But their goal would be to ensure it only runs on their chips, and not any competitors. I can't see how they could do that if the best models truely were "Open".
I can see one of Nvidia's biggest fears is the inference hardware becoming commoditised.
can someone weigh in on this. what's the actual play here
are they actually suppressing the western open models?
china doesn't give a fuck either way
imo, they see the weakness emerging at the intersection of all the labs, everybody knew there was no moat, so they're gonna control its direction and basically tell the Jev guys what they want them to work on
3 replies →
Watch what they do and not what they say.
I think the idea that they’d purposely spend company time and resources making a bad model is an extraordinary claim, requiring extraordinary evidence. The more likely explanation is that they aren’t willing to distill from their own customers, so they are at a disadvantage.
Insane conspiracy theory. There's no incentive whatsoever for Nvidia to release weak models. If you bothered to pay any attention, they are aggressively trying to catch up. Whether they succeed, that's of course a separate question.
why would Nvidia try to compete against their biggest customers - openai and anthropic ?
4 replies →
Yes, NV took $800M of a $2B round in Reflection.ai.
NV's long-term strategic incentive in funding a semi-open model provider like Reflection.ai is to ensure competitive frontier models which fully leverage the NV proprietary stack (chips, interconnects, servers, CUDA) continue to be widely available and continue to offer performance worth a higher price to the most profitable market segments.
NV's ~75% margins on hardware(!) at >$100B/yr scale are historically unprecedented and still increasing, creating tectonic pressure on NV's largest customers (hyperscalers and frontier labs) to escape the "NV Tax" by gaining access to competitive frontier chips, servers, and/or middleware at lower margins. Why would NV help create semi-open models that threaten their best customers? Because as Jeff Bezos famously said, "Your margin is my opportunity" and that's turning NV's biggest customers into their largest existential threat.
At the moment, NV's moats blocking significant competition are almost unimaginably deep, but on a decadal time-scale, literal trillions of dollars are at stake. That's enough to get people thinking the unthinkable, making NV the biggest target in modern business history. It's to the point that it's almost "Everyone against NVidia" which is forcing all the big companies into playing 3D strategic chess on multiple time horizons at once, simultaneously working with, investing in and hedging against each other. It's a 'co-opetition' (https://en.wikipedia.org/wiki/Coopetition) race where the smaller players are grouping into tactical alliances and uneasy truces while the biggest players are spending billions to 'commoditize their complements' (https://gwern.net/complement) as NV is doing with Reflection.ai. This can create strange bedfellows overnight. I wouldn't be surprised to see some of NV's biggest customers, who compete fiercely against each other, pooling resources with NV's competitors to create a viable alternative to NV. This is the stuff Jensen has nightmares about, waking up in a cold sweat in his black leather pajamas.
NV doesn't need Reflection to be better than the best models or even be profitable. They just need to ensure a viable alternative to frontier lab's proprietary models: A. Remains widely available at low enough cost for all NV's other customers to buy, B. 'Works best on NVidia', and C. Stays close enough in price/perf to prevent any single proprietary model becoming as dominant in models as NV is in hardware. The Chinese semi-open models have been strategically convenient for NV but it'd be foolish to count on the Chinese govt continuing to subsidize them or Chinese models not getting blocked or limited by some governments. If it only costs a few billion, Reflection.ai being "good enough," especially for a semi-open, (near-)free, US-based model, is a cheap strategic hedge against long-term threats to NV's (near-)monopoly, especially when Jensen has trouble finding room to store the mountains of cash NV is piling up. When your margins are ~75%, there's literally no better place to put money except toward extending your dominance.
It's really hard to know what's keeping you ahead in a field that's constantly innovating.