141–150 of 340 posts
Platform co seems fitting, considering Nvidia's data center revenue in the fourth quarter of 2023 was a record $18.4 billion, which is 27% higher than the previous quarter and 409% higher than the previous year. Seems revenue from inference is growing at a significant clip.
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As someone who has used Ubuntu in the past and has now moved onto greener pastures, I appreciate everything Canonical and Ubuntu have done for the Linux community but there are many better options today and Canonical is already far from the company it once used to be.
Whenever I see an open job req for canonical I run for the hills.
Probably depends on the team and the worst ones have a lot of churn.
Like a lot of the commenters here, I have a problem with this headline. They don't "seek to become a platform company", as in making their own cloud platform where they rent GPU time, meaning they stop selling GPUs to other cloud platforms. That easy misinterpretation makes good clickbait, but no, that's not what the article says - the article has Huang bragging that CUDA already is a parallel computing platform, for…
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https://lambdalabs.com/gpu-workstations/vector
Heh Heh Heh "Ultimate AI Workstation". Pricing starts at US$83,549: https://shop.lambdalabs.com/gpu-workstations/vector/customiz... Adding every option only adds $2,100 to the price too (totalling $85,649). They should probably just include everything as standard. ;)
I haven't listened to Jensen speak before, but am I the only one who thought the presentation wasn't very polished? Not a knock on anything he has accomplished, just an observation that sorta surprised me
Don’t think anyone cares as long as the company keeps getting the bets right
Like a lot of the commenters here, I have a problem with this headline. They don't "seek to become a platform company", as in making their own cloud platform where they rent GPU time, meaning they stop selling GPUs to other cloud platforms. That easy misinterpretation makes good clickbait, but no, that's not what the article says - the article has Huang bragging that CUDA already is a parallel computing platform, for…
https://developer.nvidia.com/blog/nvidia-nim-offers-optimize...
so there is a push towards a platform company, but probably not well explained in this article.
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This seems as good a place as any to be Corrected by the Internet, so... correct me if I'm wrong. Making a graphics chip that is as good as Nvidia: Very difficult. Huge moat, huge effort, lots of barriers, lots of APIs, lot of experience, lots of decades of experience to overcome. Making something that can run a NN: Much, much easier. I'd guess, start-up level feasible. The math is much simpler. There's a lot of it,…
CUDA is a big reason for their moat. And that's not something you can build in a couple of years no matter how money you can throw on it. Without CUDA you have a chip that runs on premise without anyone having a clue how good that is which is supposedly what Google does. Your only offering is cloud services. As big as this is, corporations would want to build their own datacenters.
Zen 1 showed that absolute performance is not the end-all metric ( Zen lost on single-core performance vs Intel). A lot of people care for bang-for-buck metric. If AMD can squeak out good-enough drivers for cards with good-enough performance for a TCO[1] significantly lower than NVidia, they break Nvidia's current positive feedback cycle.
1. Initial cost and cooling - I imagine for AI data center usage, opex exceeds capex.
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How can 4 bits possibly be enough? Are intermediate calculations done at a higher width and then converted down back to FP4?
- Training isn’t done at 4-bits, to date this small size has only been for inference. - Research for a while now has been finding that smaller weights are surprisingly effective. It’s kind of a counterintuitive result, but one way to think about it is there are billions of weights working together. So taken as a whole you still have a large amount of information.
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Tell me more about why you believe their stock is hilariously overvalued.
No moat. Yes, CUDA, but CUDA is maaaaaybe a few tens of billion USD deep and a few (more) years wide. When the rest of the industry saw compute as a vanity market, that was sufficient. Now, it's a matter of time before margins go to, uhhh, less than 90%. Does that make shorting a good idea? I wouldn't count on it. The market can always remain irrational longer than you can remain solvent.
However, given that the nearest competitor AMD has basically given up on building a CUDA alternative, despite the fact that this could grow the company by literal trillions of dollars, I suspect the CUDA moat is much bigger than I give it credit for.
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And MS and everyone else have plenty of interest in helping AMD commodify CUDA compatibility.
It's so weird it's taking them so long, because as far as anyone can tell AMD is mostly competent enough to make GPUs within some percentage points of Nvidia, the "breadth of complexity" in what these things do at the end of the day is ... rather underwhelming, the software stack may appear to be changing all the time but is also distinctly JavaScript-frotend-esque... is there an insider that knows what the holdup is…
Looking at the chart for $AMD over the past 5 years gives plenty od reasons to be happy, and no reason to rebel. A rational AMD investor should not be Jonesing Nvidia's catching lightning in a bottle via crypto + AI. The Transformers paper was published a few months before AMD released Zen 1 chips - they did not have a lot of money for GPU R&D then.
The timing of the LLM-craze was very fortuitous for Nvidia.