▲ | ants_everywhere 4 days ago | |||||||
it seems feasible, it's more a matter of how much of a priority it is. I follow Google most closely. They design and manufacture their own accelerators. AWS I know manufactures its own CPUs, but I don't know if they're working on or already have an AI accelerator. Several of the big players are working on OpenXLA, which is designed to abstract and commoditize the GPU layer: https://openxla.org/xla OpenXLA mentions: > Alibaba, Amazon Web Services, AMD, Apple, Arm, Google, Intel, Meta, and NVIDIA | ||||||||
▲ | mdaniel 4 days ago | parent [-] | |||||||
> AWS I know manufactures its own CPUs, but I don't know if they're working on or already have an AI accelerator I believe those are the Inferentia: https://aws.amazon.com/ai/machine-learning/inferentia/ > AWS Inferentia chips are designed by AWS to deliver high performance at the lowest cost in Amazon EC2 for your deep learning (DL) and generative AI inference applications but I don't know this second if they're supported by the major frameworks, or what I also didn't recall about https://aws.amazon.com/ai/machine-learning/trainium/ until I was looking up that page, so it seems they're trying to have a competitor to the TPUs just naming them dumb, because AWS > AWS Trainium chips are a family of AI chips purpose built by AWS for AI training and inference to deliver high performance while reducing costs. | ||||||||
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