If Nvidia and AMD are licking their lips thinking about all of the GPUs they can sell to the hyperscalers and cloud builders to support their huge aspirations in generative AI – particularly when it comes to the OpenAI GPT large language model that is the centerpiece of all of the company’s future software and services – they had better think again. …
A year ago, at its Google I/O 2022 event, Google revealed to the world that it had eight pods of TPUv4 accelerators, with a combined 32,768 of its fourth generation, homegrown matrix math accelerators, running in a machine learning hub located in its Mayes County, Oklahoma datacenter. …
It seems like we have been talking about Google’s TPUv4 machine learning accelerators for a long time, and that is because we have been. …
In an ideal platform cloud, you would not know or care what the underlying hardware was and how it was composed to run your HPC – and now AI – applications. …
The inception of Google’s effort to build its own AI chips is quite well known by now but in the interests of review, we’ll note that as early 2013 the company envisioned machine learning could consume the majority of its compute time. …
Carey Kloss has been intimately involved with the rise of AI hardware over the last several years, most notably with his work building the first Nervana compute engine, which Intel captured and is rolling into two separate products: one chip for training, another for inference. …
Training deep neural networks is one of the more computationally intensive applications running in datacenters today. …
This week we have heard much about the inference side of the deep learning workload, with a range of startups emerging at the AI Hardware Summit. …
If there is anything the hyperscalers have taught us, it is the value of homogeneity and scale in an enterprise. …
Google did its best to impress this week at its annual IO conference. …
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