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 ...
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 ...
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 ...
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 ...
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A new technical paper titled “Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs” was published by researcher at Intel. “The advent of ultra-low-bit LLM models (1/1.58/2-bit), which match ...
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