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The capital costs are enormous, not even counting the CUDA moat. It takes years to start producing a big AI processor. Yet many startups and existing designers

The capital costs are enormous, not even counting the CUDA moat. It takes years to start producing a big AI processor. Yet many startups and existing designers anticipated this demand correctly, years in advance, and they are all still kinda struggling. Nvidia is massively supply constrained. AI customers would be buying up MI250s, CS-2s, IPUs, Tenstorrent accelerators, Gaudi 2s and so on en masse if they wanted to... But they are not, and its not going to get any easier once the supply catches up. Unless there's a big one in stealth mode, I think we are stuck with the hardware companies we have.

solardev 3y ago
Is there not a distributed computing potential here like there was for crypto mining? Some sort of seti@home/boinc like setup where home users can donate or sell compute time?
- latchkey 3y ago
  The capex/opex is different for ML/AI than it was for crypto mining. Totally different hardware profiles.
- fragmede 3y ago
  you can setup a computer and sell time on it on a couple of saas platforms, but only for inference. for training, the slowness of the interconnect between nodes become a bottleneck.
  - solardev 3y ago
    I see, thanks for the explanation!
- brucethemoose2 3y ago
  Yes, see projects like the AI Horde and Petals. I highly recommend the Horde in particular. Theres also some kind of actual AI crypto project that I wouldn't touch with a 10 foot pole. But ultimately, even if true distribution like Petals figures out the inefficiency (and thats hard), it had the same issue as non Nvidia hardware: its not turnkey.
aleph_minus_one 3y ago
> Yet many startups and existing designers anticipated this demand correctly, years in advance, and they are all still kinda struggling. As I already hinted in my post: I see a huge problem in the fact that in my opinion it still is not completely clear to this day which capabilities an AI accelerator really needs - too much is in my opinion still in a state of flux.
- brucethemoose2 3y ago
  The answer is kinda "whatever Nvidia implements." Research papers literally build around their hardware capabilities. A good example of this is Intel canceling, and AMD sidelining, their unified memory CPU/GPU chips for AI. They are super useful!.. In theory. But actually, they totally useless because no one is programming frameworks with unified memory SoCs in mind, as Nvidia does not make something like that.
jasonwatkinspdx 3y ago
There's also potential IP issues.
abstractcontrol 3y ago
> Yet many startups and existing designers anticipated this demand correctly, years in advance, and they are all still kinda struggling. Nvidia is massively supply constrained. AI customers would be buying up MI250s, CS-2s, IPUs, Tenstorrent accelerators, Gaudi 2s and so on en masse if they wanted to... But they are not, and its not going to get any easier once the supply catches up. Can you order any of these devices online as a regular person? Anybody can order a $300 Nvidia GPU and program it. This is the reason why deep learning originated on the GPUs. Forget those other AI accelerators, even if you bought something like a consumer grade AMD GPU, you couldn't program it because it's restricted. The reason why Nvidia's competitors are struggling is because their hardware is either too expensive or hard to buy.

solardev 3y ago

Is there not a distributed computing potential here like there was for crypto mining? Some sort of seti@home/boinc like setup where home users can donate or sell compute time?

latchkey 3y ago
The capex/opex is different for ML/AI than it was for crypto mining. Totally different hardware profiles.
fragmede 3y ago
you can setup a computer and sell time on it on a couple of saas platforms, but only for inference. for training, the slowness of the interconnect between nodes become a bottleneck.
- solardev 3y ago
  I see, thanks for the explanation!
brucethemoose2 3y ago
Yes, see projects like the AI Horde and Petals. I highly recommend the Horde in particular. Theres also some kind of actual AI crypto project that I wouldn't touch with a 10 foot pole. But ultimately, even if true distribution like Petals figures out the inefficiency (and thats hard), it had the same issue as non Nvidia hardware: its not turnkey.

aleph_minus_one 3y ago

> Yet many startups and existing designers anticipated this demand correctly, years in advance, and they are all still kinda struggling. As I already hinted in my post: I see a huge problem in the fact that in my opinion it still is not completely clear to this day which capabilities an AI accelerator really needs - too much is in my opinion still in a state of flux.

brucethemoose2 3y ago
The answer is kinda "whatever Nvidia implements." Research papers literally build around their hardware capabilities. A good example of this is Intel canceling, and AMD sidelining, their unified memory CPU/GPU chips for AI. They are super useful!.. In theory. But actually, they totally useless because no one is programming frameworks with unified memory SoCs in mind, as Nvidia does not make something like that.

jasonwatkinspdx 3y ago

There's also potential IP issues.

abstractcontrol 3y ago

> Yet many startups and existing designers anticipated this demand correctly, years in advance, and they are all still kinda struggling. Nvidia is massively supply constrained. AI customers would be buying up MI250s, CS-2s, IPUs, Tenstorrent accelerators, Gaudi 2s and so on en masse if they wanted to... But they are not, and its not going to get any easier once the supply catches up. Can you order any of these devices online as a regular person? Anybody can order a $300 Nvidia GPU and program it. This is the reason why deep learning originated on the GPUs. Forget those other AI accelerators, even if you bought something like a consumer grade AMD GPU, you couldn't program it because it's restricted. The reason why Nvidia's competitors are struggling is because their hardware is either too expensive or hard to buy.