How Jensen Huang's Nvidia is powering the A.I. revolution
21–30 of 100 posts
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#22Earlier quoted context omitted.
Nvidia doesn't have a monopoly here. Google have their TPU, AMD have MI300, Intel have Data Centre GPU Max, and then there are start ups like Graphcore. There are many alternatives. I think people choose Nvidia because it's easier for them to use. That's because everyone else uses them, and that's because for years and years their competitors (other than Google perhaps) have dropped the ball on good software.
NVIDIA is the leader because most academic AI setups are NVIDIA based. When AI moves further away from academia NVIDIA will have less of a grip. Proprietary, hardware specific APIs never stand the test of time. Ask 3Dfx. Either CUDA will open up, if it is to survive or open API use will spread. Weirdly, NVIDIA hardware only outperforms competitors on its own API. When you compare NVIDIA on a level playing field, they…
> Either CUDA will open up, if it is to survive or open API use will spread.
CUDA won't die if Open APIs take over AI inferencing operations. It's still used and applied in so many niche industries that it can only be "replaced" in industries like AI where companies invest in moving digital mountains. Stuff like Microsoft's ONNX project will go a long way towards making CUDA unneccesary for AI acceleration, but it won't ever kill the demand for CUDA.
Just look at how lethargic the industry's response has been in the wake of AI, and look at how other companies like AMD and Apple abandoned OpenCL before it was ready. Now Apple is banking on CoreML as an integration feature and AMD is segmenting their consumer and server hardware like crazy.
> Weirdly, NVIDIA hardware only outperforms competitors on its own API. When you compare NVIDIA on a level playing field, they aren't the clear winners.
That does not reflect any of the benchmarks I've seen at all, unless by "level playing field" you mean comparing old Nvidia chips to modern AMD ones. The only systems comparable to the DGX pods Nvidia sells is Apple's hardware, which lacks the networking and OS support to be competitive server side.
AMD is an amazing company for being open and transparent with their approach, but nice guys always finish last. This is a race between the highest-density TSMC customers, which means it's Apple and Nvidia laughing their respective paths to the bank.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#23Earlier quoted context omitted.
Nvidia doesn't have a monopoly here. Google have their TPU, AMD have MI300, Intel have Data Centre GPU Max, and then there are start ups like Graphcore. There are many alternatives. I think people choose Nvidia because it's easier for them to use. That's because everyone else uses them, and that's because for years and years their competitors (other than Google perhaps) have dropped the ball on good software.
NVIDIA is the leader because most academic AI setups are NVIDIA based. When AI moves further away from academia NVIDIA will have less of a grip. Proprietary, hardware specific APIs never stand the test of time. Ask 3Dfx. Either CUDA will open up, if it is to survive or open API use will spread. Weirdly, NVIDIA hardware only outperforms competitors on its own API. When you compare NVIDIA on a level playing field, they…
How did NVIDIA entrench itself as the leader in academia? From a market perspective it's interesting how they cornered it.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#24“Employee demographics are “diverse,” sort of—I would guess, based on a visual survey of the cafeteria at lunchtime, that about a third of the staff is South Asian, a third is East Asian, and a third is white.” This part of the article bothered me. Why was diverse placed in quotation marks? Aren’t South Asians and East Asians considered minorities? Also South Asians are a very diverse group in it itself. As are East…
"Also South Asians are a very diverse group in it itself. As are East Asians. Many different languages and cultures…."
Europeans and their diaspora (aka White people) are also very diverse in the sense of many languages, many cultures, many phenotypes, varied histories, etc.
But we all know what capital-D "Diversity" means here, and it is not this.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#25Earlier quoted context omitted.
NVIDIA is the leader because most academic AI setups are NVIDIA based. When AI moves further away from academia NVIDIA will have less of a grip. Proprietary, hardware specific APIs never stand the test of time. Ask 3Dfx. Either CUDA will open up, if it is to survive or open API use will spread. Weirdly, NVIDIA hardware only outperforms competitors on its own API. When you compare NVIDIA on a level playing field, they…
How did NVIDIA entrench itself as the leader in academia? From a market perspective it's interesting how they cornered it.
CUDA runs on most recent Nvidia GPUs, which are replete on college campuses and well-supported in server software. AMD's GPGPU compute support differs from GPU to GPU, and Apple didn't start contributing acceleration patches to Pytorch and Tensorflow until stuff like Llama and Stable Diffusion took off.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#26> In May, hundreds of industry leaders endorsed a statement that equated the risk of runaway A.I. with that of nuclear war. Huang didn’t sign it. Some economists have observed that the Industrial Revolution led to a relative decline in the global population of horses, and have wondered if A.I. might do the same to humans. “Horses have limited career options,” Huang said. “For example, horses can’t type.” As he finish…
Not sure if you're implying that it's easy or hard to be a NYer writer
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#27Earlier quoted context omitted.
NVIDIA is the leader because most academic AI setups are NVIDIA based. When AI moves further away from academia NVIDIA will have less of a grip. Proprietary, hardware specific APIs never stand the test of time. Ask 3Dfx. Either CUDA will open up, if it is to survive or open API use will spread. Weirdly, NVIDIA hardware only outperforms competitors on its own API. When you compare NVIDIA on a level playing field, they…
> Either CUDA will open up, if it is to survive or open API use will spread. CUDA won't die if Open APIs take over AI inferencing operations. It's still used and applied in so many niche industries that it can only be "replaced" in industries like AI where companies invest in moving digital mountains. Stuff like Microsoft's ONNX project will go a long way towards making CUDA unneccesary for AI acceleration, but it wo…
Calling AMD a nice guy is a huge stretch in my opinion. From my understanding they didn't even allow use of ROCm with consumer GPUs until this year... CUDA is and always was very accessible to a broad audience.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#28Should have never sold NVDA stock when I bought them in 2007
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#29Earlier quoted context omitted.
I think the writer is insinuating that ethnicity is only one type of diversity. What if 95% of those Asian employees were male, for instance?
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As well as the overwhelming majority-male workforce, which is mentioned right below this in the article.
Re: How Jensen Huang's Nvidia is powering the A.I. revolution
#30Earlier quoted context omitted.
How did NVIDIA entrench itself as the leader in academia? From a market perspective it's interesting how they cornered it.
CUDA runs on most recent Nvidia GPUs, which are replete on college campuses and well-supported in server software. AMD's GPGPU compute support differs from GPU to GPU, and Apple didn't start contributing acceleration patches to Pytorch and Tensorflow until stuff like Llama and Stable Diffusion took off.
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