Changing AI math could reduce the hardware burden, researchers show

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...
XDA Developers on MSN

My 7-year-old GPU runs local AI perfectly, and I don't need my cloud subscriptions anymore

You don't always need an RTX 5090 to run useful models ...
manilatimes

Nota AI Has Two MoE Quantization Papers Accepted at ICML 2026 Workshop, Demonstrating Global Competitiveness in Large-Scale AI Optimization

Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026 Recognition follows Nota AI's overall win at the NVIDIA Nemotron Hackathon Strengthening ...
Yahoo Finance

Nota AI Has Two MoE Quantization Papers Accepted at ICML 2026 Workshop, Demonstrating Global Competitiveness in Large-Scale AI Optimization

SEOUL, South Korea, June 11, 2026 /PRNewswire/ -- Nota AI, a company specializing in AI model compression and optimization, announced that two of its papers on MoE-specific quantization algorithms ...
Android Authority

The latest Gemma 4 models use a training trick to slash their on-device memory footprint

Gemma 4 models are now available for download with quantization-aware training (QAT), which reduces the size and memory footprint of the models. These open-source models retain quality better thanks ...
Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...
VentureBeat

Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
Semiconductor Engineering

Balancing Training, Quantization, And Hardware Integration In NPUs

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 ...
IEEE

A Low-Latency, Highly-Pipelined Hardware Architecture for H.266/VVC Dependent Quantization

Abstract: In comparison to H.265/HEVC, H.266/VVC introduces a novel quantization tool—dependent quantization, which significantly reduces the rate while maintaining the same video quality. However, ...