In many industries, embracing AI in the application software stack it is not just a matter of training some large language models or recommender systems against general and then specific datasets and plugging it in. …
Sponsored Post: We all know that inferencing and training AI models needs a lot of CPU muscle, but we don’t necessarily appreciate how important other components are in supporting AI and ML applications. …
It has been becoming increasingly clear – anecdotally at least – just how expensive it is to train large language models and recommender systems, which are arguably the two most important workloads driving AI into the enterprise. …
Changing the compute paradigm in the datacenter, or even extending it or augmenting it in some fashion, is no easy task. …
Sponsored Feature: It’s a decade and a half since researchers dazzled the tech world by demonstrating that GPUs could be used to dramatically accelerate key AI operations. …
Sponsored Feature: Humans have wanted to know what the weather is going to do since first we understood how the forces of nature determined our expectations of survival. …
While AI training dims the lights at hyperscalers and cloud builders and costs billions of dollars a year, in the long run, there will be a whole lot more aggregate processing done on AI inference than on AI training. …
Big Blue was one of the system designers that caught the accelerator bug early and declared rather emphatically that, over the long haul, all kinds of high performance computing would have some sort of acceleration. …
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. …
Beijing-based hyperscale giant, ByteDance, owners of TikTok among other popular applications, have found that general purpose, open source recommendation engines fall far short of what is required for on-point real-time suggestions. …
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