Semiconductor Engineering

Outlier-aware Quantization Framework Co-designed With Heterogeneous NVM For SLM Deployment on Edge Platforms (UCSD et al.)

Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design” was published by researchers at University of California San Diego and San Diego State University. Abstract ...
Forbes

How Mixed-Precision Quantization Could Break AI’s Power Addiction

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 ...
Semiconductor Engineering

Ultra-low-bit LLM Inference Allows AI-PC CPUs And Discrete Client GPUs To Approach High-end GPU-Level (Intel)

A new technical paper titled “Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs” was published by researcher at ...
Hackaday

Making The Smallest And Dumbest LLM With Extreme Quantization

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 ...
VentureBeat

How Microsoft's next-gen BitNet architecture is turbocharging LLM efficiency

One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, ...
VentureBeat

Here are 3 critical LLM compression strategies to supercharge AI performance

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In today’s fast-paced digital landscape, businesses relying on AI face ...