The AI model makers of the world have been waiting for more than a year to get their hands on the Trainium3 XPUs, which have been designed explicitly for both training and inference and which present a credible alternative to Nvidia’s “Blackwell” B200 and B300 GPUs as well as Google’s “Trillium” TPU v6e and “Ironwood” TPU v7p accelerators. …
One only need look at the incredible revenues and profits of the datacenter business at Nvidia to know that the world’s biggest compute customers – the hyperscalers, the cloud builders, and now the biggest model builders – need to bend the price/performance curve to boost their own profits. …
Marvell Technology made some big bets about delivering chip packaging and I/O technologies to the hyperscalers and cloud builders of the world who want to design their own ASICs but who do not have the expertise to get those designs across the finish line into products. …
There is a bit of AI spending one-upmanship going on among the hyperscalers and cloud builders – and now the foundation model builders who are partnering with their new sugar daddies to be able to afford to build vast AI accelerator estates to push the state of the art in model capabilities and intelligence. …
When the hyperscalers and cloud builders were smaller and the Arm collective had failed to storm the datacenter and AMD was not yet on its path to resurgence, it was Intel that controlled the cadence of new compute engine introductions into the datacenter. …
There was a time – and it doesn’t seem like that long ago – that the datacenter chip market was a big-money but relatively simple landscape, with CPUs from Intel and AMD and Arm looking to muscle its way in and GPUs mostly from Nvidia with some from AMD and Intel looking to muscle its way in. …
If Microsoft has the half of OpenAI that didn’t leave, then Amazon and its Amazon Web Services cloud division needs the half of OpenAI that did leave – meaning Anthropic. …
Amazon Web Services may not be the first of the hyperscalers and cloud builders to create its own custom compute engines, but it has been hot on the heels of Google, which started using its homegrown TPU accelerators for AI workloads in 2015. …
For the last two quarters at least, the generic infrastructure server market – the one running databases, application servers, various web layers, and print and file serving workloads the world over – has been in a recession. …
While AWS is making it clear it will continue to innovate around GPUs for cloud-based machine learning, there was little doubt after today that the company’s own Trainium devices are set to outdo what even the top-line Nvidia GPU instances have achieved. …
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