Your GPU is an AI accelerator already. Running trained AI models is not as resource demanding as training one. Unless local training becomes universal, AI acclerators for consumers make very few sense.
this post was submitted on 27 Jun 2023
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The newest gen GPUs have sections dedicated to AI already, so we effectively already have dedicated AI accelerators.
Yes there are but the op is talking about discrete AI accelerators....
Unless the AI processing is much more specialized than graphics, I think manufacturers would put that effort into making more powerful GPUs that can also be used for AI tasks.
They would try to alleviate the cost on running GPU by making an AI accelerator chip like Tensor Core, but it'll get bottleneck by limited VRAM when Neural Net models require steep amount of memory. it's more productive to have something like NPU that runs either on RAM or by it's own memory chips offering higher amount of capacity to run such neural net and avoid the roundtrip data copying between GPU and CPU.
We saw this happen a long time ago with PPUs. Physics Processing Units. They came around for a couple of years, then the graphics cards manufacturers integrated the PPU into the GPU and destroyed any market for PPUs.
Look into what Mystic AI was doing. It's effectively what you were talking about but based in reality :)
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It was before my time but.. If physX cards are any indication, then no.
The PhysX debate was also before my time. But I read into it, and it seems like they solved it partly software based. Please correct me if I'm wrong, I just skimmed over the PPU subject. But with AI we are talking about hardware limitations, especially memory.
Currently, AI operations mean a lot of time-consuming copy tasks between CPU and GPU.
Absolutely, I would suggest looking into two separate devices that focuses solely on AI acceleration:
and
Two very interesting articles. Thank you for that!
Especially the analog processor is a game changer with having the computation directly in memory. Generally, analog computers are a very interesting subject!
Good question, but I'd say that the same train of thought went through dedicated physics cards. I'd guess that an AI card should have a great value proposition to be worth buying.
For compatibility, they could e.g. offer a cloud based subscription system.
I'm not sure where you're going with this, but it feels wrong. I'm not buying a hardware part that cannot function without a constant internet connection or regular payment.
I'd guess that an AI card should have a great value proposition to be worth buying.
Sure the card should have great value or must have an accessible price. It probably also depends on how "heavy" the tasks get. But seeing e.g. OpenAI struggling with requests, it may be useful to decentralize the processing (with running the model locally on the user's pc).
I'm not sure where you're going with this, but it feels wrong. I'm not buying a hardware part that cannot function without a constant internet connection or regular payment.
Maybe this statement was a bit confusing. What I meant was, that in a transition phase developers could choose to allow the usage of a dedicated accelerator card to run everything locally and offline. And for people who don't have or want such a card they could provide a cloud based subscription model, where the processing is done on remote servers.