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I'm surprised they are not developing their own GPUs.
by andrewstuart 3y ago
I'm surprised they are not developing their own GPUs.
- grogenaut 3y agoThey are... https://aws.amazon.com/machine-learning/inferentia/ https://aws.amazon.com/machine-learning/inferentia/
- my123 3y agoThose aren't anywhere near the flexibility of a GPU. It's a more specialized accelerator tailored to machine learning workloads.
- w-ll 3y agothats the point. they dont want GPUs, they want AIPUs
- wmf 3y agoWhich is what people want.
- schneems 3y agoBrawndo, it's got what plants crave.
- rockwotj 3y agoGoogle already did this with TPUs and it supposedly saved them a ton in energy costs. I read somewhere that NVIDIA doesn’t gain as much by making the chips more cost effective, just making them more powerful
- mr_toad 3y ago> It's a more specialized accelerator tailored to machine learning workloads. Hard to imagine anyone using a cloud GPU for anything else.
- singhrac 3y agoand Trainium for training of course
- wenc 3y agoAnd compete with CUDA? It's not the hardware that's the lockin but CUDA.
- AgentOrange1234 3y agoHow much of the big expensive training jobs are CUDA specific? If it’s billions of dollars of compute, rewriting the software to use whatever hardware is cheapest may make sense?
- vineyardmike 3y agoIt takes time to re-engineer an entire ecosystem of tools. The whole 9-pregnant ladies in 1 month analogy comes to mind. If you’re trying to accomplish a goal, how long are you willing to wait for your entire dependency tree to be engineered in-house. It’s happening slowly, but teams have to ship, and can’t wait for other teams to build fresh tools. Additionally, the compute hardware is rented, and if there’s no alternatives available for rent it doesn’t matter. Data centers aren’t full of NVidia and AMD GPUs and TPUs (because the support isn’t there). It’s a crazy chicken/egg situation where everyone benefits but no one makes the move. It’s slowly happening, but it’s not there yet to totally replace them.
- delfinom 3y agoThat's the thing, they can be working on multiple paths in parallel. They can be building on nvidia and have a semiconductor team in another corner experimenting for alternate in the future. When profit margins are insane, there is always competition quietly fomenting. We just won't know until they release it because it's also a competitive advantage to keep your own plans underwrap until it's ready. Or else it may start an arms race that'll only drive up costs to get done faster.
- xbpx 3y agoThe CUDA lock-in is over played. Tensorflow, Pytorch and any large framework supports multiple hardware including Google TPUs. Any company making significant investment will steer some of that towards hardware support in the software they need.