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Nvidia's undervalued. Once enterprise adoption of AI picks up, demand for chips will increase 2-3 times further. I'm told Nvidia's building their own fab in S

Nvidia's undervalued. Once enterprise adoption of AI picks up, demand for chips will increase 2-3 times further. I'm told Nvidia's building their own fab in Southeast Asia over the next few years. This will massively boost their output.

kccqzy 3y ago
It remains debatable whether mass enterprise adoption of AI would happen first, or Nvidia's competitors coming up with equivalent chips would happen first.
- danielmarkbruce 3y ago
  On the surface, it's not debatable. Enterprises are going full steam ahead on AI. Building out an ecosystem to challenge Nvidia seems like a decade long battle, if it's even possible.
  - haldujai 3y ago
    What is full steam ahead for enterprises? It's not like they're throwing autoregressive LLMs into production any time soon. In any case Nvidia is expecting to ship ~550k H100s in 2023, hardly enough to satisfy every user. Tesla decided to in-house. TPUv4 and Gaudi2 exceeded A100 performance, they just never hit scale or the market and then Hopper added optimization for transformers rendering these chips relatively obsolete. Nvidia's lead is not unassailable and it seems incredibly unlikely that they would not face serious competition within the next 2-3 years given the $ being thrown around.
    - danielmarkbruce 3y ago
      Large enterprises are already putting them into production. I have direct experience with it. It's not unassailable. But it's going to take a lot to make any difference to Nvidia's volume or pricing, let alone a meaningful difference. They already face serious competitors in google and aws with TPU and inferentia, but those competitors are at a pretty big disadvantage for now (and others too). The cuda ecosystem is a big advantage. Nvidia has a lot of leverage with semi manufacturers because of volume. They spend way more on chip R&D than their competitors in the space. They have brand recognition. You can buy and own Nvidia chips v tpu and inferentia. It's... a tough road ahead for competitors.
      - haldujai 3y ago
        > Large enterprises are already putting them into production. I have direct experience with it. For what function have you experienced LLMs being used at significant scale in production right now? It seems unlikely most enterprises have built up sufficient technical know-how to run these workloads in-house already. > They already face serious competitors in google and aws with TPU and inferentia TPUv4s aren't widely available and are not competitive with Hopper. Inferentia isn't for training and is also not competitive on throughput. > The cuda ecosystem is a big advantage. Is it really though? A year ago it wasn't the case (see TPUs). Nvidia hasn't maintained a stranglehold on the software stack. > Nvidia has a lot of leverage with semi manufacturers because of volume. With TSMC*. If we believe Pat Gelsinger, Intel is starting to catch-up and their roadmap would place them ahead of TSMC in H2 2024. > They have brand recognition. You can buy and own Nvidia chips v tpu and inferentia. Intel, AMD and Google don't have brand recognition? There isn't a competitive alternative today for sale or rent, that doesn't mean this can't/won't change. It also doesn't mean they won't become available for enterprise purchase when we're talking about 60B/year in revenue. > It's... a tough road ahead for competitors. While hard I disagree that their moat is as wide as being touted. At the end of the day, whoever gets the most FLOPS, can deliver product faster and/or is cheaper will win as long as it's trivial to migrate workloads. I really don't believe anyone actually cares who the manufacturer is.
        danielmarkbruce 3y ago
        Customer support. I've never seen so many people get up to speed so fast on something. It's unlike anything ever. Yep. I believe so. I don't believe Pat. With the relevant audience, Nvidia is the brand which is the strongest. Google could just drop support for TPU, AMD isn't viewed as currently credible for people doing the work, nor Intel. You may be right, only time will tell. I've been involved with GPU use for general purpose workloads since Cuda was launched. At every step of the way there was apparently credible competition at different layers of the stack just around the corner. That's over 15 years. OpenCL, ASICs, FPGA, Intel this that and the other, AMD this that and the other. TPU. Others I've forgotten.
        3y ago
- haldujai 3y ago
  It's hard to imagine Nvidia will maintain what is right now effectively 100% market share for training forever, especially given the $ being thrown around.
- steno132 3y ago
  There’s no competitor to Nvidia for the next 10 years. They’ve got a monopoly. And with AI’s coming explosion, I’d wager 50/50 odds Jensen becomes the world’s first trillionaire.
  - enos_feedler 3y ago
    this is a totally insane statement to make. 10 years? it will take at _most_ 2-3 years for the software and hardware ecosystem to rearrange around a competitive landscape. The only reason nvidia has a large time window of opportunity is because of the lead time required to create such a complex ecosystem and the "suddenness" of how meaningful particular category of workloads are (which nvidia happened to be good at, because... pixels)
johnvanommen 3y ago
> Once enterprise adoption of AI picks up, demand for chips will increase 2-3 times further. Possibly their greatest asset, as an investment, is their crazy high margins. Nvidia in 2023 is where Intel was in 2007, where they could basically charge almost any price because they were so dominant in the market. I remember when E5s were selling for $2000 a pop and data centers were using thousands of them.
qwytw 3y ago
> will increase 2-3 times further. That and possibly way more than that is already priced in. Nvidia's stock is extremely expensive not because of they are making now (which is not a lot relative to valuation, they just barely surpassed Intel this quarter in revenue) but because investors expect pretty much exponential growth over the next few years..

