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 ...
Hosted on MSN

Maximizing self-hosted LLM performance with limited VRAM

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 ...
Elektor Magazine

TurboQuant Vector Quantization Cuts LLM Memory Use

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...
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 ...
Morningstar

Nota AI Reduces Memory Usage of Upstage's Solar LLM by 72%, Demonstrating Proprietary Quantization Technology

SEOUL, South Korea, March 5, 2026 /PRNewswire/ -- Nota AI, an AI optimization technology company behind the Nota AI brand, announced that it has developed a next-generation quantization technology ...
InfoWorld

What is model quantization? Smaller, faster LLMs

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 ...