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 ...

Huawei, a major Chinese technology company, has announced Sinkhorn-Normalized Quantization (SINQ), a quantization technique that enables large-scale language models (LLMs) to run on consumer-grade ...

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 ...

Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.

Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor ...

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 ...

Researchers at Nvidia have developed a novel approach to train large language models (LLMs) in 4-bit quantized format while maintaining their stability and accuracy at the level of high-precision ...

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 ...