kccqzy 3y ago

It remains debatable whether mass enterprise adoption of AI would happen first, or Nvidia's competitors coming up with equivalent chips would happen first.

danielmarkbruce 3y ago
On the surface, it's not debatable. Enterprises are going full steam ahead on AI. Building out an ecosystem to challenge Nvidia seems like a decade long battle, if it's even possible.
- haldujai 3y ago
  What is full steam ahead for enterprises? It's not like they're throwing autoregressive LLMs into production any time soon. In any case Nvidia is expecting to ship ~550k H100s in 2023, hardly enough to satisfy every user. Tesla decided to in-house. TPUv4 and Gaudi2 exceeded A100 performance, they just never hit scale or the market and then Hopper added optimization for transformers rendering these chips relatively obsolete. Nvidia's lead is not unassailable and it seems incredibly unlikely that they would not face serious competition within the next 2-3 years given the $ being thrown around.
  - danielmarkbruce 3y ago
    Large enterprises are already putting them into production. I have direct experience with it. It's not unassailable. But it's going to take a lot to make any difference to Nvidia's volume or pricing, let alone a meaningful difference. They already face serious competitors in google and aws with TPU and inferentia, but those competitors are at a pretty big disadvantage for now (and others too). The cuda ecosystem is a big advantage. Nvidia has a lot of leverage with semi manufacturers because of volume. They spend way more on chip R&D than their competitors in the space. They have brand recognition. You can buy and own Nvidia chips v tpu and inferentia. It's... a tough road ahead for competitors.
    - haldujai 3y ago
      > Large enterprises are already putting them into production. I have direct experience with it. For what function have you experienced LLMs being used at significant scale in production right now? It seems unlikely most enterprises have built up sufficient technical know-how to run these workloads in-house already. > They already face serious competitors in google and aws with TPU and inferentia TPUv4s aren't widely available and are not competitive with Hopper. Inferentia isn't for training and is also not competitive on throughput. > The cuda ecosystem is a big advantage. Is it really though? A year ago it wasn't the case (see TPUs). Nvidia hasn't maintained a stranglehold on the software stack. > Nvidia has a lot of leverage with semi manufacturers because of volume. With TSMC*. If we believe Pat Gelsinger, Intel is starting to catch-up and their roadmap would place them ahead of TSMC in H2 2024. > They have brand recognition. You can buy and own Nvidia chips v tpu and inferentia. Intel, AMD and Google don't have brand recognition? There isn't a competitive alternative today for sale or rent, that doesn't mean this can't/won't change. It also doesn't mean they won't become available for enterprise purchase when we're talking about 60B/year in revenue. > It's... a tough road ahead for competitors. While hard I disagree that their moat is as wide as being touted. At the end of the day, whoever gets the most FLOPS, can deliver product faster and/or is cheaper will win as long as it's trivial to migrate workloads. I really don't believe anyone actually cares who the manufacturer is.
      - danielmarkbruce 3y ago
        Customer support. I've never seen so many people get up to speed so fast on something. It's unlike anything ever. Yep. I believe so. I don't believe Pat. With the relevant audience, Nvidia is the brand which is the strongest. Google could just drop support for TPU, AMD isn't viewed as currently credible for people doing the work, nor Intel. You may be right, only time will tell. I've been involved with GPU use for general purpose workloads since Cuda was launched. At every step of the way there was apparently credible competition at different layers of the stack just around the corner. That's over 15 years. OpenCL, ASICs, FPGA, Intel this that and the other, AMD this that and the other. TPU. Others I've forgotten.
        3y ago
haldujai 3y ago
It's hard to imagine Nvidia will maintain what is right now effectively 100% market share for training forever, especially given the $ being thrown around.
steno132 3y ago
There’s no competitor to Nvidia for the next 10 years. They’ve got a monopoly. And with AI’s coming explosion, I’d wager 50/50 odds Jensen becomes the world’s first trillionaire.
- enos_feedler 3y ago
  this is a totally insane statement to make. 10 years? it will take at _most_ 2-3 years for the software and hardware ecosystem to rearrange around a competitive landscape. The only reason nvidia has a large time window of opportunity is because of the lead time required to create such a complex ecosystem and the "suddenness" of how meaningful particular category of workloads are (which nvidia happened to be good at, because... pixels)

johnvanommen 3y ago

> Once enterprise adoption of AI picks up, demand for chips will increase 2-3 times further. Possibly their greatest asset, as an investment, is their crazy high margins. Nvidia in 2023 is where Intel was in 2007, where they could basically charge almost any price because they were so dominant in the market. I remember when E5s were selling for $2000 a pop and data centers were using thousands of them.

qwytw 3y ago

> will increase 2-3 times further. That and possibly way more than that is already priced in. Nvidia's stock is extremely expensive not because of they are making now (which is not a lot relative to valuation, they just barely surpassed Intel this quarter in revenue) but because investors expect pretty much exponential growth over the next few years..