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You need orders of magnitude more compute for training than for inference. Nvidia still wins in your scenario. Currently rendering and local GPGPU compute is N

You need orders of magnitude more compute for training than for inference. Nvidia still wins in your scenario. Currently rendering and local GPGPU compute is Nvidia dominated and I don’t see AMD competently going after the market segments.

demaga 2y ago
But you also run inference orders of magnitudes more times, so it should still amount to more compute than training?
- teaearlgraycold 2y ago
  That matters more to the electricity company than the silicon company. The profit margins on the datacenter training hardware are stupidly high compared to an AMD APU.
  - binary132 2y ago
    If there are tens of thousands of training GPUs but billions of APUs, then what? BTW, training is such a high cost that it seems like a major motive for the customer to reduce costs there.
    - k__ 2y ago
      This. Most will probably use something like Llama as base.
    - talldayo 2y ago
      > If there are tens of thousands of training GPUs but billions of APUs, then what? Believe it or not, we've actually been grappling with this scenario for almost a decade at this point. Originally the answer was to unite hardware manufacturers around a common featureset that could compete with (albeit not replace) CUDA. Khronos was prepared to elevate OpenCL to an industry standard, but Apple pulled their support for it and let the industry collapse into proprietary competition again. I bet they're kicking themselves over that one, if they still hold a stronger grudge against Nvidia than Khronos at least. So - logically, there's actually a one-size-fits-all solution for this problem. It was even going to get managed by the same people handling Vulkan. The problem was corporate greed and shortsighted investment that let OpenCL languish while CUDA was under active heavy development. > BTW, training is such a high cost that it seems like a major motive for the customer to reduce costs there. Eh, that's kinda like saying "app development is so expensive that consumers will eventually care". Consumers just buy the end product; they are never exposed to building the software or concerned with the cost of the development. This is especially true with businesses like OpenAI that just give you free access to a decent LLM (or Apple and their "it's free for now" mentality).
- marcosdumay 2y ago
  Besides, if you separate them, the people doing the training will put way more effort into optimizing their hardware ROI than the ones doing inference.
mistercow 2y ago
I think this is the big point of uncertainty in Nvidia’s future: will we find new training techniques which require significantly less compute, and/or are better suited to some fundamentally different architecture than GPUs? I’m reluctant to bet no on that long term, and “long term” for ML right now is not very long.
- brigadier132 2y ago
  If we find a new training technique that is that much more efficient why do you think we wont just increase the amount of training we do be n times? (or even more since it's now accessible to train custom models for smaller businesses)
  - mistercow 2y ago
    We might, but it’s also plausible that it would change the ecosystem so much that centralized models are no longer so prominent. For example, suppose that with much cheaper training, most training is on your specific data and behaviors so that you have a model (or ensemble of models) tailored to your own needs. You still need a foundation model, but those are much smaller so that they can run on device, so even with overparameterization and distillation, the training costs are orders of magnitude smaller. Or, in the small business case (mind you, “long term” for tech reaching small businesses is looooong), these businesses again need much smaller models because a) they don’t need a model well versed in Shakespeare and multi variable calculus, and b) they want inference to be as low cost as possible. These are just scenarios off the top of my head. The broader point is that a dramatic drop in training cost is a wildcard whose effects are really hard to predict.
- marcosdumay 2y ago
  I'd bet that any AI that is really useful for the tasks people want to push LLMs into will answer "yes" to both parts of your question. But I don't know what "long term" is exactly, and have no idea how to time this thing. Besides, I'd bet the sibling evoking the Jevon's paradox is correct.
- acchow 2y ago

demaga 2y ago

But you also run inference orders of magnitudes more times, so it should still amount to more compute than training?

teaearlgraycold 2y ago
That matters more to the electricity company than the silicon company. The profit margins on the datacenter training hardware are stupidly high compared to an AMD APU.
- binary132 2y ago
  If there are tens of thousands of training GPUs but billions of APUs, then what? BTW, training is such a high cost that it seems like a major motive for the customer to reduce costs there.
  - k__ 2y ago
    This. Most will probably use something like Llama as base.
  - talldayo 2y ago
    > If there are tens of thousands of training GPUs but billions of APUs, then what? Believe it or not, we've actually been grappling with this scenario for almost a decade at this point. Originally the answer was to unite hardware manufacturers around a common featureset that could compete with (albeit not replace) CUDA. Khronos was prepared to elevate OpenCL to an industry standard, but Apple pulled their support for it and let the industry collapse into proprietary competition again. I bet they're kicking themselves over that one, if they still hold a stronger grudge against Nvidia than Khronos at least. So - logically, there's actually a one-size-fits-all solution for this problem. It was even going to get managed by the same people handling Vulkan. The problem was corporate greed and shortsighted investment that let OpenCL languish while CUDA was under active heavy development. > BTW, training is such a high cost that it seems like a major motive for the customer to reduce costs there. Eh, that's kinda like saying "app development is so expensive that consumers will eventually care". Consumers just buy the end product; they are never exposed to building the software or concerned with the cost of the development. This is especially true with businesses like OpenAI that just give you free access to a decent LLM (or Apple and their "it's free for now" mentality).
marcosdumay 2y ago
Besides, if you separate them, the people doing the training will put way more effort into optimizing their hardware ROI than the ones doing inference.

mistercow 2y ago

I think this is the big point of uncertainty in Nvidia’s future: will we find new training techniques which require significantly less compute, and/or are better suited to some fundamentally different architecture than GPUs? I’m reluctant to bet no on that long term, and “long term” for ML right now is not very long.

brigadier132 2y ago
If we find a new training technique that is that much more efficient why do you think we wont just increase the amount of training we do be n times? (or even more since it's now accessible to train custom models for smaller businesses)
- mistercow 2y ago
  We might, but it’s also plausible that it would change the ecosystem so much that centralized models are no longer so prominent. For example, suppose that with much cheaper training, most training is on your specific data and behaviors so that you have a model (or ensemble of models) tailored to your own needs. You still need a foundation model, but those are much smaller so that they can run on device, so even with overparameterization and distillation, the training costs are orders of magnitude smaller. Or, in the small business case (mind you, “long term” for tech reaching small businesses is looooong), these businesses again need much smaller models because a) they don’t need a model well versed in Shakespeare and multi variable calculus, and b) they want inference to be as low cost as possible. These are just scenarios off the top of my head. The broader point is that a dramatic drop in training cost is a wildcard whose effects are really hard to predict.
marcosdumay 2y ago
I'd bet that any AI that is really useful for the tasks people want to push LLMs into will answer "yes" to both parts of your question. But I don't know what "long term" is exactly, and have no idea how to time this thing. Besides, I'd bet the sibling evoking the Jevon's paradox is correct.
acchow 2y ago