4 ms·

Google does the same thing with their TPUs. The masses will be left with the NVidia monopoly, while large companies will be able to free themselves from that.

by ronsor 3y ago

wmf 3y ago
MI300 is coming.
- machinekob 3y ago
  This time AMD for sure will fight with NV (its only failed 20 times already copium)
  - wmf 3y ago
    On one hand this is a fair prediction but Triton exists now and it didn't exist last time.
    - pjmlp 3y ago
      If the software still sucks with what CUDA can do, in graphics tooling, polyglot support and optmized libraries, across at very least Windows and GNU/Linux, than whatever the card is capable of doing isn't much relevant.
- FuriouslyAdrift 3y ago
  December 6 launch date: https://ir.amd.com/news-events/press-releases/detail/1168/amd-announces-amd-instinct-mi300-accelerator-launch-event https://ir.amd.com/news-events/press-releases/detail/1168/am...
llm_nerd 3y ago
nvidia is a $1.2 trillion dollar company (the 6th largest company by cap), and at this point AI is a huge component of that wealth. It has appreciated by 3.3x since just the beginning of this year. If any of these companies truly made competitive silicon they absolutely would commercialize it. I suspect they aren't as competitive as the press releases hold them to be, and this Microsoft entrant is likely to follow the same path. Like Google, Tesla, Amazon and others it seems mostly an initiative to negotiate discounts from nvidia. It would be great if there were really competition. When Google was hyped about their Tensor chips they did have a period where they were looking to commercialize it, and there are some pretty crappy USB products they sell.
- jsnell 3y ago
  They are commercializing the silicon, by selling access to it on their clouds. Now, I know that what you actually mean is selling the chips themselves to third parties :) But it's not obvious that there's any point to it given their already existing model of commercializing the chips. First, literally everyone is already supply-constrained due to limits on high end foundry capacity. Nvidia has a ton of capacity because they're one of TSMC's top two customers. The big tech companies will have much smaller allocations which are used up just supplying their own clouds. Even if the demand for buying these chips rather than renting were there, they just don't have the chips to sell without losing out on the customers who want to rent capacity. Second, the chips by themselves are probably not all that useful. A lot of the benefit is coming from the silicon/system/software co-design. (E.g. the TPUv4 papers spent as much attention on the optical interconnect as the chips). Selling just chips or accelerator cards wouldn't do much good to any customers. Nor can they just trust that systems integrators could buy the cards and build good systems to house them in. They need to sell and support massive large scale custom systems to third parties. That's not a core competency for any of them, it'll take years to build up that org if you start now. And it means they need to ship the software to the customers, it can't continue being the secret sauce any more. Nvidia on the other hand has been building up an ecosystem and organization for exactly this for the last decade.
  - scarface_74 3y ago
    > Nvidia has a ton of capacity because they're one of TSMC's top two customers. The big tech companies will have much smaller allocations which are used up just supplying their own clouds. And TSMCs top customer is not even playing in the cloud space.
aleph_minus_one 3y ago
> The masses will be left with the NVidia monopoly, while large companies will be able to free themselves from that. My bet: if it really becomes clear what capabilities an AI accelerator chip needs and lots of people want to run (or even train) AIs on their own computers, AI accelerators will appear at the market. This is how capitalism typically works. My further bet: these AI accelerators will initially come from China. Just look at the history of Bitcoin: initially the blocks were mined on CPUs, but then the miners switched to GPUs and "everybody" was complaining about increasing GPU prices because of all the Bitcoin mining. At some moment, Bitcoin mining ASICs appeared from China and after those spread, GPUs were not attractive anymore for Bitcoin mining (of course the cryptocurrency fans who bought the GPUs for mining attempted to use their investment for mining other cryptocurrencies).
- brucethemoose2 3y ago
  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.
    - 3y ago

Google does the same thing with their TPUs. The masses will be left with the NVidia monopoly, while large companies will be able to free themselves from that.

by ronsor 3y ago

Google does the same thing with their TPUs. The masses will be left with the NVidia monopoly, while large companies will be able to free themselves from that.

wmf 3y ago

MI300 is coming.

machinekob 3y ago
This time AMD for sure will fight with NV (its only failed 20 times already copium)
- wmf 3y ago
  On one hand this is a fair prediction but Triton exists now and it didn't exist last time.
  - pjmlp 3y ago
    If the software still sucks with what CUDA can do, in graphics tooling, polyglot support and optmized libraries, across at very least Windows and GNU/Linux, than whatever the card is capable of doing isn't much relevant.
FuriouslyAdrift 3y ago
December 6 launch date: https://ir.amd.com/news-events/press-releases/detail/1168/amd-announces-amd-instinct-mi300-accelerator-launch-event https://ir.amd.com/news-events/press-releases/detail/1168/am...

llm_nerd 3y ago

nvidia is a $1.2 trillion dollar company (the 6th largest company by cap), and at this point AI is a huge component of that wealth. It has appreciated by 3.3x since just the beginning of this year. If any of these companies truly made competitive silicon they absolutely would commercialize it. I suspect they aren't as competitive as the press releases hold them to be, and this Microsoft entrant is likely to follow the same path. Like Google, Tesla, Amazon and others it seems mostly an initiative to negotiate discounts from nvidia. It would be great if there were really competition. When Google was hyped about their Tensor chips they did have a period where they were looking to commercialize it, and there are some pretty crappy USB products they sell.

jsnell 3y ago
They are commercializing the silicon, by selling access to it on their clouds. Now, I know that what you actually mean is selling the chips themselves to third parties :) But it's not obvious that there's any point to it given their already existing model of commercializing the chips. First, literally everyone is already supply-constrained due to limits on high end foundry capacity. Nvidia has a ton of capacity because they're one of TSMC's top two customers. The big tech companies will have much smaller allocations which are used up just supplying their own clouds. Even if the demand for buying these chips rather than renting were there, they just don't have the chips to sell without losing out on the customers who want to rent capacity. Second, the chips by themselves are probably not all that useful. A lot of the benefit is coming from the silicon/system/software co-design. (E.g. the TPUv4 papers spent as much attention on the optical interconnect as the chips). Selling just chips or accelerator cards wouldn't do much good to any customers. Nor can they just trust that systems integrators could buy the cards and build good systems to house them in. They need to sell and support massive large scale custom systems to third parties. That's not a core competency for any of them, it'll take years to build up that org if you start now. And it means they need to ship the software to the customers, it can't continue being the secret sauce any more. Nvidia on the other hand has been building up an ecosystem and organization for exactly this for the last decade.
- scarface_74 3y ago
  > Nvidia has a ton of capacity because they're one of TSMC's top two customers. The big tech companies will have much smaller allocations which are used up just supplying their own clouds. And TSMCs top customer is not even playing in the cloud space.

aleph_minus_one 3y ago

> The masses will be left with the NVidia monopoly, while large companies will be able to free themselves from that. My bet: if it really becomes clear what capabilities an AI accelerator chip needs and lots of people want to run (or even train) AIs on their own computers, AI accelerators will appear at the market. This is how capitalism typically works. My further bet: these AI accelerators will initially come from China. Just look at the history of Bitcoin: initially the blocks were mined on CPUs, but then the miners switched to GPUs and "everybody" was complaining about increasing GPU prices because of all the Bitcoin mining. At some moment, Bitcoin mining ASICs appeared from China and after those spread, GPUs were not attractive anymore for Bitcoin mining (of course the cryptocurrency fans who bought the GPUs for mining attempted to use their investment for mining other cryptocurrencies).

brucethemoose2 3y ago
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.
  - 3y ago