Since the advent of distributed computing, there has been a tension between the tight coherency of memory and its compute within a node – the base level of a unit of compute – and the looser coherency over the network across those nodes. …
Because they are in the front of the line for acquiring Nvidia datacenter GPUs, the hyperscalers and cloud builders are going to be the ones who benefit mightily from shortages of matrix math engines that can train AI models and run inference against them. …
We said it from the beginning: There is no way that Meta Platforms, the originator of the Open Compute Project, wanted to buy a complete supercomputer system from Nvidia in order to advance its AI research and move newer large language models and recommendation engines into production. …
It takes big money as well as big ideas to compete in the generative AI space. …
UPDATED: It is funny what courses were the most fun and most useful when we look back at college. …
If you want to get the attention of server makers and compute engine providers and especially if you are going to be building GPU-laden clusters with shiny new gear to drive AI training and possibly AI inference for large language models and recommendation engines, the first thing you need is $1 billion. …
If you were hoping to get your hands on one of Nvidia’s “Grace” Arm-based CPUs, then you better be prepared to buy a pretty big machine. …
The heady, exciting days of ChatGPT and other generative AI and large-language models (LLMs) is beginning to give way to the understanding that enterprises will need to get a tight grasp on how these models are being used in their operations or they will risk privacy, security, legal, and other problems down the road. …
If you are a Global 20,000 company and you want to build a large language model that is specifically tuned to your business, the first thing you need is a corpus of your own textual data on which to train that LLM. …
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