Harman announced this week that it has developed a private large language model trained on a variety of healthcare data sets. The Samsung Electronics subsidiary's advanced deployment techniques using ...

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...

Microsoft’s latest Phi4 LLM has 14 billion parameters that require about 11 GB of storage. Can you run it on a Raspberry Pi? Get serious. However, the Phi4-mini ...

Large language models (LLMs) are increasingly everywhere. Copilot, ChatGPT, and others are now so ubiquitous that you almost can’t use a website without being exposed to some form of "artificial ...